System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting battery installation request information from a home; means for collectively ordering batteries; means for securing an integrated installation location of batteries; means for providing installation progress information; means for collecting power generation amount and consumption amount data; means for predicting power generation amount and consumption amount based on the collected data; means for instructing optimal power utilization; means for collecting power demand and supply data; means for calculating a selling price; means for providing calculated price information; means for collecting and analyzing power usage data; and means for providing analysis results.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] ---

[0005] The purpose of this project is to address the issue of handling surplus electricity generated by residential solar power generation systems after the end of the Feed-in Tariff (FIT) scheme. Specifically, the current situation, in which efficient energy use is hindered due to the low purchase price and the heavy financial burden of installing storage batteries for individual households, needs to be improved. It is also necessary to promote preparations for power shortages and the creation of cities that are resilient to disasters. Furthermore, the project aims to promote the local production and consumption of electricity within the region, achieving the Sustainable Development Goals (SDGs) and carbon neutrality. [Means for solving the problem]

[0006] To solve the above problems, we propose a system that includes the following means. This system achieves the effective introduction and management of storage batteries by collecting information on storage battery installation requests from households, ordering storage batteries in bulk, securing centralized storage battery installation locations, and providing installation progress information. It also promotes efficient power use by collecting data on power generation and consumption, predicting power generation and consumption based on the collected data, and providing instructions for optimal power usage. Furthermore, it applies dynamic pricing and optimizes local power trading by collecting data on power supply and demand, calculating buying and selling prices, and providing calculated price information. Finally, it clarifies energy consumption patterns and supports efficient power usage by collecting and analyzing power usage data and providing the analysis results.

[0007] ---

[0008] A "storage battery" is a device for storing electricity, for home or community use.

[0009] "Installation preference information" refers to information indicating a household's intention to install a storage battery.

[0010] "Bulk ordering" refers to a purchasing method in which orders from multiple households are aggregated and placed together.

[0011] "Concentrated installation site" refers to a location set aside for the centralized installation of storage batteries throughout an area.

[0012] "Installation progress information" refers to information indicating the progress of the storage battery installation work.

[0013] "Power generation" refers to the amount of power generated by a solar power generation system.

[0014] "Electricity consumption" refers to the amount of electricity consumed in a household or area.

[0015] "Data collection means" refers to a device or system for acquiring data on electricity generation and consumption.

[0016] The "power generation amount prediction means" refers to a function that predicts future power generation amount based on collected data.

[0017] "Consumption prediction means" refers to a function that predicts future electricity consumption based on collected data.

[0018] "Power usage instruction means" refers to a function for instructing a household on the optimal method of power usage.

[0019] "Electricity demand data" refers to data showing the electricity demand situation within a region.

[0020] "Electricity Supply Data" refers to data indicating the electricity supply status generated within a region.

[0021] "Buying and selling price calculation means" refers to a function that calculates the buying and selling price of electricity based on supply and demand data.

[0022] "Price information providing means" refers to a function for providing calculated purchase and sale price information to households.

[0023] "Electricity usage data" refers to detailed data on the electricity consumed by each household.

[0024] "Data analysis means" refers to a function for analyzing collected power usage data.

[0025] "Means for providing analysis results" refers to the function for providing the results of data analysis to households. [Brief explanation of the drawings]

[0026] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2]1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0027] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0028] First, the terms used in the following description will be explained.

[0029] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0030] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0031] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0032] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0033] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0034] [First embodiment]

[0035] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0036] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0037] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0038] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0039] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0040] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0041] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0042] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0043] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0044] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0045] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0046] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0047] ---

[0048] This invention is a system that promotes the self-consumption of surplus electricity generated by residential solar power generation systems for which the feed-in tariff (FIT) system has expired, and promotes preparation for power shortages and disaster-resistant urban development. This system includes a means for collecting information on battery installation requests from households and maximizing cost benefits through bulk orders. It also utilizes AI to optimize the flow of electricity and achieve efficient power consumption within the community.

[0049] 1. Bulk purchase and centralized installation of storage batteries

[0050] The server collects battery installation request information from each household. This information is then stored in a database, along with the number of people wanting to install a battery and the desired installation time. The server then selects the most suitable supplier based on the aggregated data and places a bulk order. This is more cost-effective than purchasing batteries individually.

[0051] For example, if 100 households in a region request the installation of storage batteries, the server will compile this information and place a bulk order. The obtained storage batteries will be installed at a centralized installation location in cooperation with the local government. Users can check the installation progress information in real time using their smartphones or PCs.

[0052] 2. Optimizing power flow using AI

[0053] The server collects data on power generation and consumption from each home's solar power generation system and power consumption devices. Based on this data, the server uses an AI algorithm to predict the next day's power generation and consumption. Based on the prediction results, the server creates an optimal power storage plan and sends appropriate power usage instructions to each household.

[0054] For example, if the weather forecast for the next day is sunny, the server will predict high power generation and instruct the system to prioritize storing surplus power. Conversely, during times of high consumption, the system will instruct the system to supply power from the storage battery, achieving efficient power usage. Users can check the AI's instructions via their device and manually adjust the settings as needed.

[0055] 3. Dynamic pricing of electricity using AI

[0056] The server collects local electricity demand and supply data in real time and calculates the buying and selling price using an AI algorithm. Based on this calculation, the device notifies the user of dynamic price information. This allows users to buy and sell electricity at a more advantageous price than under a contract with the power company.

[0057] For example, during times when demand for electricity in a region is high and supply is insufficient, the server will send notifications to terminals encouraging users to sell electricity at a higher price. Conversely, during times of excess supply, the server will offer lower prices to encourage users to buy electricity. Users can make buying and selling decisions based on this price information.

[0058] 4. AI-based data collection and analysis

[0059] The server collects real-time electricity usage data from each household and analyzes it using AI algorithms. Based on the results, recommendations for energy consumption patterns and energy conservation are generated and provided to users via their devices.

[0060] For example, if a household shows high power consumption during a certain time period, the server will detect this pattern and notify the device of specific actions to save energy (such as reducing power use at night). Users can then check their own energy consumption patterns and adjust their settings to achieve further energy savings.

[0061] In this way, the system of the present invention utilizes AI at each stage to achieve efficient power management and consumption, improving energy efficiency at the household level while also realizing sustainable energy management across the entire region.

[0062] The processing flow will be explained below.

[0063] ---

[0064] 1. Bulk purchase and centralized installation of storage batteries

[0065] Step 1:

[0066] The user inputs the desired battery installation information into the terminal, which then sends the information to the server.

[0067] Step 2:

[0068] The server stores and aggregates battery installation request information in a database.

[0069] Step 3:

[0070] The server selects the most suitable supplier based on the aggregated data and places a bulk order.

[0071] Step 4:

[0072] The server will work with local governments to secure a centralized installation location.

[0073] Step 5:

[0074] The server updates installation progress information in real time and notifies the user via the terminal.

[0075] ---

[0076] 2. Optimizing power flow using AI

[0077] Step 1:

[0078] The device measures the amount of electricity generated and consumed within the home in real time and sends the data to a server.

[0079] Step 2:

[0080] The server uses the collected data to run an AI algorithm and predict the next day's power generation and consumption.

[0081] Step 3:

[0082] Based on the prediction results, the server generates an optimal energy storage plan and instructions for power usage for each household.

[0083] Step 4:

[0084] The server generates instructions and distributes them to each household's terminal.

[0085] Step 5:

[0086] The user checks the instructions received through the device and manually adjusts the settings if necessary.

[0087] ---

[0088] 3. Dynamic pricing of electricity using AI

[0089] Step 1:

[0090] The server collects real-time data on electricity demand and supply within the region.

[0091] Step 2:

[0092] The server uses an AI algorithm to analyze the collected data and calculate the optimal buying and selling price.

[0093] Step 3:

[0094] The server sends the calculated purchase and sale price information to each household's terminal.

[0095] Step 4:

[0096] The device sends push notifications to the user, providing buying and selling price information.

[0097] Step 5:

[0098] The user checks the buying and selling price information on the terminal and decides whether to buy or sell electricity.

[0099] ---

[0100] 4. AI-based data collection and analysis

[0101] Step 1:

[0102] The terminal collects electricity usage data for each household in real time and sends it to a server.

[0103] Step 2:

[0104] The server stores the collected data in a database.

[0105] Step 3:

[0106] The server uses AI algorithms to analyze the data and identify energy consumption patterns.

[0107] Step 4:

[0108] The server generates energy-saving actions based on the analysis results and sends them to the terminal.

[0109] Step 5:

[0110] The device notifies the user of the analysis results and energy-saving actions.

[0111] Step 6:

[0112] The user checks the analysis results on the device and adjusts settings to save energy.

[0113] ---

[0114] The above are the specific steps and operation details of each process.

[0115] Example 1

[0116] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0117] Many households face the challenge of efficiently self-consuming surplus electricity generated by residential solar power generation systems, for which the Feed-in Tariff (FIT) scheme has expired. Furthermore, the high cost of purchasing and installing energy storage devices for individual households is another major obstacle. Furthermore, efficient power management and consumption across an entire region requires the prediction and optimization of power generation and consumption, and responses to power shortages and disasters are also required. Given this background, there is a demand for systems that enable the efficient purchase and installation of energy storage devices and power management.

[0118] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0119] In this invention, the server includes means for collecting energy storage device installation request information from households, means for ordering energy storage devices in bulk, means for reserving centralized installation locations for the energy storage devices, means for providing installation progress information, means for collecting data on power generation and consumption, means for predicting power generation and consumption based on the collected data, means for instructing optimal power usage, means for collecting data on power supply and demand, means for calculating buying and selling prices, means for providing calculated price information, means for collecting and analyzing power usage data, and means for providing analysis results. This makes it possible to efficiently install energy storage devices, reduce costs, and predict and optimize power generation and consumption, thereby realizing efficient power management and consumption throughout the entire region.

[0120] "Household" refers to an individual or group of occupants, a unit of living and consuming energy within a particular dwelling.

[0121] An "energy storage device" is a device for storing excess electricity generated by home solar power generation, and includes batteries and storage cells.

[0122] "Installation preference information" is data indicating the user's wishes and requests regarding the installation of an energy storage device, and specifically includes information such as the desired installation date, budget, and installation location.

[0123] "Bulk ordering means" refers to a method or process for placing bulk orders with suppliers based on the installation preference information for energy storage devices collected from multiple households.

[0124] "Aggregate installation site" refers to a concentrated installation area for multiple homes selected for efficient installation of energy storage devices.

[0125] "Installation progress information" refers to data regarding the progress of the installation work of the energy storage device, and is provided so that the user can check it.

[0126] "Electricity generation" refers to the amount of electricity generated by solar power generation systems, etc.

[0127] "Consumption" refers to the amount of electricity actually used by a household.

[0128] "Collected data" refers to various data obtained from households, such as electricity generation, consumption, and installation preferences.

[0129] "Forecasting means" refers to the method or process of forecasting future electricity generation and consumption based on collected data using machine learning algorithms, statistical models, etc.

[0130] "Means for instructing optimal power usage" refers to a method or process that provides specific instructions for optimizing power usage within a home based on the prediction results.

[0131] "Electricity demand" refers to the total amount of electricity required within a market or a specific region.

[0132] "Supply data" refers to data regarding the amount of electricity supplied from power plants, solar power generation systems, etc.

[0133] "Purchase price" refers to the transaction price for buying and selling electricity, which is determined based on supply and demand.

[0134] "Means of calculation" refers to the method or process for calculating the purchase and sale price of electricity.

[0135] "Means of providing" refers to the method or process for notifying the user of the necessary information or instructions.

[0136] "Electricity usage data" refers to data relating to individual electricity usage within a household or community.

[0137] "Analytical means" refers to the methods and processes used to analyze collected electricity usage data and identify patterns and trends.

[0138] The "analysis results" are the results of analyzing the power usage data, and include energy consumption patterns and recommendations for energy conservation.

[0139] This invention is a system for promoting the self-consumption of surplus electricity generated by residential solar power generation systems for which the feed-in tariff (FIT) system has expired, and for promoting preparations for power shortages and the creation of disaster-resistant towns. This system includes a means for collecting information on energy storage device installation requests from households and maximizing cost benefits through bulk orders, as well as a means for optimizing the flow of electricity using AI to achieve efficient electricity consumption within a community.

[0140] Bulk purchasing and centralized installation of energy storage devices

[0141] The server collects energy storage device installation preference information from each household. This process is carried out using a web or mobile application (e.g., React or Flutter) with data provided by each household via the internet. The collected installation preference information is stored in a database (e.g., MySQL or PostgreSQL). The server then uses the aggregated data to select the most suitable supplier and place a bulk order. This bulk order is more cost-effective than individual purchases by each household.

[0142] For example, if 100 households in a region wish to install energy storage devices, the server will consolidate this information and place a bulk order. The ordered energy storage devices will be installed at a centralized installation location in cooperation with the local government. Users can check the installation progress information in real time using their smartphones or PCs.

[0143] Optimizing power flow with AI

[0144] The server collects data on power generation and consumption from each household's solar power generation system and consumption devices. This data collection is done using IoT devices (e.g., smart meters) and communication protocols (e.g., MQTT). The server processes the collected data and predicts the next day's generation and consumption amounts based on machine learning algorithms (e.g., TensorFlow or PyTorch). Based on the prediction results, it creates an optimal energy storage plan and sends appropriate power usage instructions to each household.

[0145] For example, if the weather forecast for the next day is sunny, the server will predict a high amount of power generation and instruct the system to prioritize storing surplus power. Conversely, during times of high consumption, the system will instruct the system to supply power from the storage battery, achieving efficient power usage. Users can check the AI's instructions via their device and manually adjust the settings as needed.

[0146] Dynamic pricing of electricity prices using AI

[0147] The server collects local electricity demand and supply data in real time and calculates buying and selling prices using an AI algorithm. The algorithm used for this calculation is suitable for analyzing time-series data (for example, the ARIMA model or LSTM). The server then sends the calculated price information to the user's device via a notification system (for example, Firebase Cloud Messaging).

[0148] For example, during times when demand for electricity in a region is high and supply is insufficient, the server will send notifications to terminals encouraging users to sell electricity at a higher price. Conversely, during times of excess supply, the server will offer lower prices to encourage users to purchase electricity. Users can make buying and selling decisions based on this price information.

[0149] AI-based collection and analysis of electricity usage data

[0150] The server collects real-time electricity usage data from each household and analyzes it using AI algorithms. This analysis utilizes data stored in a database (e.g., MongoDB or SQLite). Based on the analysis results, energy consumption patterns and energy-saving recommendations are generated and provided to users through a notification system.

[0151] For example, if a particular household shows high power consumption during a particular time period, the server will detect that pattern and notify the device of specific actions to save energy (such as reducing power consumption at night). Users can then check their own energy consumption patterns and adjust their settings to further save energy.

[0152] Example prompts for generative AI models

[0153] "Please explain the design of an optimal power management system for self-consumption of surplus electricity from residential solar power generation. Specifically, please show the system's processing steps, including bulk purchasing and centralized installation of energy storage devices, optimization of power flow and dynamic pricing using AI, and energy conservation recommendations."

[0154] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0155] Step 1: Collect information on installation preferences from households

[0156] The user uses a smartphone or PC to input desired installation information for the energy storage device (desired installation date, budget, installation location, etc.). The input information is sent from the device to the server. The input is done using a web application or mobile application (e.g., React or Flutter). The server stores the received desired installation information in a database (e.g., MySQL or PostgreSQL). This allows the server to obtain the desired installation information for use in subsequent processing. Specifically, the user enters the information they wish to provide and presses the "Submit" button, which sends the information to the server.

[0157] input:

[0158] Installation information entered by the user (desired installation date, budget, installation location, etc.)

[0159] output:

[0160] Installation preference information stored in the database

[0161] Step 2: Place a bulk order based on your installation requirements

[0162] The server aggregates the installation preference information collected from households. Based on this information, the server selects the most suitable supplier. For example, a cost-based optimization algorithm is used for the selection. The server then places a bulk order with the supplier. Specifically, the server automatically generates order data and sends it to the supplier. This allows the server to aggregate the installation preferences of multiple households and place an order with the supplier, achieving cost-effectiveness.

[0163] input:

[0164] Installation preference information stored in the database

[0165] output:

[0166] Bulk order data from suppliers

[0167] Step 3: Provide installation progress information

[0168] The server periodically receives installation progress information from the supplier after receiving the order and stores it in a database. The terminal provides the installation progress information to the user in real time. Specifically, the progress information is notified via a smartphone or PC application. This allows the user to constantly check the installation status of the energy storage device.

[0169] input:

[0170] Installation progress information received from the supplier

[0171] output:

[0172] Notifying users of installation progress information

[0173] Step 4: Collecting electricity production and consumption data

[0174] The server collects data on electricity generation and consumption from each home's solar power generation system and consumption devices. This data is collected using IoT devices (e.g., smart meters) and communication protocols (e.g., MQTT). The server stores the collected data in a database. Specifically, the smart meters send data to the server in real time, and the server records it in the database.

[0175] input:

[0176] Electricity generation and consumption data received from smart meters

[0177] output:

[0178] Power generation and consumption data stored in a database

[0179] Step 5: Forecast production and consumption

[0180] The server uses machine learning algorithms (such as TensorFlow or PyTorch) to predict the next day's generation and consumption amounts based on the collected data on power generation and consumption. Specifically, the server inputs the data into the algorithm, generates predictions, and stores them in a database. This makes it easier to plan the next day's power usage.

[0181] input:

[0182] Power generation and consumption data stored in a database

[0183] output:

[0184] Machine learning algorithm predicts next day's production and consumption

[0185] Step 6: Delivering optimal power usage instructions

[0186] The server creates an optimal power usage plan based on the prediction results and notifies each household. The notification is sent to the device in real time using, for example, Firebase Cloud Messaging. Specifically, the server generates optimal power usage plan data and distributes it to the user's device via the notification system. This allows the user to receive instructions on efficient power usage.

[0187] input:

[0188] Production and consumption forecast results using machine learning algorithms

[0189] output:

[0190] Notification of optimal power usage instructions to each household

[0191] Step 7: Collect electricity demand and supply data

[0192] The server collects real-time electricity demand and supply data within the region. This data is also stored in a database and used for subsequent processing. Specifically, the server analyzes and stores data collected from electricity meters within the region.

[0193] input:

[0194] Power supply and demand data received from power meters

[0195] output:

[0196] Power demand and supply data stored in a database

[0197] Step 8: Calculate the purchase price

[0198] The server calculates the buying and selling prices using AI algorithms (such as LSTM or ARIMA models) based on the collected electricity demand and supply data. Specifically, the AI ​​algorithm analyzes the data and calculates the buying and selling prices in real time, making dynamic pricing possible.

[0199] input:

[0200] Power demand and supply data stored in a database

[0201] output:

[0202] Calculated purchase and sale price data

[0203] Step 9: Notification of purchase and sale price information

[0204] The server sends the calculated buying and selling price information to the user's device via a notification system. The user buys and sells electricity based on this information. Specifically, the server sends the price information to the device using a notification system such as Firebase Cloud Messaging.

[0205] input:

[0206] Calculated purchase and sale price data

[0207] output:

[0208] Notification of buying and selling price information to users

[0209] Step 10: Collect and analyze electricity usage data

[0210] The server collects electricity usage data from each household in real time and analyzes it using AI algorithms (e.g., k-means clustering and deep learning). The analysis results are stored in a database and provided to users. Specifically, the server analyzes the collected data, stores the results, and notifies them.

[0211] input:

[0212] Electricity usage data collected from households

[0213] output:

[0214] AI analysis results and notification data

[0215] (Application example 1)

[0216] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0217] It is becoming increasingly important to effectively utilize surplus electricity from residential solar power generation systems, promote self-consumption, and realize efficient electricity management across the entire region. In particular, it is necessary not only to efficiently utilize electricity within individual households, but also to provide economic benefits through the sharing and trading of surplus electricity. There is also a need to balance supply and demand through dynamic electricity pricing and provide attractive price information to consumers in real time. Therefore, it is necessary to provide a comprehensive system to solve these issues, which have been difficult to address with conventional systems.

[0218] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0219] In this invention, the server includes means for collecting storage battery installation request information from households, means for ordering storage batteries in bulk, means for reserving centralized storage battery installation locations, means for providing installation progress information, means for collecting data on power generation and consumption, means for predicting power generation and consumption based on the collected data, means for instructing optimal power usage, means for collecting data on power supply and demand, means for calculating buying and selling prices, means for providing dynamic price information based on the predicted data and notifying the user, means for storing surplus power in a digital wallet as power tokens, means for enabling the buying and selling of power tokens to other users, means for collecting and analyzing power usage data, and means for providing the analysis results. This makes it possible to effectively manage surplus power generated by home solar power generation and provide optimal power usage and economic benefits.

[0220] "Storage battery installation request information" is information collected from homes and facilities that wish to install power storage equipment.

[0221] A "centralized battery installation site" is a location where batteries are installed together in order to efficiently manage electricity throughout an entire region.

[0222] "Installation progress information" is information indicating how far the installation work of the storage battery has progressed.

[0223] An "electricity token" is a digital record of excess electricity generated by a home solar power system.

[0224] A "digital wallet" is an electronic wallet for managing tokens or currencies recorded in digital form.

[0225] "Dynamic price information" refers to price information that fluctuates in real time depending on the electricity supply and demand situation.

[0226] "Marketplace" means an electronic exchange for the buying and selling of energy tokens between users.

[0227] "Electricity usage data" is information on the amount of electricity consumed by each household or facility.

[0228] "Analysis results" are the results of analysis using AI and other technologies based on collected data.

[0229] "Predictive data" is data that predicts future electricity generation and consumption using AI algorithms.

[0230] This invention is a comprehensive energy management system aimed at utilizing surplus electricity generated by residential solar power generation, with a particular focus on digital wallet applications including energy tokens and dynamic pricing functionality.

[0231] The server collects battery installation requests from each household and places a bulk order for the batteries. It also secures centralized battery installation locations and provides installation progress information. It collects data on power generation and consumption, and uses AI algorithms to predict power generation and consumption based on this data. Specific software used includes machine learning libraries such as Scikit-learn. Based on the predictions, it instructs on optimal power usage and notifies households in real time.

[0232] Furthermore, data on electricity supply and demand is collected and buying and selling prices are dynamically calculated using AI algorithms. The calculated price information is provided to users, and dynamic price information is provided based on forecast data. This allows users to buy and sell energy tokens at the most advantageous price. Energy tokens are a digital record of surplus electricity and are stored in a digital wallet. Users can buy and sell energy tokens with other users on the buying and selling marketplace using their own devices (smartphones, tablets, etc.).

[0233] For example, if a household generates 10 kWh and consumes 5 kWh in a day, the difference of 5 kWh is stored in the digital wallet as electricity tokens. Then, the AI ​​predicts whether the weather will be sunny the next day and notifies the user in real time of the times when electricity prices will be high. In this way, users can efficiently buy and sell surplus electricity.

[0234] The server also constantly collects power usage data and provides the analysis results to the user. Based on the analysis results, the user can understand power consumption patterns and take power-saving measures as necessary, thereby achieving further energy savings.

[0235] Prompt Sentence Examples

[0236] "Please create an example implementation of a system that manages surplus electricity generated by a home solar power generation system as electricity tokens and notifies prices in real time via a smartphone app. Please also add a marketplace function that allows users to buy and sell electricity tokens."

[0237] In this way, the present invention provides a comprehensive energy management system that covers all processes from battery installation to energy token management and dynamic pricing notification.

[0238] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0239] Step 1:

[0240] The server collects information from each household regarding their preference for battery installation.

[0241] Input: Battery installation request information from households

[0242] Data processing and calculation: The collected information is stored in a database, and the number of people who wish to install the device and the desired installation date are compiled.

[0243] Output: List of battery installation applicants

[0244] Step 2:

[0245] The server orders storage batteries in bulk based on the installation preference information collected.

[0246] Input: List of people wanting to install a storage battery

[0247] Data processing and data calculation: Supplier selection and bulk order optimization calculation.

[0248] Output: Order data for the best supplier

[0249] Step 3:

[0250] The server secures a centralized installation location for the storage batteries and provides installation progress information.

[0251] Input: Bulk order data, installation request list

[0252] Data processing and data calculation: Select the installation location and manage the installation schedule.

[0253] Output: Update installation progress information

[0254] Step 4:

[0255] The server collects data on the amount of electricity generated and consumed by each household.

[0256] Input: Power generation and consumption data for each household

[0257] Data processing and calculation: The amount of power generated and consumed is calculated based on the collected data.

[0258] Output: Data set of daily and hourly energy production and consumption

[0259] Step 5:

[0260] The server uses AI algorithms to predict power generation and consumption based on the collected data.

[0261] Input: Dataset of daily and hourly energy production and consumption

[0262] Data processing and data calculations: Machine learning models are used to predict the next day's electricity generation and consumption.

[0263] Output: Forecast data

[0264] Step 6:

[0265] The server instructs on optimal power usage and notifies households in real time.

[0266] Input: Forecast data

[0267] Data processing and calculation: Generate an optimal power usage plan and notify each household.

[0268] Output: Power usage instruction

[0269] Step 7:

[0270] The server collects data on electricity demand and supply and dynamically calculates buying and selling prices.

[0271] Input: Demand data, Supply data

[0272] Data processing and calculation: Calculate appropriate electricity prices using AI algorithms.

[0273] Output: Dynamic price information

[0274] Step 8:

[0275] The server provides and notifies the user of dynamic pricing information based on the forecast data.

[0276] Input: Dynamic Price Information

[0277] Data processing and data calculation: price information is generated and sent to the user terminal.

[0278] Output: Price notification to user

[0279] Step 9:

[0280] The server stores the surplus electricity in a digital wallet as electricity tokens.

[0281] Input: Surplus power data

[0282] Data processing and data calculation: Surplus electricity is tokenized and stored in a digital wallet.

[0283] Output: Power tokens

[0284] Step 10:

[0285] The server allows users to buy and sell energy tokens to other users and transacts them on the marketplace.

[0286] Input: Power tokens, desired trading information

[0287] Data processing and data calculation: Matching token purchases and sales and updating transaction records.

[0288] Output: Transaction completion notification

[0289] Step 11:

[0290] The server collects power usage data and provides the analysis results to the user.

[0291] Input: Power usage data

[0292] Data processing and data calculation: Perform data analysis and generate energy-saving recommendations.

[0293] Output: Notification of analysis results

[0294] A sample prompt is, "Please create an implementation example of a system that manages surplus electricity generated by a home solar power generation system as electricity tokens and notifies prices in real time via a smartphone app. Please also add a marketplace function that allows users to buy and sell electricity tokens."

[0295] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0296] ---

[0297] This invention is a system that promotes the self-consumption of surplus electricity generated by residential solar power generation systems for which the feed-in tariff (FIT) system has expired, and promotes preparations for power shortages and the creation of disaster-resistant towns. This system includes a means for collecting information on battery installation requests from households and maximizing cost benefits through bulk orders. It also utilizes AI to optimize the flow of electricity and achieve efficient power consumption within the region. Furthermore, it integrates an emotion engine that recognizes the user's emotions and suggests optimal energy-saving actions based on the user's energy consumption patterns.

[0298] 1. Bulk purchase and centralized installation of storage batteries

[0299] The server collects battery installation request information from each household. This information is then stored in a database, along with the number of people wanting to install a battery and the desired installation time. The server then selects the most suitable supplier based on the aggregated data and places a bulk order. This is more cost-effective than purchasing batteries individually.

[0300] For example, if 100 households in a region request the installation of storage batteries, the server will compile this information and place a bulk order. The obtained storage batteries will be installed at a centralized installation location in cooperation with the local government. Users can check the installation progress information in real time using their smartphones or PCs.

[0301] 2. Optimizing power flow using AI

[0302] The server collects data on power generation and consumption from each home's solar power generation system and power consumption devices. Based on this data, the server uses an AI algorithm to predict the next day's power generation and consumption. Based on the prediction results, the server creates an optimal power storage plan and sends appropriate power usage instructions to each household.

[0303] For example, if the weather forecast for the next day is sunny, the server will predict high power generation and instruct the system to prioritize storing surplus power. Conversely, during times of high consumption, the system will instruct the system to supply power from the storage battery, achieving efficient power usage. Users can check the AI's instructions via their device and manually adjust the settings as needed.

[0304] 3. Dynamic pricing of electricity using AI

[0305] The server collects local electricity demand and supply data in real time and calculates the buying and selling price using an AI algorithm. Based on this calculation, the device notifies the user of dynamic price information. This allows users to buy and sell electricity at a more advantageous price than under a contract with the power company.

[0306] For example, during times when demand for electricity in a region is high and supply is insufficient, the server will send notifications to terminals encouraging users to sell electricity at a higher price. Conversely, during times of excess supply, the server will offer lower prices to encourage users to buy electricity. Users can make buying and selling decisions based on this price information.

[0307] 4. AI-based data collection and analysis

[0308] The server collects real-time electricity usage data from each household and analyzes it using AI algorithms. Based on the results, recommendations for energy consumption patterns and energy conservation are generated and provided to users via their devices.

[0309] For example, if a household shows high power consumption during a certain time period, the server will detect this pattern and notify the device of specific actions to save energy (such as reducing power use at night). Users can then check their own energy consumption patterns and adjust their settings to achieve further energy savings.

[0310] 5. Integration of emotion engine that recognizes user emotions

[0311] The emotion engine collects emotional data from the user's facial expressions and voice and sends it to the server, which then analyzes the user's emotional state and identifies factors that affect power usage patterns.

[0312] For example, if the device detects that the user is feeling stressed, the server can use that data to adjust energy-saving behaviors, such as suggesting actions that help with relaxation (adjusting lighting, setting the air conditioner temperature, playing music, etc.), thereby improving the user's comfort.

[0313] 6. Adjusting energy-saving actions based on emotion data

[0314] The server combines the collected emotional data with power consumption data to generate optimal energy-saving actions, enabling flexible energy management according to the user's emotional state.

[0315] For example, when the user is relaxing, the server will prioritize actions to reduce power consumption. Conversely, when the user is energetic and active, the server will suggest settings that prioritize comfort. The user can easily adjust the energy-saving settings based on this.

[0316] By combining user emotion recognition, the system of the present invention offers greater flexibility and user experience than conventional energy-saving systems, thereby improving not only energy efficiency but also the user's quality of life.

[0317] The processing flow will be explained below.

[0318] ---

[0319] 1. Bulk purchase and centralized installation of storage batteries

[0320] Step 1:

[0321] The user inputs the desired battery installation information into the terminal, which then sends the information to the server.

[0322] Step 2:

[0323] The server stores and aggregates battery installation request information in a database.

[0324] Step 3:

[0325] The server selects the most suitable supplier based on the aggregated data and places a bulk order.

[0326] Step 4:

[0327] The server will work with local governments to secure a centralized installation location.

[0328] Step 5:

[0329] The server updates installation progress information in real time and notifies the user via the terminal.

[0330] ---

[0331] 2. Optimizing power flow using AI

[0332] Step 1:

[0333] The device measures the amount of electricity generated and consumed within the home in real time and sends the data to a server.

[0334] Step 2:

[0335] The server uses the collected data to run an AI algorithm and predict the next day's power generation and consumption.

[0336] Step 3:

[0337] Based on the prediction results, the server generates an optimal energy storage plan and instructions for power usage for each household.

[0338] Step 4:

[0339] The server generates instructions and distributes them to each household's terminal.

[0340] Step 5:

[0341] The user checks the instructions received through the device and manually adjusts the settings if necessary.

[0342] ---

[0343] 3. Dynamic pricing of electricity using AI

[0344] Step 1:

[0345] The server collects real-time data on electricity demand and supply within the region.

[0346] Step 2:

[0347] The server uses an AI algorithm to analyze the collected data and calculate the optimal buying and selling price.

[0348] Step 3:

[0349] The server sends the calculated purchase and sale price information to each household's terminal.

[0350] Step 4:

[0351] The device sends push notifications to the user, providing buying and selling price information.

[0352] Step 5:

[0353] The user checks the buying and selling price information on the terminal and decides whether to buy or sell electricity.

[0354] ---

[0355] 4. AI-based data collection and analysis

[0356] Step 1:

[0357] The terminal collects electricity usage data for each household in real time and sends it to a server.

[0358] Step 2:

[0359] The server stores the collected data in a database.

[0360] Step 3:

[0361] The server uses AI algorithms to analyze the data and identify energy consumption patterns.

[0362] Step 4:

[0363] The server generates energy-saving actions based on the analysis results and sends them to the terminal.

[0364] Step 5:

[0365] The device notifies the user of the analysis results and energy-saving actions.

[0366] Step 6:

[0367] The user checks the analysis results on the device and adjusts settings to save energy.

[0368] ---

[0369] 5. Integration of emotion engine that recognizes user emotions

[0370] Step 1:

[0371] The device analyzes the user's facial expressions and voice in real time to generate emotional data.

[0372] Step 2:

[0373] The device transmits the emotion data to the server.

[0374] Step 3:

[0375] The server analyzes the user's emotional state based on the emotional data.

[0376] Step 4:

[0377] The server combines emotional state and power consumption data to identify factors that influence energy consumption patterns.

[0378] ---

[0379] 6. Adjusting energy-saving actions based on emotion data

[0380] Step 1:

[0381] The server analyzes the emotion data and power consumption data and generates energy-saving actions.

[0382] Step 2:

[0383] The server sends energy-saving actions to each household's terminal.

[0384] Step 3:

[0385] The terminal notifies the user of energy-saving actions based on the emotion data.

[0386] Step 4:

[0387] The user checks the proposed energy-saving action on the terminal and decides whether to execute it.

[0388] ---

[0389] The above are the specific processing steps and operation details of the invention combined with the emotion engine.

[0390] Example 2

[0391] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0392] With the end of the Feed-in Tariff (FIT) system in modern society, there is a need to promote the self-consumption of surplus electricity from residential solar power generation while also preparing for power shortages and creating disaster-resistant communities. However, purchasing and installing energy storage devices individually for each household poses challenges, including high costs and the hassle of installation. There are also concerns about reduced efficiency due to suboptimal power usage patterns. Furthermore, there are no systems that take into account the influence of user emotions on power consumption behavior.

[0393] The specification process by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for collecting information on desired installation of household power storage devices, a means for ordering power storage devices in bulk, a means for reserving centralized installation locations for the power storage devices, a means for providing installation progress information, a means for collecting data on power generation and consumption, a means for predicting power generation and consumption based on the collected data, a means for instructing optimal power usage, a means for collecting data on power supply and demand, a means for calculating buying and selling prices, a means for providing calculated price information, a means for collecting and analyzing power usage data, a means for providing the analysis results, a means for collecting emotion data and analyzing factors affecting power usage patterns, and a means for generating and providing energy-saving actions based on the user's emotional state. This reduces the high cost and installation effort for each household, enables optimization of power usage patterns, and efficient energy management. Furthermore, flexible energy-saving actions based on the user's emotions can improve the user experience and quality of life.

[0394] A "home energy storage device" is a device with an energy storage function that is installed in a home and stores surplus electricity generated by solar power generation and other means, and supplies it when needed.

[0395] The "installation preference information" is information indicating that a household wishes to install a power storage device, and includes details such as the number of applicants and the desired installation time.

[0396] "Bulk ordering" refers to ordering multiple household energy storage devices at once, and is an ordering method that offers cost benefits over ordering individually.

[0397] "Concentrated installation site" means a specific location set aside temporarily or permanently for the efficient installation of energy storage devices.

[0398] "Installation progress information" is information indicating whether the installation of the power storage device is proceeding as planned, and is information that provides the progress status of the installation process in real time.

[0399] "Power generation amount" is data indicating the amount of power generated by the photovoltaic power generation system during a certain time period.

[0400] "Consumption" is data indicating the amount of electricity used within the home.

[0401] "Collected Data" refers to various data measured and acquired by the system, such as the amount of electricity generated and consumed.

[0402] "Forecasting" means estimating future power generation and consumption based on collected data.

[0403] "Directing power usage" means proposing and delivering optimal power usage methods to each household based on collected data and predictions.

[0404] "Dynamic pricing" is a pricing method that changes electricity prices based on real-time conditions of electricity supply and demand.

[0405] "Price information" refers to information including pricing and calculation results related to the purchase and sale of electricity.

[0406] "Electricity usage data" refers to historical information on the amount of electricity actually used within a household.

[0407] "Analysis" means the process of analyzing collected data and converting the information into a useful form.

[0408] "Emotion data" is data that indicates the emotional state of the user as determined by their facial expressions and voice.

[0409] "Energy saving actions" refer to specific actions or measures that should be taken to reduce energy consumption.

[0410] "User status" is information that indicates the user's current emotions and behavioral status.

[0411] The above definitions allow a clear understanding of each element of the invention and its function.

[0412] This invention relates to a system that improves the efficiency of home energy storage device installation and power management. This system collects information on energy storage device installation requests from households, reduces costs through bulk ordering, and uses AI to predict and optimize power consumption. It also collects user emotional data and suggests energy-saving actions based on emotions, improving the user experience.

[0413] Hardware and Software

[0414] server:

[0415] Database (storage of information on desired installation of power storage devices, and data on power generation and consumption)

[0416] AI algorithms (power forecasting, dynamic pricing, sentiment analysis)

[0417] Communication module (transmits and receives data from home devices)

[0418] Terminals (home devices):

[0419] Smartphone, tablet, PC (for entering and checking information)

[0420] Smart meters (for collecting data on electricity generation and consumption)

[0421] Emotion engine (for collecting emotion data)

[0422] Basic operation

[0423] Collection of information on home energy storage device installation requests:

[0424] Users enter their desired installation information for the storage device via a dedicated application on their smartphone or PC and send it to the server.

[0425] Supplier selection and bulk ordering:

[0426] Based on the installation information collected by the server, the system selects the most suitable supplier and places a bulk order for the energy storage devices, reducing costs compared to purchasing them individually.

[0427] Electricity generation and consumption data collection:

[0428] It collects electricity data in real time from smart meters and energy management systems (EMS) in homes and sends it to a server.

[0429] Prediction and optimization:

[0430] The server uses AI algorithms to analyze the collected data and predict the next day's electricity generation and consumption, and based on this, generates and presents an optimal electricity usage plan for each household.

[0431] Dynamic Pricing:

[0432] The server uses an AI algorithm to calculate the electricity buying and selling price based on real-time data on electricity demand and supply. The calculated price information is sent to the terminal, and the user can decide whether to buy or sell electricity.

[0433] Collecting emotional data and suggesting energy-saving actions:

[0434] The device uses an emotion engine to collect emotional data from the user's facial expressions and voice, and sends it to the server. The server analyzes the emotional data and power usage patterns, and generates and suggests energy-saving actions based on the user's emotional state.

[0435] Specific examples

[0436] If 100 households in a region wish to install a power storage device, each household can enter their information through a dedicated app. The server compiles this information and places a bulk order with significant cost benefits. Based on the power generation and consumption data collected from each household's smart meter, the system calculates the optimal power usage plan for the next day and notifies the smartphone or PC. Also, if the weather forecast predicts clear skies, the system will prioritize storing surplus power, thereby achieving efficient energy use. Furthermore, when the user is feeling stressed, the system will suggest actions to help them relax (such as adjusting the lighting or playing music).

[0437] Prompt Sentence Examples

[0438] Please explain the specific implementation procedures and effects of an energy management system that combines residential solar power generation and storage batteries, as shown below.

[0439] A system that collects battery installation request information from each household on a server and places a bulk order

[0440] A function that uses AI to predict power generation and consumption and provide optimal power usage plans

[0441] Dynamic pricing calculates electricity prices in real time and notifies users

[0442] A function that recognizes the user's emotions and suggests optimal energy-saving actions based on those emotions.

[0443] With the above configuration, the system of the present invention can improve the efficiency of installation and power management of home power storage devices, and can improve the energy consumption and quality of life of users.

[0444] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0445] Step 1:

[0446] Collecting information on home energy storage device installation requests

[0447] Input: The user enters desired information about the installation of the energy storage device (such as name, address, and desired installation date) through a dedicated application.

[0448] How it works: User input information is sent from the smartphone or PC to the server.

[0449] Output: The server stores these preferences in a database.

[0450] Step 2:

[0451] Supplier selection and bulk ordering

[0452] Input: The server compiles the storage battery installation request information stored in the database.

[0453] How it works: The server compares the prices, delivery times, and performance data of each supplier based on the aggregated data, selects the best supplier, and places a bulk order.

[0454] Output: A purchase order is placed with the best supplier and the required number of storage units is determined.

[0455] Step 3:

[0456] Collecting data on electricity generation and consumption

[0457] Input: Real-time electricity generation and consumption data collected from home smart meters and energy management systems.

[0458] How it works: The devices send this data to a server, which stores the data collected from all homes in a database.

[0459] Output: The electricity generation and consumption data stored on the server is updated.

[0460] Step 4:

[0461] Forecasting and optimizing power generation and consumption

[0462] Input: Historical electricity generation and consumption data stored on the server, along with related information such as weather forecasts.

[0463] How it works: The server uses AI algorithms to predict the next day's electricity generation and consumption. Based on this, the server generates an optimal electricity usage plan (when to store electricity and when to use it).

[0464] Output: A specific electricity usage plan for each household is generated and sent to the terminal.

[0465] Step 5:

[0466] Dynamic pricing calculation and notification

[0467] Input: Local electricity demand and supply data collected in real time by the server.

[0468] How it works: The server uses an AI algorithm to calculate the optimal price for selling and purchasing electricity.

[0469] Output: The calculated purchase and sale price information is sent to the home device.

[0470] Step 6:

[0471] Energy consumption data collection and analysis

[0472] Input: Electricity usage data collected from households.

[0473] How it works: The server analyzes this data using AI algorithms to generate energy consumption patterns and energy-saving recommendations.

[0474] Output: The generated recommendations are sent to the home device and notified to the user.

[0475] Step 7:

[0476] Collecting emotional data and suggesting energy-saving actions

[0477] Input: Emotional data such as facial expressions and voice of the user collected by the device.

[0478] How it works: The device sends this data to the server, which then analyzes it using an emotion engine. Based on the analysis, it generates energy-saving actions that correspond to the user's emotional state.

[0479] Output: The proposed energy saving actions are notified to the home device.

[0480] Through these processing steps, the system can integrate and manage the efficient deployment of energy storage devices, the optimization of power usage patterns, dynamic pricing, and emotion-based energy-saving behavior, thereby improving users' energy consumption and quality of life.

[0481] (Application example 2)

[0482] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0483] Efficient installation and operation of storage batteries is important to promote self-consumption of surplus electricity from residential solar power generation systems. However, purchasing and installing individual storage batteries is costly, and an advanced management system is required to efficiently use and optimally buy and sell electricity. Furthermore, flexible energy management that takes into account the emotional state of the user is required to balance power consumption with user comfort. A comprehensive system is needed to solve these problems.

[0484] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting storage battery installation preference information from households, means for ordering storage batteries in bulk, means for reserving centralized storage battery installation locations, means for providing installation progress information, means for collecting data on power generation and consumption, means for predicting power generation and consumption based on the collected data, means for instructing optimal power usage, means for collecting data on power supply and demand, means for calculating buying and selling prices, means for providing calculated price information, means for collecting and analyzing power usage data, means for providing the analysis results, means for collecting emotion data, means for adjusting settings based on the emotion data, and means for optimizing lighting and air conditioning settings based on the emotion data. This enables cost reduction through efficient installation and bulk purchase of storage batteries, and further realizes optimal power usage using AI and comfortable energy management based on the user's emotional state.

[0485] "Means for collecting information on battery installation preferences from households" refers to devices or systems that collect information on each household's desire to install a battery.

[0486] "Means for bulk ordering of batteries" refers to a method or system for ordering a large number of batteries at once.

[0487] "Means for securing locations for centralized installation of storage batteries" refers to methods or systems for centralizing and securing suitable locations for installing storage batteries.

[0488] "Means for providing installation progress information" refers to a device or system that provides information about the installation progress of a storage battery.

[0489] "Means for collecting data on electricity generation and consumption" refers to devices or systems that collect data on electricity generation and consumption.

[0490] "Means for predicting power generation and consumption based on collected data" refers to a method or system for predicting future power generation and consumption based on collected data.

[0491] "Means for instructing optimal power usage" refers to devices or systems that issue instructions for efficient power usage based on predictive data.

[0492] "Means for collecting electricity demand and supply data" refers to devices and systems that collect information on electricity demand and supply.

[0493] "Means for calculating purchase and sale prices" refers to methods and systems for calculating the purchase and sale prices of electricity based on collected data.

[0494] "Means for providing calculated price information" refers to a device or system that provides calculated price information to a user.

[0495] "Means for collecting and analyzing electricity usage data" refers to devices and systems that collect and analyze data related to electricity usage.

[0496] "Means for providing analysis results" refers to a device or system that provides the results of analyzed data to the user.

[0497] "Means for collecting emotional data" refers to a device or system that collects data about a user's emotional state.

[0498] "Means for adjusting settings based on emotional data" refers to a method or system for adjusting various system settings based on collected emotional data.

[0499] "Means for optimizing lighting and air conditioning settings based on emotional data" refers to devices and systems that optimize lighting and air conditioning settings based on emotional data.

[0500] This invention is a comprehensive system for promoting self-consumption of surplus electricity from a home solar power generation system or for efficient power management in a physical store. The system includes an emotion engine for optimizing lighting and air conditioning based on the user's emotional state.

[0501] System Configuration

[0502] The system includes several means:

[0503] 1. A means of collecting information on households' desire to install storage batteries

[0504] 2. A way to order batteries in bulk

[0505] 3. Measures to secure locations for centralized battery installation

[0506] 4. Means of providing installation progress information

[0507] 5. Means of collecting data on electricity generation and consumption

[0508] 6. A means of forecasting power generation and consumption based on collected data

[0509] 7. Means of directing optimal power usage

[0510] 8. Means of collecting electricity demand and supply data

[0511] 9. Means of calculating the purchase and sale price

[0512] 10. Means of Providing Calculated Pricing Information

[0513] 11. Means of collecting and analyzing electricity usage data

[0514] 12. Means of Providing Analysis Results

[0515] 13. Means of collecting emotional data

[0516] 14. A way to adjust settings based on emotional data

[0517] 15. A way to optimize lighting and air conditioning settings based on emotional data

[0518] What the program does

[0519] The server collects information on each household's battery installation preferences and places a bulk order, reducing installation costs. The server also secures centralized installation locations for the batteries and provides real-time updates on the installation progress.

[0520] The server collects data on each household's electricity generation and consumption and uses AI to predict future power generation and consumption. This allows it to create an optimal power usage plan and provide instructions to the user. It also collects data on electricity demand and supply and calculates electricity buying and selling prices in real time. The server then provides this price information to the user's device.

[0521] The emotion engine collects emotional data from the user's facial expressions and voice. The server analyzes the emotional data and adjusts lighting and air conditioning settings based on the user's emotional state, thereby improving the user's comfort.

[0522] Specific examples

[0523] For example, by using this system in a physical store, the following effects can be expected. First, the AI ​​predicts times when electricity usage will be high in the store and performs optimal energy management. Furthermore, the emotion engine recognizes customer emotions in real time, and if a customer appears stressed, it will dim the lights and adjust the air conditioning to provide a comfortable environment. These settings can be performed automatically by the server, eliminating the need for users to do anything.

[0524] Prompt Sentence Examples

[0525] An example of a prompt to be input to a generative AI model might be:

[0526] "Apply a system for managing excess electricity from home solar power generation systems to a physical store. Use AI to predict electricity usage and propose programs to optimize lighting and air conditioning settings based on the emotions of staff and customers."

[0527] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0528] Step 1:

[0529] The server collects battery installation preference information from households. The installation preference information (e.g., desired installation time, battery size, etc.) is sent from each household's terminal and stored as data. This allows the overall demand for battery installation to be grasped.

[0530] Step 2:

[0531] The server orders batteries in bulk. Based on the collected installation information, it generates and sends bulk ordering data to suppliers. Bulk orders can reduce costs.

[0532] Step 3:

[0533] The server secures a centralized installation location for the storage batteries. After the ordered storage batteries arrive, it aggregates and adjusts the installation locations for each household and secures a centralized installation location. It analyzes the geographical information from each household and generates a list of optimal installation locations.

[0534] Step 4:

[0535] The server provides installation progress information. It monitors the progress of the battery storage installation in real time and sends that information to the home device. The progress information includes the installation completion date and time and ongoing status data.

[0536] Step 5:

[0537] The server collects data on the amount of electricity generated and consumed. This data is input in real time from each household's solar power generation system and consumption devices, and the data is stored and managed.

[0538] Step 6:

[0539] The server predicts power generation and consumption amounts based on the collected data. The collected power generation and consumption data is input into an AI algorithm to predict power generation and consumption amounts for the next day. An energy management plan is generated based on the predicted data.

[0540] Step 7:

[0541] The server then instructs the optimal use of electricity. Based on the predicted power generation and consumption, it generates power usage instructions for each household and sends them to the terminal. Specifically, this includes battery charging and discharging schedules and timings for efficient power usage.

[0542] Step 8:

[0543] The server collects and stores data on electricity demand and supply. This data is input in real time and includes supply data from the power supply company and demand data from users.

[0544] Step 9:

[0545] The server calculates the buying and selling prices. Based on the collected electricity demand and supply data, an AI algorithm calculates the optimal buying and selling price. The price calculation algorithm takes into account market trends and seasonal fluctuations.

[0546] Step 10:

[0547] The server provides the calculated price information. The calculated purchase and sale price information is sent to a home terminal and notified to the user. The user receives the notification and makes a decision to execute the purchase and sale as necessary.

[0548] Step 11:

[0549] The server collects and analyzes real-time power usage data sent from home devices and analyzes it using AI algorithms to identify power usage patterns and generate recommendations for improving energy efficiency.

[0550] Step 12:

[0551] The server provides the analysis results. The analyzed power usage data is sent to the home device, where specific suggestions (e.g., recommended usage times for high-efficiency devices) are presented to promote the optimization of energy consumption.

[0552] Step 13:

[0553] The server collects emotion data by receiving the user's facial expressions and voice as analysis results from the emotion engine connected to the home device.

[0554] Step 14:

[0555] The server adjusts settings based on the emotion data. Based on the emotion data, it generates settings (e.g., lighting and air conditioning adjustments) to improve the user's comfort and sends them to the home device.

[0556] Step 15:

[0557] The server optimizes lighting and air conditioning settings based on the emotional data. It analyzes the emotional data and applies the most appropriate lighting and air conditioning settings to the home devices. By automatically reflecting the settings that the user finds most comfortable, it is possible to achieve both energy efficiency and comfort.

[0558] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0559] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0560] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0561] [Second embodiment]

[0562] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0563] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0564] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0565] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0566] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0567] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0568] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0569] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0570] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0571] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0572] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0573] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0574] ---

[0575] This invention is a system that promotes the self-consumption of surplus electricity generated by residential solar power generation systems for which the feed-in tariff (FIT) system has expired, and promotes preparation for power shortages and disaster-resistant urban development. This system includes a means for collecting information on battery installation requests from households and maximizing cost benefits through bulk orders. It also utilizes AI to optimize the flow of electricity and achieve efficient power consumption within the community.

[0576] 1. Bulk purchase and centralized installation of storage batteries

[0577] The server collects battery installation request information from each household. This information is then stored in a database, along with the number of people wanting to install a battery and the desired installation time. The server then selects the most suitable supplier based on the aggregated data and places a bulk order. This is more cost-effective than purchasing batteries individually.

[0578] For example, if 100 households in a region request the installation of storage batteries, the server will compile this information and place a bulk order. The obtained storage batteries will be installed at a centralized installation location in cooperation with the local government. Users can check the installation progress information in real time using their smartphones or PCs.

[0579] 2. Optimizing power flow using AI

[0580] The server collects data on power generation and consumption from each home's solar power generation system and power consumption devices. Based on this data, the server uses an AI algorithm to predict the next day's power generation and consumption. Based on the prediction results, the server creates an optimal power storage plan and sends appropriate power usage instructions to each household.

[0581] For example, if the weather forecast for the next day is sunny, the server will predict high power generation and instruct the system to prioritize storing surplus power. Conversely, during times of high consumption, the system will instruct the system to supply power from the storage battery, achieving efficient power usage. Users can check the AI's instructions via their device and manually adjust the settings as needed.

[0582] 3. Dynamic pricing of electricity using AI

[0583] The server collects local electricity demand and supply data in real time and calculates the buying and selling price using an AI algorithm. Based on this calculation, the device notifies the user of dynamic price information. This allows users to buy and sell electricity at a more advantageous price than under a contract with the power company.

[0584] For example, during times when demand for electricity in a region is high and supply is insufficient, the server will send notifications to terminals encouraging users to sell electricity at a higher price. Conversely, during times of excess supply, the server will offer lower prices to encourage users to buy electricity. Users can make buying and selling decisions based on this price information.

[0585] 4. AI-based data collection and analysis

[0586] The server collects real-time electricity usage data from each household and analyzes it using AI algorithms. Based on the results, recommendations for energy consumption patterns and energy conservation are generated and provided to users via their devices.

[0587] For example, if a household shows high power consumption during a certain time period, the server will detect this pattern and notify the device of specific actions to save energy (such as reducing power use at night). Users can then check their own energy consumption patterns and adjust their settings to achieve further energy savings.

[0588] In this way, the system of the present invention utilizes AI at each stage to achieve efficient power management and consumption, improving energy efficiency at the household level while also realizing sustainable energy management across the entire region.

[0589] The processing flow will be explained below.

[0590] ---

[0591] 1. Bulk purchase and centralized installation of storage batteries

[0592] Step 1:

[0593] The user inputs the desired battery installation information into the terminal, which then sends the information to the server.

[0594] Step 2:

[0595] The server stores and aggregates battery installation request information in a database.

[0596] Step 3:

[0597] The server selects the most suitable supplier based on the aggregated data and places a bulk order.

[0598] Step 4:

[0599] The server will work with local governments to secure a centralized installation location.

[0600] Step 5:

[0601] The server updates installation progress information in real time and notifies the user via the terminal.

[0602] ---

[0603] 2. Optimizing power flow using AI

[0604] Step 1:

[0605] The device measures the amount of electricity generated and consumed within the home in real time and sends the data to a server.

[0606] Step 2:

[0607] The server uses the collected data to run an AI algorithm and predict the next day's power generation and consumption.

[0608] Step 3:

[0609] Based on the prediction results, the server generates an optimal energy storage plan and instructions for power usage for each household.

[0610] Step 4:

[0611] The server generates instructions and distributes them to each household's terminal.

[0612] Step 5:

[0613] The user checks the instructions received through the device and manually adjusts the settings if necessary.

[0614] ---

[0615] 3. Dynamic pricing of electricity using AI

[0616] Step 1:

[0617] The server collects real-time data on electricity demand and supply within the region.

[0618] Step 2:

[0619] The server uses an AI algorithm to analyze the collected data and calculate the optimal buying and selling price.

[0620] Step 3:

[0621] The server sends the calculated purchase and sale price information to each household's terminal.

[0622] Step 4:

[0623] The device sends push notifications to the user, providing buying and selling price information.

[0624] Step 5:

[0625] The user checks the buying and selling price information on the terminal and decides whether to buy or sell electricity.

[0626] ---

[0627] 4. AI-based data collection and analysis

[0628] Step 1:

[0629] The terminal collects electricity usage data for each household in real time and sends it to a server.

[0630] Step 2:

[0631] The server stores the collected data in a database.

[0632] Step 3:

[0633] The server uses AI algorithms to analyze the data and identify energy consumption patterns.

[0634] Step 4:

[0635] The server generates energy-saving actions based on the analysis results and sends them to the terminal.

[0636] Step 5:

[0637] The device notifies the user of the analysis results and energy-saving actions.

[0638] Step 6:

[0639] The user checks the analysis results on the device and adjusts settings to save energy.

[0640] ---

[0641] The above are the specific steps and operation details of each process.

[0642] Example 1

[0643] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0644] Many households face the challenge of efficiently self-consuming surplus electricity generated by residential solar power generation systems, for which the Feed-in Tariff (FIT) scheme has expired. Furthermore, the high cost of purchasing and installing energy storage devices for individual households is another major obstacle. Furthermore, efficient power management and consumption across an entire region requires the prediction and optimization of power generation and consumption, and responses to power shortages and disasters are also required. Given this background, there is a demand for systems that enable the efficient purchase and installation of energy storage devices and power management.

[0645] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0646] In this invention, the server includes means for collecting energy storage device installation request information from households, means for ordering energy storage devices in bulk, means for reserving centralized installation locations for the energy storage devices, means for providing installation progress information, means for collecting data on power generation and consumption, means for predicting power generation and consumption based on the collected data, means for instructing optimal power usage, means for collecting data on power supply and demand, means for calculating buying and selling prices, means for providing calculated price information, means for collecting and analyzing power usage data, and means for providing analysis results. This makes it possible to efficiently install energy storage devices, reduce costs, and predict and optimize power generation and consumption, thereby realizing efficient power management and consumption throughout the entire region.

[0647] "Household" refers to an individual or group of occupants, a unit of living and consuming energy within a particular dwelling.

[0648] An "energy storage device" is a device for storing excess electricity generated by home solar power generation, and includes batteries and storage cells.

[0649] "Installation preference information" is data indicating the user's wishes and requests regarding the installation of an energy storage device, and specifically includes information such as the desired installation date, budget, and installation location.

[0650] "Bulk ordering means" refers to a method or process for placing bulk orders with suppliers based on the installation preference information for energy storage devices collected from multiple households.

[0651] "Aggregate installation site" refers to a concentrated installation area for multiple homes selected for efficient installation of energy storage devices.

[0652] "Installation progress information" refers to data regarding the progress of the installation work of the energy storage device, and is provided so that the user can check it.

[0653] "Electricity generation" refers to the amount of electricity generated by solar power generation systems, etc.

[0654] "Consumption" refers to the amount of electricity actually used by a household.

[0655] "Collected data" refers to various data obtained from households, such as electricity generation, consumption, and installation preferences.

[0656] "Forecasting means" refers to the method or process of forecasting future electricity generation and consumption based on collected data using machine learning algorithms, statistical models, etc.

[0657] "Means for instructing optimal power usage" refers to a method or process that provides specific instructions for optimizing power usage within a home based on the prediction results.

[0658] "Electricity demand" refers to the total amount of electricity required within a market or a specific region.

[0659] "Supply data" refers to data regarding the amount of electricity supplied from power plants, solar power generation systems, etc.

[0660] "Purchase price" refers to the transaction price for buying and selling electricity, which is determined based on supply and demand.

[0661] "Means of calculation" refers to the method or process for calculating the purchase and sale price of electricity.

[0662] "Means of providing" refers to the method or process for notifying the user of the necessary information or instructions.

[0663] "Electricity usage data" refers to data relating to individual electricity usage within a household or community.

[0664] "Analytical means" refers to the methods and processes used to analyze collected electricity usage data and identify patterns and trends.

[0665] The "analysis results" are the results of analyzing the power usage data, and include energy consumption patterns and recommendations for energy conservation.

[0666] This invention is a system for promoting the self-consumption of surplus electricity generated by residential solar power generation systems for which the feed-in tariff (FIT) system has expired, and for promoting preparations for power shortages and the creation of disaster-resistant towns. This system includes a means for collecting information on energy storage device installation requests from households and maximizing cost benefits through bulk orders, as well as a means for optimizing the flow of electricity using AI to achieve efficient electricity consumption within a community.

[0667] Bulk purchasing and centralized installation of energy storage devices

[0668] The server collects energy storage device installation preference information from each household. This process is carried out using a web or mobile application (e.g., React or Flutter) with data provided by each household via the internet. The collected installation preference information is stored in a database (e.g., MySQL or PostgreSQL). The server then uses the aggregated data to select the most suitable supplier and place a bulk order. This bulk order is more cost-effective than individual purchases by each household.

[0669] For example, if 100 households in a region wish to install energy storage devices, the server will consolidate this information and place a bulk order. The ordered energy storage devices will be installed at a centralized installation location in cooperation with the local government. Users can check the installation progress information in real time using their smartphones or PCs.

[0670] Optimizing power flow with AI

[0671] The server collects data on power generation and consumption from each household's solar power generation system and consumption devices. This data collection is done using IoT devices (e.g., smart meters) and communication protocols (e.g., MQTT). The server processes the collected data and predicts the next day's generation and consumption amounts based on machine learning algorithms (e.g., TensorFlow or PyTorch). Based on the prediction results, it creates an optimal energy storage plan and sends appropriate power usage instructions to each household.

[0672] For example, if the weather forecast for the next day is sunny, the server will predict a high amount of power generation and instruct the system to prioritize storing surplus power. Conversely, during times of high consumption, the system will instruct the system to supply power from the storage battery, achieving efficient power usage. Users can check the AI's instructions via their device and manually adjust the settings as needed.

[0673] Dynamic pricing of electricity prices using AI

[0674] The server collects local electricity demand and supply data in real time and calculates buying and selling prices using an AI algorithm. The algorithm used for this calculation is suitable for analyzing time-series data (for example, the ARIMA model or LSTM). The server then sends the calculated price information to the user's device via a notification system (for example, Firebase Cloud Messaging).

[0675] For example, during times when demand for electricity in a region is high and supply is insufficient, the server will send notifications to terminals encouraging users to sell electricity at a higher price. Conversely, during times of excess supply, the server will offer lower prices to encourage users to purchase electricity. Users can make buying and selling decisions based on this price information.

[0676] AI-based collection and analysis of electricity usage data

[0677] The server collects real-time electricity usage data from each household and analyzes it using AI algorithms. This analysis utilizes data stored in a database (e.g., MongoDB or SQLite). Based on the analysis results, energy consumption patterns and energy-saving recommendations are generated and provided to users through a notification system.

[0678] For example, if a particular household shows high power consumption during a particular time period, the server will detect that pattern and notify the device of specific actions to save energy (such as reducing power consumption at night). Users can then check their own energy consumption patterns and adjust their settings to further save energy.

[0679] Example prompts for generative AI models

[0680] "Please explain the design of an optimal power management system for self-consumption of surplus electricity from residential solar power generation. Specifically, please show the system's processing steps, including bulk purchasing and centralized installation of energy storage devices, optimization of power flow and dynamic pricing using AI, and energy conservation recommendations."

[0681] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0682] Step 1: Collect information on installation preferences from households

[0683] The user uses a smartphone or PC to input desired installation information for the energy storage device (desired installation date, budget, installation location, etc.). The input information is sent from the device to the server. The input is done using a web application or mobile application (e.g., React or Flutter). The server stores the received desired installation information in a database (e.g., MySQL or PostgreSQL). This allows the server to obtain the desired installation information for use in subsequent processing. Specifically, the user enters the information they wish to provide and presses the "Submit" button, which sends the information to the server.

[0684] input:

[0685] Installation information entered by the user (desired installation date, budget, installation location, etc.)

[0686] output:

[0687] Installation preference information stored in the database

[0688] Step 2: Place a bulk order based on your installation requirements

[0689] The server aggregates the installation preference information collected from households. Based on this information, the server selects the most suitable supplier. For example, a cost-based optimization algorithm is used for the selection. The server then places a bulk order with the supplier. Specifically, the server automatically generates order data and sends it to the supplier. This allows the server to aggregate the installation preferences of multiple households and place an order with the supplier, achieving cost-effectiveness.

[0690] input:

[0691] Installation preference information stored in the database

[0692] output:

[0693] Bulk order data from suppliers

[0694] Step 3: Provide installation progress information

[0695] The server periodically receives installation progress information from the supplier after receiving the order and stores it in a database. The terminal provides the installation progress information to the user in real time. Specifically, the progress information is notified via a smartphone or PC application. This allows the user to constantly check the installation status of the energy storage device.

[0696] input:

[0697] Installation progress information received from the supplier

[0698] output:

[0699] Notifying users of installation progress information

[0700] Step 4: Collecting electricity production and consumption data

[0701] The server collects data on electricity generation and consumption from each home's solar power generation system and consumption devices. This data is collected using IoT devices (e.g., smart meters) and communication protocols (e.g., MQTT). The server stores the collected data in a database. Specifically, the smart meters send data to the server in real time, and the server records it in the database.

[0702] input:

[0703] Electricity generation and consumption data received from smart meters

[0704] output:

[0705] Power generation and consumption data stored in a database

[0706] Step 5: Forecast production and consumption

[0707] The server uses machine learning algorithms (such as TensorFlow or PyTorch) to predict the next day's generation and consumption amounts based on the collected data on power generation and consumption. Specifically, the server inputs the data into the algorithm, generates predictions, and stores them in a database. This makes it easier to plan the next day's power usage.

[0708] input:

[0709] Power generation and consumption data stored in a database

[0710] output:

[0711] Machine learning algorithm predicts next day's production and consumption

[0712] Step 6: Delivering optimal power usage instructions

[0713] The server creates an optimal power usage plan based on the prediction results and notifies each household. The notification is sent to the device in real time using, for example, Firebase Cloud Messaging. Specifically, the server generates optimal power usage plan data and distributes it to the user's device via the notification system. This allows the user to receive instructions on efficient power usage.

[0714] input:

[0715] Production and consumption forecast results using machine learning algorithms

[0716] output:

[0717] Notification of optimal power usage instructions to each household

[0718] Step 7: Collect electricity demand and supply data

[0719] The server collects real-time electricity demand and supply data within the region. This data is also stored in a database and used for subsequent processing. Specifically, the server analyzes and stores data collected from electricity meters within the region.

[0720] input:

[0721] Power supply and demand data received from power meters

[0722] output:

[0723] Power demand and supply data stored in a database

[0724] Step 8: Calculate the purchase price

[0725] The server calculates the buying and selling prices using AI algorithms (such as LSTM or ARIMA models) based on the collected electricity demand and supply data. Specifically, the AI ​​algorithm analyzes the data and calculates the buying and selling prices in real time, making dynamic pricing possible.

[0726] input:

[0727] Power demand and supply data stored in a database

[0728] output:

[0729] Calculated purchase and sale price data

[0730] Step 9: Notification of purchase and sale price information

[0731] The server sends the calculated buying and selling price information to the user's device via a notification system. The user buys and sells electricity based on this information. Specifically, the server sends the price information to the device using a notification system such as Firebase Cloud Messaging.

[0732] input:

[0733] Calculated purchase and sale price data

[0734] output:

[0735] Notification of buying and selling price information to users

[0736] Step 10: Collect and analyze electricity usage data

[0737] The server collects electricity usage data from each household in real time and analyzes it using AI algorithms (e.g., k-means clustering and deep learning). The analysis results are stored in a database and provided to users. Specifically, the server analyzes the collected data, stores the results, and notifies them.

[0738] input:

[0739] Electricity usage data collected from households

[0740] output:

[0741] AI analysis results and notification data

[0742] (Application example 1)

[0743] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0744] It is becoming increasingly important to effectively utilize surplus electricity from residential solar power generation systems, promote self-consumption, and realize efficient electricity management across the entire region. In particular, it is necessary not only to efficiently utilize electricity within individual households, but also to provide economic benefits through the sharing and trading of surplus electricity. There is also a need to balance supply and demand through dynamic electricity pricing and provide attractive price information to consumers in real time. Therefore, it is necessary to provide a comprehensive system to solve these issues, which have been difficult to address with conventional systems.

[0745] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0746] In this invention, the server includes means for collecting storage battery installation request information from households, means for ordering storage batteries in bulk, means for reserving centralized storage battery installation locations, means for providing installation progress information, means for collecting data on power generation and consumption, means for predicting power generation and consumption based on the collected data, means for instructing optimal power usage, means for collecting data on power supply and demand, means for calculating buying and selling prices, means for providing dynamic price information based on the predicted data and notifying the user, means for storing surplus power in a digital wallet as power tokens, means for enabling the buying and selling of power tokens to other users, means for collecting and analyzing power usage data, and means for providing the analysis results. This makes it possible to effectively manage surplus power generated by home solar power generation and provide optimal power usage and economic benefits.

[0747] "Storage battery installation request information" is information collected from homes and facilities that wish to install power storage equipment.

[0748] A "centralized battery installation site" is a location where batteries are installed together in order to efficiently manage electricity throughout an entire region.

[0749] "Installation progress information" is information indicating how far the installation work of the storage battery has progressed.

[0750] An "electricity token" is a digital record of excess electricity generated by a home solar power system.

[0751] A "digital wallet" is an electronic wallet for managing tokens or currencies recorded in digital form.

[0752] "Dynamic price information" refers to price information that fluctuates in real time depending on the electricity supply and demand situation.

[0753] "Marketplace" means an electronic exchange for the buying and selling of energy tokens between users.

[0754] "Electricity usage data" is information on the amount of electricity consumed by each household or facility.

[0755] "Analysis results" are the results of analysis using AI and other technologies based on collected data.

[0756] "Predictive data" is data that predicts future electricity generation and consumption using AI algorithms.

[0757] This invention is a comprehensive energy management system aimed at utilizing surplus electricity generated by residential solar power generation, with a particular focus on digital wallet applications including energy tokens and dynamic pricing functionality.

[0758] The server collects battery installation requests from each household and places a bulk order for the batteries. It also secures centralized battery installation locations and provides installation progress information. It collects data on power generation and consumption, and uses AI algorithms to predict power generation and consumption based on this data. Specific software used includes machine learning libraries such as Scikit-learn. Based on the predictions, it instructs on optimal power usage and notifies households in real time.

[0759] Furthermore, data on electricity supply and demand is collected and buying and selling prices are dynamically calculated using AI algorithms. The calculated price information is provided to users, and dynamic price information is provided based on forecast data. This allows users to buy and sell energy tokens at the most advantageous price. Energy tokens are a digital record of surplus electricity and are stored in a digital wallet. Users can buy and sell energy tokens with other users on the buying and selling marketplace using their own devices (smartphones, tablets, etc.).

[0760] For example, if a household generates 10 kWh and consumes 5 kWh in a day, the difference of 5 kWh is stored in the digital wallet as electricity tokens. Then, the AI ​​predicts whether the weather will be sunny the next day and notifies the user in real time of the times when electricity prices will be high. In this way, users can efficiently buy and sell surplus electricity.

[0761] The server also constantly collects power usage data and provides the analysis results to the user. Based on the analysis results, the user can understand power consumption patterns and take power-saving measures as necessary, thereby achieving further energy savings.

[0762] Prompt Sentence Examples

[0763] "Please create an example implementation of a system that manages surplus electricity generated by a home solar power generation system as electricity tokens and notifies prices in real time via a smartphone app. Please also add a marketplace function that allows users to buy and sell electricity tokens."

[0764] In this way, the present invention provides a comprehensive energy management system that covers all processes from battery installation to energy token management and dynamic pricing notification.

[0765] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0766] Step 1:

[0767] The server collects information from each household regarding their preference for battery installation.

[0768] Input: Battery installation request information from households

[0769] Data processing and calculation: The collected information is stored in a database, and the number of people who wish to install the device and the desired installation date are compiled.

[0770] Output: List of battery installation applicants

[0771] Step 2:

[0772] The server orders storage batteries in bulk based on the installation preference information collected.

[0773] Input: List of people wanting to install a storage battery

[0774] Data processing and data calculation: Supplier selection and bulk order optimization calculation.

[0775] Output: Order data for the best supplier

[0776] Step 3:

[0777] The server secures a centralized installation location for the storage batteries and provides installation progress information.

[0778] Input: Bulk order data, installation request list

[0779] Data processing and data calculation: Select the installation location and manage the installation schedule.

[0780] Output: Update installation progress information

[0781] Step 4:

[0782] The server collects data on the amount of electricity generated and consumed by each household.

[0783] Input: Power generation and consumption data for each household

[0784] Data processing and calculation: The amount of power generated and consumed is calculated based on the collected data.

[0785] Output: Data set of daily and hourly energy production and consumption

[0786] Step 5:

[0787] The server uses AI algorithms to predict power generation and consumption based on the collected data.

[0788] Input: Dataset of daily and hourly energy production and consumption

[0789] Data processing and data calculations: Machine learning models are used to predict the next day's electricity generation and consumption.

[0790] Output: Forecast data

[0791] Step 6:

[0792] The server instructs on optimal power usage and notifies households in real time.

[0793] Input: Forecast data

[0794] Data processing and calculation: Generate an optimal power usage plan and notify each household.

[0795] Output: Power usage instruction

[0796] Step 7:

[0797] The server collects data on electricity demand and supply and dynamically calculates buying and selling prices.

[0798] Input: Demand data, Supply data

[0799] Data processing and calculation: Calculate appropriate electricity prices using AI algorithms.

[0800] Output: Dynamic price information

[0801] Step 8:

[0802] The server provides and notifies the user of dynamic pricing information based on the forecast data.

[0803] Input: Dynamic Price Information

[0804] Data processing and data calculation: price information is generated and sent to the user terminal.

[0805] Output: Price notification to user

[0806] Step 9:

[0807] The server stores the surplus electricity in a digital wallet as electricity tokens.

[0808] Input: Surplus power data

[0809] Data processing and data calculation: Surplus electricity is tokenized and stored in a digital wallet.

[0810] Output: Power tokens

[0811] Step 10:

[0812] The server allows users to buy and sell energy tokens to other users and transacts them on the marketplace.

[0813] Input: Power tokens, desired trading information

[0814] Data processing and data calculation: Matching token purchases and sales and updating transaction records.

[0815] Output: Transaction completion notification

[0816] Step 11:

[0817] The server collects power usage data and provides the analysis results to the user.

[0818] Input: Power usage data

[0819] Data processing and data calculation: Perform data analysis and generate energy-saving recommendations.

[0820] Output: Notification of analysis results

[0821] A sample prompt is, "Please create an implementation example of a system that manages surplus electricity generated by a home solar power generation system as electricity tokens and notifies prices in real time via a smartphone app. Please also add a marketplace function that allows users to buy and sell electricity tokens."

[0822] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0823] ---

[0824] This invention is a system that promotes the self-consumption of surplus electricity generated by residential solar power generation systems for which the feed-in tariff (FIT) system has expired, and promotes preparations for power shortages and the creation of disaster-resistant towns. This system includes a means for collecting information on battery installation requests from households and maximizing cost benefits through bulk orders. It also utilizes AI to optimize the flow of electricity and achieve efficient power consumption within the region. Furthermore, it integrates an emotion engine that recognizes the user's emotions and suggests optimal energy-saving actions based on the user's energy consumption patterns.

[0825] 1. Bulk purchase and centralized installation of storage batteries

[0826] The server collects battery installation request information from each household. This information is then stored in a database, along with the number of people wanting to install a battery and the desired installation time. The server then selects the most suitable supplier based on the aggregated data and places a bulk order. This is more cost-effective than purchasing batteries individually.

[0827] For example, if 100 households in a region request the installation of storage batteries, the server will compile this information and place a bulk order. The obtained storage batteries will be installed at a centralized installation location in cooperation with the local government. Users can check the installation progress information in real time using their smartphones or PCs.

[0828] 2. Optimizing power flow using AI

[0829] The server collects data on power generation and consumption from each home's solar power generation system and power consumption devices. Based on this data, the server uses an AI algorithm to predict the next day's power generation and consumption. Based on the prediction results, the server creates an optimal power storage plan and sends appropriate power usage instructions to each household.

[0830] For example, if the weather forecast for the next day is sunny, the server will predict high power generation and instruct the system to prioritize storing surplus power. Conversely, during times of high consumption, the system will instruct the system to supply power from the storage battery, achieving efficient power usage. Users can check the AI's instructions via their device and manually adjust the settings as needed.

[0831] 3. Dynamic pricing of electricity using AI

[0832] The server collects local electricity demand and supply data in real time and calculates the buying and selling price using an AI algorithm. Based on this calculation, the device notifies the user of dynamic price information. This allows users to buy and sell electricity at a more advantageous price than under a contract with the power company.

[0833] For example, during times when demand for electricity in a region is high and supply is insufficient, the server will send notifications to terminals encouraging users to sell electricity at a higher price. Conversely, during times of excess supply, the server will offer lower prices to encourage users to buy electricity. Users can make buying and selling decisions based on this price information.

[0834] 4. AI-based data collection and analysis

[0835] The server collects real-time electricity usage data from each household and analyzes it using AI algorithms. Based on the results, recommendations for energy consumption patterns and energy conservation are generated and provided to users via their devices.

[0836] For example, if a household shows high power consumption during a certain time period, the server will detect this pattern and notify the device of specific actions to save energy (such as reducing power use at night). Users can then check their own energy consumption patterns and adjust their settings to achieve further energy savings.

[0837] 5. Integration of emotion engine that recognizes user emotions

[0838] The emotion engine collects emotional data from the user's facial expressions and voice and sends it to the server, which then analyzes the user's emotional state and identifies factors that affect power usage patterns.

[0839] For example, if the device detects that the user is feeling stressed, the server can use that data to adjust energy-saving behaviors, such as suggesting actions that help with relaxation (adjusting lighting, setting the air conditioner temperature, playing music, etc.), thereby improving the user's comfort.

[0840] 6. Adjusting energy-saving actions based on emotion data

[0841] The server combines the collected emotional data with power consumption data to generate optimal energy-saving actions, enabling flexible energy management according to the user's emotional state.

[0842] For example, when the user is relaxing, the server will prioritize actions to reduce power consumption. Conversely, when the user is energetic and active, the server will suggest settings that prioritize comfort. The user can easily adjust the energy-saving settings based on this.

[0843] By combining user emotion recognition, the system of the present invention offers greater flexibility and user experience than conventional energy-saving systems, thereby improving not only energy efficiency but also the user's quality of life.

[0844] The processing flow will be explained below.

[0845] ---

[0846] 1. Bulk purchase and centralized installation of storage batteries

[0847] Step 1:

[0848] The user inputs the desired battery installation information into the terminal, which then sends the information to the server.

[0849] Step 2:

[0850] The server stores and aggregates battery installation request information in a database.

[0851] Step 3:

[0852] The server selects the most suitable supplier based on the aggregated data and places a bulk order.

[0853] Step 4:

[0854] The server will work with local governments to secure a centralized installation location.

[0855] Step 5:

[0856] The server updates installation progress information in real time and notifies the user via the terminal.

[0857] ---

[0858] 2. Optimizing power flow using AI

[0859] Step 1:

[0860] The device measures the amount of electricity generated and consumed within the home in real time and sends the data to a server.

[0861] Step 2:

[0862] The server uses the collected data to run an AI algorithm and predict the next day's power generation and consumption.

[0863] Step 3:

[0864] Based on the prediction results, the server generates an optimal energy storage plan and instructions for power usage for each household.

[0865] Step 4:

[0866] The server generates instructions and distributes them to each household's terminal.

[0867] Step 5:

[0868] The user checks the instructions received through the device and manually adjusts the settings if necessary.

[0869] ---

[0870] 3. Dynamic pricing of electricity using AI

[0871] Step 1:

[0872] The server collects real-time data on electricity demand and supply within the region.

[0873] Step 2:

[0874] The server uses an AI algorithm to analyze the collected data and calculate the optimal buying and selling price.

[0875] Step 3:

[0876] The server sends the calculated purchase and sale price information to each household's terminal.

[0877] Step 4:

[0878] The device sends push notifications to the user, providing buying and selling price information.

[0879] Step 5:

[0880] The user checks the buying and selling price information on the terminal and decides whether to buy or sell electricity.

[0881] ---

[0882] 4. AI-based data collection and analysis

[0883] Step 1:

[0884] The terminal collects electricity usage data for each household in real time and sends it to a server.

[0885] Step 2:

[0886] The server stores the collected data in a database.

[0887] Step 3:

[0888] The server uses AI algorithms to analyze the data and identify energy consumption patterns.

[0889] Step 4:

[0890] The server generates energy-saving actions based on the analysis results and sends them to the terminal.

[0891] Step 5:

[0892] The device notifies the user of the analysis results and energy-saving actions.

[0893] Step 6:

[0894] The user checks the analysis results on the device and adjusts settings to save energy.

[0895] ---

[0896] 5. Integration of emotion engine that recognizes user emotions

[0897] Step 1:

[0898] The device analyzes the user's facial expressions and voice in real time to generate emotional data.

[0899] Step 2:

[0900] The device transmits the emotion data to the server.

[0901] Step 3:

[0902] The server analyzes the user's emotional state based on the emotional data.

[0903] Step 4:

[0904] The server combines emotional state and power consumption data to identify factors that influence energy consumption patterns.

[0905] ---

[0906] 6. Adjusting energy-saving actions based on emotion data

[0907] Step 1:

[0908] The server analyzes the emotion data and power consumption data and generates energy-saving actions.

[0909] Step 2:

[0910] The server sends energy-saving actions to each household's terminal.

[0911] Step 3:

[0912] The terminal notifies the user of energy-saving actions based on the emotion data.

[0913] Step 4:

[0914] The user checks the proposed energy-saving action on the terminal and decides whether to execute it.

[0915] ---

[0916] The above are the specific processing steps and operation details of the invention combined with the emotion engine.

[0917] Example 2

[0918] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0919] With the end of the Feed-in Tariff (FIT) system in modern society, there is a need to promote the self-consumption of surplus electricity from residential solar power generation while also preparing for power shortages and creating disaster-resistant communities. However, purchasing and installing energy storage devices individually for each household poses challenges, including high costs and the hassle of installation. There are also concerns about reduced efficiency due to suboptimal power usage patterns. Furthermore, there are no systems that take into account the influence of user emotions on power consumption behavior.

[0920] The specification process by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for collecting information on desired installation of household power storage devices, a means for ordering power storage devices in bulk, a means for reserving centralized installation locations for the power storage devices, a means for providing installation progress information, a means for collecting data on power generation and consumption, a means for predicting power generation and consumption based on the collected data, a means for instructing optimal power usage, a means for collecting data on power supply and demand, a means for calculating buying and selling prices, a means for providing calculated price information, a means for collecting and analyzing power usage data, a means for providing the analysis results, a means for collecting emotion data and analyzing factors affecting power usage patterns, and a means for generating and providing energy-saving actions based on the user's emotional state. This reduces the high cost and installation effort for each household, enables optimization of power usage patterns, and efficient energy management. Furthermore, flexible energy-saving actions based on the user's emotions can improve the user experience and quality of life.

[0921] A "home energy storage device" is a device with an energy storage function that is installed in a home and stores surplus electricity generated by solar power generation and other means, and supplies it when needed.

[0922] The "installation preference information" is information indicating that a household wishes to install a power storage device, and includes details such as the number of applicants and the desired installation time.

[0923] "Bulk ordering" refers to ordering multiple household energy storage devices at once, and is an ordering method that offers cost benefits over ordering individually.

[0924] "Concentrated installation site" means a specific location set aside temporarily or permanently for the efficient installation of energy storage devices.

[0925] "Installation progress information" is information indicating whether the installation of the power storage device is proceeding as planned, and is information that provides the progress status of the installation process in real time.

[0926] "Power generation amount" is data indicating the amount of power generated by the photovoltaic power generation system during a certain time period.

[0927] "Consumption" is data indicating the amount of electricity used within the home.

[0928] "Collected Data" refers to various data measured and acquired by the system, such as the amount of electricity generated and consumed.

[0929] "Forecasting" means estimating future power generation and consumption based on collected data.

[0930] "Directing power usage" means proposing and delivering optimal power usage methods to each household based on collected data and predictions.

[0931] "Dynamic pricing" is a pricing method that changes electricity prices based on real-time conditions of electricity supply and demand.

[0932] "Price information" refers to information including pricing and calculation results related to the purchase and sale of electricity.

[0933] "Electricity usage data" refers to historical information on the amount of electricity actually used within a household.

[0934] "Analysis" means the process of analyzing collected data and converting the information into a useful form.

[0935] "Emotion data" is data that indicates the emotional state of the user as determined by their facial expressions and voice.

[0936] "Energy saving actions" refer to specific actions or measures that should be taken to reduce energy consumption.

[0937] "User status" is information that indicates the user's current emotions and behavioral status.

[0938] The above definitions allow a clear understanding of each element of the invention and its function.

[0939] This invention relates to a system that improves the efficiency of home energy storage device installation and power management. This system collects information on energy storage device installation requests from households, reduces costs through bulk ordering, and uses AI to predict and optimize power consumption. It also collects user emotional data and suggests energy-saving actions based on emotions, improving the user experience.

[0940] Hardware and Software

[0941] server:

[0942] Database (storage of information on desired installation of power storage devices, and data on power generation and consumption)

[0943] AI algorithms (power forecasting, dynamic pricing, sentiment analysis)

[0944] Communication module (transmits and receives data from home devices)

[0945] Terminals (home devices):

[0946] Smartphone, tablet, PC (for entering and checking information)

[0947] Smart meters (for collecting data on electricity generation and consumption)

[0948] Emotion engine (for collecting emotion data)

[0949] Basic operation

[0950] Collection of information on home energy storage device installation requests:

[0951] Users enter their desired installation information for the storage device via a dedicated application on their smartphone or PC and send it to the server.

[0952] Supplier selection and bulk ordering:

[0953] Based on the installation information collected by the server, the system selects the most suitable supplier and places a bulk order for the energy storage devices, reducing costs compared to purchasing them individually.

[0954] Electricity generation and consumption data collection:

[0955] It collects electricity data in real time from smart meters and energy management systems (EMS) in homes and sends it to a server.

[0956] Prediction and optimization:

[0957] The server uses AI algorithms to analyze the collected data and predict the next day's electricity generation and consumption, and based on this, generates and presents an optimal electricity usage plan for each household.

[0958] Dynamic Pricing:

[0959] The server uses an AI algorithm to calculate the electricity buying and selling price based on real-time data on electricity demand and supply. The calculated price information is sent to the terminal, and the user can decide whether to buy or sell electricity.

[0960] Collecting emotional data and suggesting energy-saving actions:

[0961] The device uses an emotion engine to collect emotional data from the user's facial expressions and voice, and sends it to the server. The server analyzes the emotional data and power usage patterns, and generates and suggests energy-saving actions based on the user's emotional state.

[0962] Specific examples

[0963] If 100 households in a region wish to install a power storage device, each household can enter their information through a dedicated app. The server compiles this information and places a bulk order with significant cost benefits. Based on the power generation and consumption data collected from each household's smart meter, the system calculates the optimal power usage plan for the next day and notifies the smartphone or PC. Also, if the weather forecast predicts clear skies, the system will prioritize storing surplus power, thereby achieving efficient energy use. Furthermore, when the user is feeling stressed, the system will suggest actions to help them relax (such as adjusting the lighting or playing music).

[0964] Prompt Sentence Examples

[0965] Please explain the specific implementation procedures and effects of an energy management system that combines residential solar power generation and storage batteries, as shown below.

[0966] A system that collects battery installation request information from each household on a server and places a bulk order

[0967] A function that uses AI to predict power generation and consumption and provide optimal power usage plans

[0968] Dynamic pricing calculates electricity prices in real time and notifies users

[0969] A function that recognizes the user's emotions and suggests optimal energy-saving actions based on those emotions.

[0970] With the above configuration, the system of the present invention can improve the efficiency of installation and power management of home power storage devices, and can improve the energy consumption and quality of life of users.

[0971] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0972] Step 1:

[0973] Collecting information on home energy storage device installation requests

[0974] Input: The user enters desired information about the installation of the energy storage device (such as name, address, and desired installation date) through a dedicated application.

[0975] How it works: User input information is sent from the smartphone or PC to the server.

[0976] Output: The server stores these preferences in a database.

[0977] Step 2:

[0978] Supplier selection and bulk ordering

[0979] Input: The server compiles the storage battery installation request information stored in the database.

[0980] How it works: The server compares the prices, delivery times, and performance data of each supplier based on the aggregated data, selects the best supplier, and places a bulk order.

[0981] Output: A purchase order is placed with the best supplier and the required number of storage units is determined.

[0982] Step 3:

[0983] Collecting data on electricity generation and consumption

[0984] Input: Real-time electricity generation and consumption data collected from home smart meters and energy management systems.

[0985] How it works: The devices send this data to a server, which stores the data collected from all homes in a database.

[0986] Output: The electricity generation and consumption data stored on the server is updated.

[0987] Step 4:

[0988] Forecasting and optimizing power generation and consumption

[0989] Input: Historical electricity generation and consumption data stored on the server, along with related information such as weather forecasts.

[0990] How it works: The server uses AI algorithms to predict the next day's electricity generation and consumption. Based on this, the server generates an optimal electricity usage plan (when to store electricity and when to use it).

[0991] Output: A specific electricity usage plan for each household is generated and sent to the terminal.

[0992] Step 5:

[0993] Dynamic pricing calculation and notification

[0994] Input: Local electricity demand and supply data collected in real time by the server.

[0995] How it works: The server uses an AI algorithm to calculate the optimal price for selling and purchasing electricity.

[0996] Output: The calculated purchase and sale price information is sent to the home device.

[0997] Step 6:

[0998] Energy consumption data collection and analysis

[0999] Input: Electricity usage data collected from households.

[1000] How it works: The server analyzes this data using AI algorithms to generate energy consumption patterns and energy-saving recommendations.

[1001] Output: The generated recommendations are sent to the home device and notified to the user.

[1002] Step 7:

[1003] Collecting emotional data and suggesting energy-saving actions

[1004] Input: Emotional data such as facial expressions and voice of the user collected by the device.

[1005] How it works: The device sends this data to the server, which then analyzes it using an emotion engine. Based on the analysis, it generates energy-saving actions that correspond to the user's emotional state.

[1006] Output: The proposed energy saving actions are notified to the home device.

[1007] Through these processing steps, the system can integrate and manage the efficient deployment of energy storage devices, the optimization of power usage patterns, dynamic pricing, and emotion-based energy-saving behavior, thereby improving users' energy consumption and quality of life.

[1008] (Application example 2)

[1009] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1010] Efficient installation and operation of storage batteries is important to promote self-consumption of surplus electricity from residential solar power generation systems. However, purchasing and installing individual storage batteries is costly, and an advanced management system is required to efficiently use and optimally buy and sell electricity. Furthermore, flexible energy management that takes into account the emotional state of the user is required to balance power consumption with user comfort. A comprehensive system is needed to solve these problems.

[1011] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting storage battery installation preference information from households, means for ordering storage batteries in bulk, means for reserving centralized storage battery installation locations, means for providing installation progress information, means for collecting data on power generation and consumption, means for predicting power generation and consumption based on the collected data, means for instructing optimal power usage, means for collecting data on power supply and demand, means for calculating buying and selling prices, means for providing calculated price information, means for collecting and analyzing power usage data, means for providing the analysis results, means for collecting emotion data, means for adjusting settings based on the emotion data, and means for optimizing lighting and air conditioning settings based on the emotion data. This enables cost reduction through efficient installation and bulk purchase of storage batteries, and further realizes optimal power usage using AI and comfortable energy management based on the user's emotional state.

[1012] "Means for collecting information on battery installation preferences from households" refers to devices or systems that collect information on each household's desire to install a battery.

[1013] "Means for bulk ordering of batteries" refers to a method or system for ordering a large number of batteries at once.

[1014] "Means for securing locations for centralized installation of storage batteries" refers to methods or systems for centralizing and securing suitable locations for installing storage batteries.

[1015] "Means for providing installation progress information" refers to a device or system that provides information about the installation progress of a storage battery.

[1016] "Means for collecting data on electricity generation and consumption" refers to devices or systems that collect data on electricity generation and consumption.

[1017] "Means for predicting power generation and consumption based on collected data" refers to a method or system for predicting future power generation and consumption based on collected data.

[1018] "Means for instructing optimal power usage" refers to devices or systems that issue instructions for efficient power usage based on predictive data.

[1019] "Means for collecting electricity demand and supply data" refers to devices and systems that collect information on electricity demand and supply.

[1020] "Means for calculating purchase and sale prices" refers to methods and systems for calculating the purchase and sale prices of electricity based on collected data.

[1021] "Means for providing calculated price information" refers to a device or system that provides calculated price information to a user.

[1022] "Means for collecting and analyzing electricity usage data" refers to devices and systems that collect and analyze data related to electricity usage.

[1023] "Means for providing analysis results" refers to a device or system that provides the results of analyzed data to the user.

[1024] "Means for collecting emotional data" refers to a device or system that collects data about a user's emotional state.

[1025] "Means for adjusting settings based on emotional data" refers to a method or system for adjusting various system settings based on collected emotional data.

[1026] "Means for optimizing lighting and air conditioning settings based on emotional data" refers to devices and systems that optimize lighting and air conditioning settings based on emotional data.

[1027] This invention is a comprehensive system for promoting self-consumption of surplus electricity from a home solar power generation system or for efficient power management in a physical store. The system includes an emotion engine for optimizing lighting and air conditioning based on the user's emotional state.

[1028] System Configuration

[1029] The system includes several means:

[1030] 1. A means of collecting information on households' desire to install storage batteries

[1031] 2. A way to order batteries in bulk

[1032] 3. Measures to secure locations for centralized battery installation

[1033] 4. Means of providing installation progress information

[1034] 5. Means of collecting data on electricity generation and consumption

[1035] 6. A means of forecasting power generation and consumption based on collected data

[1036] 7. Means of directing optimal power usage

[1037] 8. Means of collecting electricity demand and supply data

[1038] 9. Means of calculating the purchase and sale price

[1039] 10. Means of Providing Calculated Pricing Information

[1040] 11. Means of collecting and analyzing electricity usage data

[1041] 12. Means of Providing Analysis Results

[1042] 13. Means of collecting emotional data

[1043] 14. A way to adjust settings based on emotional data

[1044] 15. A way to optimize lighting and air conditioning settings based on emotional data

[1045] What the program does

[1046] The server collects information on each household's battery installation preferences and places a bulk order, reducing installation costs. The server also secures centralized installation locations for the batteries and provides real-time updates on the installation progress.

[1047] The server collects data on each household's electricity generation and consumption and uses AI to predict future power generation and consumption. This allows it to create an optimal power usage plan and provide instructions to the user. It also collects data on electricity demand and supply and calculates electricity buying and selling prices in real time. The server then provides this price information to the user's device.

[1048] The emotion engine collects emotional data from the user's facial expressions and voice. The server analyzes the emotional data and adjusts lighting and air conditioning settings based on the user's emotional state, thereby improving the user's comfort.

[1049] Specific examples

[1050] For example, by using this system in a physical store, the following effects can be expected. First, the AI ​​predicts times when electricity usage will be high in the store and performs optimal energy management. Furthermore, the emotion engine recognizes customer emotions in real time, and if a customer appears stressed, it will dim the lights and adjust the air conditioning to provide a comfortable environment. These settings can be performed automatically by the server, eliminating the need for users to do anything.

[1051] Prompt Sentence Examples

[1052] An example of a prompt to be input to a generative AI model might be:

[1053] "Apply a system for managing excess electricity from home solar power generation systems to a physical store. Use AI to predict electricity usage and propose programs to optimize lighting and air conditioning settings based on the emotions of staff and customers."

[1054] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1055] Step 1:

[1056] The server collects battery installation preference information from households. The installation preference information (e.g., desired installation time, battery size, etc.) is sent from each household's terminal and stored as data. This allows the overall demand for battery installation to be grasped.

[1057] Step 2:

[1058] The server orders batteries in bulk. Based on the collected installation information, it generates and sends bulk ordering data to suppliers. Bulk orders can reduce costs.

[1059] Step 3:

[1060] The server secures a centralized installation location for the storage batteries. After the ordered storage batteries arrive, it aggregates and adjusts the installation locations for each household and secures a centralized installation location. It analyzes the geographical information from each household and generates a list of optimal installation locations.

[1061] Step 4:

[1062] The server provides installation progress information. It monitors the progress of the battery storage installation in real time and sends that information to the home device. The progress information includes the installation completion date and time and ongoing status data.

[1063] Step 5:

[1064] The server collects data on the amount of electricity generated and consumed. This data is input in real time from each household's solar power generation system and consumption devices, and the data is stored and managed.

[1065] Step 6:

[1066] The server predicts power generation and consumption amounts based on the collected data. The collected power generation and consumption data is input into an AI algorithm to predict power generation and consumption amounts for the next day. An energy management plan is generated based on the predicted data.

[1067] Step 7:

[1068] The server then instructs the optimal use of electricity. Based on the predicted power generation and consumption, it generates power usage instructions for each household and sends them to the terminal. Specifically, this includes battery charging and discharging schedules and timings for efficient power usage.

[1069] Step 8:

[1070] The server collects and stores data on electricity demand and supply. This data is input in real time and includes supply data from the power supply company and demand data from users.

[1071] Step 9:

[1072] The server calculates the buying and selling prices. Based on the collected electricity demand and supply data, an AI algorithm calculates the optimal buying and selling price. The price calculation algorithm takes into account market trends and seasonal fluctuations.

[1073] Step 10:

[1074] The server provides the calculated price information. The calculated purchase and sale price information is sent to a home terminal and notified to the user. The user receives the notification and makes a decision to execute the purchase and sale as necessary.

[1075] Step 11:

[1076] The server collects and analyzes real-time power usage data sent from home devices and analyzes it using AI algorithms to identify power usage patterns and generate recommendations for improving energy efficiency.

[1077] Step 12:

[1078] The server provides the analysis results. The analyzed power usage data is sent to the home device, where specific suggestions (e.g., recommended usage times for high-efficiency devices) are presented to promote the optimization of energy consumption.

[1079] Step 13:

[1080] The server collects emotion data by receiving the user's facial expressions and voice as analysis results from the emotion engine connected to the home device.

[1081] Step 14:

[1082] The server adjusts settings based on the emotion data. Based on the emotion data, it generates settings (e.g., lighting and air conditioning adjustments) to improve the user's comfort and sends them to the home device.

[1083] Step 15:

[1084] The server optimizes lighting and air conditioning settings based on the emotional data. It analyzes the emotional data and applies the most appropriate lighting and air conditioning settings to the home devices. By automatically reflecting the settings that the user finds most comfortable, it is possible to achieve both energy efficiency and comfort.

[1085] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1087] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1088] [Third embodiment]

[1089] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1090] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1091] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1092] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1093] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1094] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1095] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1096] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1097] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1098] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1099] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1100] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1101] ---

[1102] This invention is a system that promotes the self-consumption of surplus electricity generated by residential solar power generation systems for which the feed-in tariff (FIT) system has expired, and promotes preparation for power shortages and disaster-resistant urban development. This system includes a means for collecting information on battery installation requests from households and maximizing cost benefits through bulk orders. It also utilizes AI to optimize the flow of electricity and achieve efficient power consumption within the community.

[1103] 1. Bulk purchase and centralized installation of storage batteries

[1104] The server collects battery installation request information from each household. This information is then stored in a database, along with the number of people wanting to install a battery and the desired installation time. The server then selects the most suitable supplier based on the aggregated data and places a bulk order. This is more cost-effective than purchasing batteries individually.

[1105] For example, if 100 households in a region request the installation of storage batteries, the server will compile this information and place a bulk order. The obtained storage batteries will be installed at a centralized installation location in cooperation with the local government. Users can check the installation progress information in real time using their smartphones or PCs.

[1106] 2. Optimizing power flow using AI

[1107] The server collects data on power generation and consumption from each home's solar power generation system and power consumption devices. Based on this data, the server uses an AI algorithm to predict the next day's power generation and consumption. Based on the prediction results, the server creates an optimal power storage plan and sends appropriate power usage instructions to each household.

[1108] For example, if the weather forecast for the next day is sunny, the server will predict high power generation and instruct the system to prioritize storing surplus power. Conversely, during times of high consumption, the system will instruct the system to supply power from the storage battery, achieving efficient power usage. Users can check the AI's instructions via their device and manually adjust the settings as needed.

[1109] 3. Dynamic pricing of electricity using AI

[1110] The server collects local electricity demand and supply data in real time and calculates the buying and selling price using an AI algorithm. Based on this calculation, the device notifies the user of dynamic price information. This allows users to buy and sell electricity at a more advantageous price than under a contract with the power company.

[1111] For example, during times when demand for electricity in a region is high and supply is insufficient, the server will send notifications to terminals encouraging users to sell electricity at a higher price. Conversely, during times of excess supply, the server will offer lower prices to encourage users to buy electricity. Users can make buying and selling decisions based on this price information.

[1112] 4. AI-based data collection and analysis

[1113] The server collects real-time electricity usage data from each household and analyzes it using AI algorithms. Based on the results, recommendations for energy consumption patterns and energy conservation are generated and provided to users via their devices.

[1114] For example, if a household shows high power consumption during a certain time period, the server will detect this pattern and notify the device of specific actions to save energy (such as reducing power use at night). Users can then check their own energy consumption patterns and adjust their settings to achieve further energy savings.

[1115] In this way, the system of the present invention utilizes AI at each stage to achieve efficient power management and consumption, improving energy efficiency at the household level while also realizing sustainable energy management across the entire region.

[1116] The processing flow will be explained below.

[1117] ---

[1118] 1. Bulk purchase and centralized installation of storage batteries

[1119] Step 1:

[1120] The user inputs the desired battery installation information into the terminal, which then sends the information to the server.

[1121] Step 2:

[1122] The server stores and aggregates battery installation request information in a database.

[1123] Step 3:

[1124] The server selects the most suitable supplier based on the aggregated data and places a bulk order.

[1125] Step 4:

[1126] The server will work with local governments to secure a centralized installation location.

[1127] Step 5:

[1128] The server updates installation progress information in real time and notifies the user via the terminal.

[1129] ---

[1130] 2. Optimizing power flow using AI

[1131] Step 1:

[1132] The device measures the amount of electricity generated and consumed within the home in real time and sends the data to a server.

[1133] Step 2:

[1134] The server uses the collected data to run an AI algorithm and predict the next day's power generation and consumption.

[1135] Step 3:

[1136] Based on the prediction results, the server generates an optimal energy storage plan and instructions for power usage for each household.

[1137] Step 4:

[1138] The server generates instructions and distributes them to each household's terminal.

[1139] Step 5:

[1140] The user checks the instructions received through the device and manually adjusts the settings if necessary.

[1141] ---

[1142] 3. Dynamic pricing of electricity using AI

[1143] Step 1:

[1144] The server collects real-time data on electricity demand and supply within the region.

[1145] Step 2:

[1146] The server uses an AI algorithm to analyze the collected data and calculate the optimal buying and selling price.

[1147] Step 3:

[1148] The server sends the calculated purchase and sale price information to each household's terminal.

[1149] Step 4:

[1150] The device sends push notifications to the user, providing buying and selling price information.

[1151] Step 5:

[1152] The user checks the buying and selling price information on the terminal and decides whether to buy or sell electricity.

[1153] ---

[1154] 4. AI-based data collection and analysis

[1155] Step 1:

[1156] The terminal collects electricity usage data for each household in real time and sends it to a server.

[1157] Step 2:

[1158] The server stores the collected data in a database.

[1159] Step 3:

[1160] The server uses AI algorithms to analyze the data and identify energy consumption patterns.

[1161] Step 4:

[1162] The server generates energy-saving actions based on the analysis results and sends them to the terminal.

[1163] Step 5:

[1164] The device notifies the user of the analysis results and energy-saving actions.

[1165] Step 6:

[1166] The user checks the analysis results on the device and adjusts settings to save energy.

[1167] ---

[1168] The above are the specific steps and operation details of each process.

[1169] Example 1

[1170] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1171] Many households face the challenge of efficiently self-consuming surplus electricity generated by residential solar power generation systems, for which the Feed-in Tariff (FIT) scheme has expired. Furthermore, the high cost of purchasing and installing energy storage devices for individual households is another major obstacle. Furthermore, efficient power management and consumption across an entire region requires the prediction and optimization of power generation and consumption, and responses to power shortages and disasters are also required. Given this background, there is a demand for systems that enable the efficient purchase and installation of energy storage devices and power management.

[1172] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1173] In this invention, the server includes means for collecting energy storage device installation request information from households, means for ordering energy storage devices in bulk, means for reserving centralized installation locations for the energy storage devices, means for providing installation progress information, means for collecting data on power generation and consumption, means for predicting power generation and consumption based on the collected data, means for instructing optimal power usage, means for collecting data on power supply and demand, means for calculating buying and selling prices, means for providing calculated price information, means for collecting and analyzing power usage data, and means for providing analysis results. This makes it possible to efficiently install energy storage devices, reduce costs, and predict and optimize power generation and consumption, thereby realizing efficient power management and consumption throughout the entire region.

[1174] "Household" refers to an individual or group of occupants, a unit of living and consuming energy within a particular dwelling.

[1175] An "energy storage device" is a device for storing excess electricity generated by home solar power generation, and includes batteries and storage cells.

[1176] "Installation preference information" is data indicating the user's wishes and requests regarding the installation of an energy storage device, and specifically includes information such as the desired installation date, budget, and installation location.

[1177] "Bulk ordering means" refers to a method or process for placing bulk orders with suppliers based on the installation preference information for energy storage devices collected from multiple households.

[1178] "Aggregate installation site" refers to a concentrated installation area for multiple homes selected for efficient installation of energy storage devices.

[1179] "Installation progress information" refers to data regarding the progress of the installation work of the energy storage device, and is provided so that the user can check it.

[1180] "Electricity generation" refers to the amount of electricity generated by solar power generation systems, etc.

[1181] "Consumption" refers to the amount of electricity actually used by a household.

[1182] "Collected data" refers to various data obtained from households, such as electricity generation, consumption, and installation preferences.

[1183] "Forecasting means" refers to the method or process of forecasting future electricity generation and consumption based on collected data using machine learning algorithms, statistical models, etc.

[1184] "Means for instructing optimal power usage" refers to a method or process that provides specific instructions for optimizing power usage within a home based on the prediction results.

[1185] "Electricity demand" refers to the total amount of electricity required within a market or a specific region.

[1186] "Supply data" refers to data regarding the amount of electricity supplied from power plants, solar power generation systems, etc.

[1187] "Purchase price" refers to the transaction price for buying and selling electricity, which is determined based on supply and demand.

[1188] "Means of calculation" refers to the method or process for calculating the purchase and sale price of electricity.

[1189] "Means of providing" refers to the method or process for notifying the user of the necessary information or instructions.

[1190] "Electricity usage data" refers to data relating to individual electricity usage within a household or community.

[1191] "Analytical means" refers to the methods and processes used to analyze collected electricity usage data and identify patterns and trends.

[1192] The "analysis results" are the results of analyzing the power usage data, and include energy consumption patterns and recommendations for energy conservation.

[1193] This invention is a system for promoting the self-consumption of surplus electricity generated by residential solar power generation systems for which the feed-in tariff (FIT) system has expired, and for promoting preparations for power shortages and the creation of disaster-resistant towns. This system includes a means for collecting information on energy storage device installation requests from households and maximizing cost benefits through bulk orders, as well as a means for optimizing the flow of electricity using AI to achieve efficient electricity consumption within a community.

[1194] Bulk purchasing and centralized installation of energy storage devices

[1195] The server collects energy storage device installation preference information from each household. This process is carried out using a web or mobile application (e.g., React or Flutter) with data provided by each household via the internet. The collected installation preference information is stored in a database (e.g., MySQL or PostgreSQL). The server then uses the aggregated data to select the most suitable supplier and place a bulk order. This bulk order is more cost-effective than individual purchases by each household.

[1196] For example, if 100 households in a region wish to install energy storage devices, the server will consolidate this information and place a bulk order. The ordered energy storage devices will be installed at a centralized installation location in cooperation with the local government. Users can check the installation progress information in real time using their smartphones or PCs.

[1197] Optimizing power flow with AI

[1198] The server collects data on power generation and consumption from each household's solar power generation system and consumption devices. This data collection is done using IoT devices (e.g., smart meters) and communication protocols (e.g., MQTT). The server processes the collected data and predicts the next day's generation and consumption amounts based on machine learning algorithms (e.g., TensorFlow or PyTorch). Based on the prediction results, it creates an optimal energy storage plan and sends appropriate power usage instructions to each household.

[1199] For example, if the weather forecast for the next day is sunny, the server will predict a high amount of power generation and instruct the system to prioritize storing surplus power. Conversely, during times of high consumption, the system will instruct the system to supply power from the storage battery, achieving efficient power usage. Users can check the AI's instructions via their device and manually adjust the settings as needed.

[1200] Dynamic pricing of electricity prices using AI

[1201] The server collects local electricity demand and supply data in real time and calculates buying and selling prices using an AI algorithm. The algorithm used for this calculation is suitable for analyzing time-series data (for example, the ARIMA model or LSTM). The server then sends the calculated price information to the user's device via a notification system (for example, Firebase Cloud Messaging).

[1202] For example, during times when demand for electricity in a region is high and supply is insufficient, the server will send notifications to terminals encouraging users to sell electricity at a higher price. Conversely, during times of excess supply, the server will offer lower prices to encourage users to purchase electricity. Users can make buying and selling decisions based on this price information.

[1203] AI-based collection and analysis of electricity usage data

[1204] The server collects real-time electricity usage data from each household and analyzes it using AI algorithms. This analysis utilizes data stored in a database (e.g., MongoDB or SQLite). Based on the analysis results, energy consumption patterns and energy-saving recommendations are generated and provided to users through a notification system.

[1205] For example, if a particular household shows high power consumption during a particular time period, the server will detect that pattern and notify the device of specific actions to save energy (such as reducing power consumption at night). Users can then check their own energy consumption patterns and adjust their settings to further save energy.

[1206] Example prompts for generative AI models

[1207] "Please explain the design of an optimal power management system for self-consumption of surplus electricity from residential solar power generation. Specifically, please show the system's processing steps, including bulk purchasing and centralized installation of energy storage devices, optimization of power flow and dynamic pricing using AI, and energy conservation recommendations."

[1208] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1209] Step 1: Collect information on installation preferences from households

[1210] The user uses a smartphone or PC to input desired installation information for the energy storage device (desired installation date, budget, installation location, etc.). The input information is sent from the device to the server. The input is done using a web application or mobile application (e.g., React or Flutter). The server stores the received desired installation information in a database (e.g., MySQL or PostgreSQL). This allows the server to obtain the desired installation information for use in subsequent processing. Specifically, the user enters the information they wish to provide and presses the "Submit" button, which sends the information to the server.

[1211] input:

[1212] Installation information entered by the user (desired installation date, budget, installation location, etc.)

[1213] output:

[1214] Installation preference information stored in the database

[1215] Step 2: Place a bulk order based on your installation requirements

[1216] The server aggregates the installation preference information collected from households. Based on this information, the server selects the most suitable supplier. For example, a cost-based optimization algorithm is used for the selection. The server then places a bulk order with the supplier. Specifically, the server automatically generates order data and sends it to the supplier. This allows the server to aggregate the installation preferences of multiple households and place an order with the supplier, achieving cost-effectiveness.

[1217] input:

[1218] Installation preference information stored in the database

[1219] output:

[1220] Bulk order data from suppliers

[1221] Step 3: Provide installation progress information

[1222] The server periodically receives installation progress information from the supplier after receiving the order and stores it in a database. The terminal provides the installation progress information to the user in real time. Specifically, the progress information is notified via a smartphone or PC application. This allows the user to constantly check the installation status of the energy storage device.

[1223] input:

[1224] Installation progress information received from the supplier

[1225] output:

[1226] Notifying users of installation progress information

[1227] Step 4: Collecting electricity production and consumption data

[1228] The server collects data on electricity generation and consumption from each home's solar power generation system and consumption devices. This data is collected using IoT devices (e.g., smart meters) and communication protocols (e.g., MQTT). The server stores the collected data in a database. Specifically, the smart meters send data to the server in real time, and the server records it in the database.

[1229] input:

[1230] Electricity generation and consumption data received from smart meters

[1231] output:

[1232] Power generation and consumption data stored in a database

[1233] Step 5: Forecast production and consumption

[1234] The server uses machine learning algorithms (such as TensorFlow or PyTorch) to predict the next day's generation and consumption amounts based on the collected data on power generation and consumption. Specifically, the server inputs the data into the algorithm, generates predictions, and stores them in a database. This makes it easier to plan the next day's power usage.

[1235] input:

[1236] Power generation and consumption data stored in a database

[1237] output:

[1238] Machine learning algorithm predicts next day's production and consumption

[1239] Step 6: Delivering optimal power usage instructions

[1240] The server creates an optimal power usage plan based on the prediction results and notifies each household. The notification is sent to the device in real time using, for example, Firebase Cloud Messaging. Specifically, the server generates optimal power usage plan data and distributes it to the user's device via the notification system. This allows the user to receive instructions on efficient power usage.

[1241] input:

[1242] Production and consumption forecast results using machine learning algorithms

[1243] output:

[1244] Notification of optimal power usage instructions to each household

[1245] Step 7: Collect electricity demand and supply data

[1246] The server collects real-time electricity demand and supply data within the region. This data is also stored in a database and used for subsequent processing. Specifically, the server analyzes and stores data collected from electricity meters within the region.

[1247] input:

[1248] Power supply and demand data received from power meters

[1249] output:

[1250] Power demand and supply data stored in a database

[1251] Step 8: Calculate the purchase price

[1252] The server calculates the buying and selling prices using AI algorithms (such as LSTM or ARIMA models) based on the collected electricity demand and supply data. Specifically, the AI ​​algorithm analyzes the data and calculates the buying and selling prices in real time, making dynamic pricing possible.

[1253] input:

[1254] Power demand and supply data stored in a database

[1255] output:

[1256] Calculated purchase and sale price data

[1257] Step 9: Notification of purchase and sale price information

[1258] The server sends the calculated buying and selling price information to the user's device via a notification system. The user buys and sells electricity based on this information. Specifically, the server sends the price information to the device using a notification system such as Firebase Cloud Messaging.

[1259] input:

[1260] Calculated purchase and sale price data

[1261] output:

[1262] Notification of buying and selling price information to users

[1263] Step 10: Collect and analyze electricity usage data

[1264] The server collects electricity usage data from each household in real time and analyzes it using AI algorithms (e.g., k-means clustering and deep learning). The analysis results are stored in a database and provided to users. Specifically, the server analyzes the collected data, stores the results, and notifies them.

[1265] input:

[1266] Electricity usage data collected from households

[1267] output:

[1268] AI analysis results and notification data

[1269] (Application example 1)

[1270] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1271] It is becoming increasingly important to effectively utilize surplus electricity from residential solar power generation systems, promote self-consumption, and realize efficient electricity management across the entire region. In particular, it is necessary not only to efficiently utilize electricity within individual households, but also to provide economic benefits through the sharing and trading of surplus electricity. There is also a need to balance supply and demand through dynamic electricity pricing and provide attractive price information to consumers in real time. Therefore, it is necessary to provide a comprehensive system to solve these issues, which have been difficult to address with conventional systems.

[1272] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1273] In this invention, the server includes means for collecting storage battery installation request information from households, means for ordering storage batteries in bulk, means for reserving centralized storage battery installation locations, means for providing installation progress information, means for collecting data on power generation and consumption, means for predicting power generation and consumption based on the collected data, means for instructing optimal power usage, means for collecting data on power supply and demand, means for calculating buying and selling prices, means for providing dynamic price information based on the predicted data and notifying the user, means for storing surplus power in a digital wallet as power tokens, means for enabling the buying and selling of power tokens to other users, means for collecting and analyzing power usage data, and means for providing the analysis results. This makes it possible to effectively manage surplus power generated by home solar power generation and provide optimal power usage and economic benefits.

[1274] "Storage battery installation request information" is information collected from homes and facilities that wish to install power storage equipment.

[1275] A "centralized battery installation site" is a location where batteries are installed together in order to efficiently manage electricity throughout an entire region.

[1276] "Installation progress information" is information indicating how far the installation work of the storage battery has progressed.

[1277] An "electricity token" is a digital record of excess electricity generated by a home solar power system.

[1278] A "digital wallet" is an electronic wallet for managing tokens or currencies recorded in digital form.

[1279] "Dynamic price information" refers to price information that fluctuates in real time depending on the electricity supply and demand situation.

[1280] "Marketplace" means an electronic exchange for the buying and selling of energy tokens between users.

[1281] "Electricity usage data" is information on the amount of electricity consumed by each household or facility.

[1282] "Analysis results" are the results of analysis using AI and other technologies based on collected data.

[1283] "Predictive data" is data that predicts future electricity generation and consumption using AI algorithms.

[1284] This invention is a comprehensive energy management system aimed at utilizing surplus electricity generated by residential solar power generation, with a particular focus on digital wallet applications including energy tokens and dynamic pricing functionality.

[1285] The server collects battery installation requests from each household and places a bulk order for the batteries. It also secures centralized battery installation locations and provides installation progress information. It collects data on power generation and consumption, and uses AI algorithms to predict power generation and consumption based on this data. Specific software used includes machine learning libraries such as Scikit-learn. Based on the predictions, it instructs on optimal power usage and notifies households in real time.

[1286] Furthermore, data on electricity supply and demand is collected and buying and selling prices are dynamically calculated using AI algorithms. The calculated price information is provided to users, and dynamic price information is provided based on forecast data. This allows users to buy and sell energy tokens at the most advantageous price. Energy tokens are a digital record of surplus electricity and are stored in a digital wallet. Users can buy and sell energy tokens with other users on the buying and selling marketplace using their own devices (smartphones, tablets, etc.).

[1287] For example, if a household generates 10 kWh and consumes 5 kWh in a day, the difference of 5 kWh is stored in the digital wallet as electricity tokens. Then, the AI ​​predicts whether the weather will be sunny the next day and notifies the user in real time of the times when electricity prices will be high. In this way, users can efficiently buy and sell surplus electricity.

[1288] The server also constantly collects power usage data and provides the analysis results to the user. Based on the analysis results, the user can understand power consumption patterns and take power-saving measures as necessary, thereby achieving further energy savings.

[1289] Prompt Sentence Examples

[1290] "Please create an example implementation of a system that manages surplus electricity generated by a home solar power generation system as electricity tokens and notifies prices in real time via a smartphone app. Please also add a marketplace function that allows users to buy and sell electricity tokens."

[1291] In this way, the present invention provides a comprehensive energy management system that covers all processes from battery installation to energy token management and dynamic pricing notification.

[1292] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1293] Step 1:

[1294] The server collects information from each household regarding their preference for battery installation.

[1295] Input: Battery installation request information from households

[1296] Data processing and calculation: The collected information is stored in a database, and the number of people who wish to install the device and the desired installation date are compiled.

[1297] Output: List of battery installation applicants

[1298] Step 2:

[1299] The server orders storage batteries in bulk based on the installation preference information collected.

[1300] Input: List of people wanting to install a storage battery

[1301] Data processing and data calculation: Supplier selection and bulk order optimization calculation.

[1302] Output: Order data for the best supplier

[1303] Step 3:

[1304] The server secures a centralized installation location for the storage batteries and provides installation progress information.

[1305] Input: Bulk order data, installation request list

[1306] Data processing and data calculation: Select the installation location and manage the installation schedule.

[1307] Output: Update installation progress information

[1308] Step 4:

[1309] The server collects data on the amount of electricity generated and consumed by each household.

[1310] Input: Power generation and consumption data for each household

[1311] Data processing and calculation: The amount of power generated and consumed is calculated based on the collected data.

[1312] Output: Data set of daily and hourly energy production and consumption

[1313] Step 5:

[1314] The server uses AI algorithms to predict power generation and consumption based on the collected data.

[1315] Input: Dataset of daily and hourly energy production and consumption

[1316] Data processing and data calculations: Machine learning models are used to predict the next day's electricity generation and consumption.

[1317] Output: Forecast data

[1318] Step 6:

[1319] The server instructs on optimal power usage and notifies households in real time.

[1320] Input: Forecast data

[1321] Data processing and calculation: Generate an optimal power usage plan and notify each household.

[1322] Output: Power usage instruction

[1323] Step 7:

[1324] The server collects data on electricity demand and supply and dynamically calculates buying and selling prices.

[1325] Input: Demand data, Supply data

[1326] Data processing and calculation: Calculate appropriate electricity prices using AI algorithms.

[1327] Output: Dynamic price information

[1328] Step 8:

[1329] The server provides and notifies the user of dynamic pricing information based on the forecast data.

[1330] Input: Dynamic Price Information

[1331] Data processing and data calculation: price information is generated and sent to the user terminal.

[1332] Output: Price notification to user

[1333] Step 9:

[1334] The server stores the surplus electricity in a digital wallet as electricity tokens.

[1335] Input: Surplus power data

[1336] Data processing and data calculation: Surplus electricity is tokenized and stored in a digital wallet.

[1337] Output: Power tokens

[1338] Step 10:

[1339] The server allows users to buy and sell energy tokens to other users and transacts them on the marketplace.

[1340] Input: Power tokens, desired trading information

[1341] Data processing and data calculation: Matching token purchases and sales and updating transaction records.

[1342] Output: Transaction completion notification

[1343] Step 11:

[1344] The server collects power usage data and provides the analysis results to the user.

[1345] Input: Power usage data

[1346] Data processing and data calculation: Perform data analysis and generate energy-saving recommendations.

[1347] Output: Notification of analysis results

[1348] A sample prompt is, "Please create an implementation example of a system that manages surplus electricity generated by a home solar power generation system as electricity tokens and notifies prices in real time via a smartphone app. Please also add a marketplace function that allows users to buy and sell electricity tokens."

[1349] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1350] ---

[1351] This invention is a system that promotes the self-consumption of surplus electricity generated by residential solar power generation systems for which the feed-in tariff (FIT) system has expired, and promotes preparations for power shortages and the creation of disaster-resistant towns. This system includes a means for collecting information on battery installation requests from households and maximizing cost benefits through bulk orders. It also utilizes AI to optimize the flow of electricity and achieve efficient power consumption within the region. Furthermore, it integrates an emotion engine that recognizes the user's emotions and suggests optimal energy-saving actions based on the user's energy consumption patterns.

[1352] 1. Bulk purchase and centralized installation of storage batteries

[1353] The server collects battery installation request information from each household. This information is then stored in a database, along with the number of people wanting to install a battery and the desired installation time. The server then selects the most suitable supplier based on the aggregated data and places a bulk order. This is more cost-effective than purchasing batteries individually.

[1354] For example, if 100 households in a region request the installation of storage batteries, the server will compile this information and place a bulk order. The obtained storage batteries will be installed at a centralized installation location in cooperation with the local government. Users can check the installation progress information in real time using their smartphones or PCs.

[1355] 2. Optimizing power flow using AI

[1356] The server collects data on power generation and consumption from each home's solar power generation system and power consumption devices. Based on this data, the server uses an AI algorithm to predict the next day's power generation and consumption. Based on the prediction results, the server creates an optimal power storage plan and sends appropriate power usage instructions to each household.

[1357] For example, if the weather forecast for the next day is sunny, the server will predict high power generation and instruct the system to prioritize storing surplus power. Conversely, during times of high consumption, the system will instruct the system to supply power from the storage battery, achieving efficient power usage. Users can check the AI's instructions via their device and manually adjust the settings as needed.

[1358] 3. Dynamic pricing of electricity using AI

[1359] The server collects local electricity demand and supply data in real time and calculates the buying and selling price using an AI algorithm. Based on this calculation, the device notifies the user of dynamic price information. This allows users to buy and sell electricity at a more advantageous price than under a contract with the power company.

[1360] For example, during times when demand for electricity in a region is high and supply is insufficient, the server will send notifications to terminals encouraging users to sell electricity at a higher price. Conversely, during times of excess supply, the server will offer lower prices to encourage users to buy electricity. Users can make buying and selling decisions based on this price information.

[1361] 4. AI-based data collection and analysis

[1362] The server collects real-time electricity usage data from each household and analyzes it using AI algorithms. Based on the results, recommendations for energy consumption patterns and energy conservation are generated and provided to users via their devices.

[1363] For example, if a household shows high power consumption during a certain time period, the server will detect this pattern and notify the device of specific actions to save energy (such as reducing power use at night). Users can then check their own energy consumption patterns and adjust their settings to achieve further energy savings.

[1364] 5. Integration of emotion engine that recognizes user emotions

[1365] The emotion engine collects emotional data from the user's facial expressions and voice and sends it to the server, which then analyzes the user's emotional state and identifies factors that affect power usage patterns.

[1366] For example, if the device detects that the user is feeling stressed, the server can use that data to adjust energy-saving behaviors, such as suggesting actions that help with relaxation (adjusting lighting, setting the air conditioner temperature, playing music, etc.), thereby improving the user's comfort.

[1367] 6. Adjusting energy-saving actions based on emotion data

[1368] The server combines the collected emotional data with power consumption data to generate optimal energy-saving actions, enabling flexible energy management according to the user's emotional state.

[1369] For example, when the user is relaxing, the server will prioritize actions to reduce power consumption. Conversely, when the user is energetic and active, the server will suggest settings that prioritize comfort. The user can easily adjust the energy-saving settings based on this.

[1370] By combining user emotion recognition, the system of the present invention offers greater flexibility and user experience than conventional energy-saving systems, thereby improving not only energy efficiency but also the user's quality of life.

[1371] The processing flow will be explained below.

[1372] ---

[1373] 1. Bulk purchase and centralized installation of storage batteries

[1374] Step 1:

[1375] The user inputs the desired battery installation information into the terminal, which then sends the information to the server.

[1376] Step 2:

[1377] The server stores and aggregates battery installation request information in a database.

[1378] Step 3:

[1379] The server selects the most suitable supplier based on the aggregated data and places a bulk order.

[1380] Step 4:

[1381] The server will work with local governments to secure a centralized installation location.

[1382] Step 5:

[1383] The server updates installation progress information in real time and notifies the user via the terminal.

[1384] ---

[1385] 2. Optimizing power flow using AI

[1386] Step 1:

[1387] The device measures the amount of electricity generated and consumed within the home in real time and sends the data to a server.

[1388] Step 2:

[1389] The server uses the collected data to run an AI algorithm and predict the next day's power generation and consumption.

[1390] Step 3:

[1391] Based on the prediction results, the server generates an optimal energy storage plan and instructions for power usage for each household.

[1392] Step 4:

[1393] The server generates instructions and distributes them to each household's terminal.

[1394] Step 5:

[1395] The user checks the instructions received through the device and manually adjusts the settings if necessary.

[1396] ---

[1397] 3. Dynamic pricing of electricity using AI

[1398] Step 1:

[1399] The server collects real-time data on electricity demand and supply within the region.

[1400] Step 2:

[1401] The server uses an AI algorithm to analyze the collected data and calculate the optimal buying and selling price.

[1402] Step 3:

[1403] The server sends the calculated purchase and sale price information to each household's terminal.

[1404] Step 4:

[1405] The device sends push notifications to the user, providing buying and selling price information.

[1406] Step 5:

[1407] The user checks the buying and selling price information on the terminal and decides whether to buy or sell electricity.

[1408] ---

[1409] 4. AI-based data collection and analysis

[1410] Step 1:

[1411] The terminal collects electricity usage data for each household in real time and sends it to a server.

[1412] Step 2:

[1413] The server stores the collected data in a database.

[1414] Step 3:

[1415] The server uses AI algorithms to analyze the data and identify energy consumption patterns.

[1416] Step 4:

[1417] The server generates energy-saving actions based on the analysis results and sends them to the terminal.

[1418] Step 5:

[1419] The device notifies the user of the analysis results and energy-saving actions.

[1420] Step 6:

[1421] The user checks the analysis results on the device and adjusts settings to save energy.

[1422] ---

[1423] 5. Integration of emotion engine that recognizes user emotions

[1424] Step 1:

[1425] The device analyzes the user's facial expressions and voice in real time to generate emotional data.

[1426] Step 2:

[1427] The device transmits the emotion data to the server.

[1428] Step 3:

[1429] The server analyzes the user's emotional state based on the emotional data.

[1430] Step 4:

[1431] The server combines emotional state and power consumption data to identify factors that influence energy consumption patterns.

[1432] ---

[1433] 6. Adjusting energy-saving actions based on emotion data

[1434] Step 1:

[1435] The server analyzes the emotion data and power consumption data and generates energy-saving actions.

[1436] Step 2:

[1437] The server sends energy-saving actions to each household's terminal.

[1438] Step 3:

[1439] The terminal notifies the user of energy-saving actions based on the emotion data.

[1440] Step 4:

[1441] The user checks the proposed energy-saving action on the terminal and decides whether to execute it.

[1442] ---

[1443] The above are the specific processing steps and operation details of the invention combined with the emotion engine.

[1444] Example 2

[1445] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1446] With the end of the Feed-in Tariff (FIT) system in modern society, there is a need to promote the self-consumption of surplus electricity from residential solar power generation while also preparing for power shortages and creating disaster-resistant communities. However, purchasing and installing energy storage devices individually for each household poses challenges, including high costs and the hassle of installation. There are also concerns about reduced efficiency due to suboptimal power usage patterns. Furthermore, there are no systems that take into account the influence of user emotions on power consumption behavior.

[1447] The specification process by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for collecting information on desired installation of household power storage devices, a means for ordering power storage devices in bulk, a means for reserving centralized installation locations for the power storage devices, a means for providing installation progress information, a means for collecting data on power generation and consumption, a means for predicting power generation and consumption based on the collected data, a means for instructing optimal power usage, a means for collecting data on power supply and demand, a means for calculating buying and selling prices, a means for providing calculated price information, a means for collecting and analyzing power usage data, a means for providing the analysis results, a means for collecting emotion data and analyzing factors affecting power usage patterns, and a means for generating and providing energy-saving actions based on the user's emotional state. This reduces the high cost and installation effort for each household, enables optimization of power usage patterns, and efficient energy management. Furthermore, flexible energy-saving actions based on the user's emotions can improve the user experience and quality of life.

[1448] A "home energy storage device" is a device with an energy storage function that is installed in a home and stores surplus electricity generated by solar power generation and other means, and supplies it when needed.

[1449] The "installation preference information" is information indicating that a household wishes to install a power storage device, and includes details such as the number of applicants and the desired installation time.

[1450] "Bulk ordering" refers to ordering multiple household energy storage devices at once, and is an ordering method that offers cost benefits over ordering individually.

[1451] "Concentrated installation site" means a specific location set aside temporarily or permanently for the efficient installation of energy storage devices.

[1452] "Installation progress information" is information indicating whether the installation of the power storage device is proceeding as planned, and is information that provides the progress status of the installation process in real time.

[1453] "Power generation amount" is data indicating the amount of power generated by the photovoltaic power generation system during a certain time period.

[1454] "Consumption" is data indicating the amount of electricity used within the home.

[1455] "Collected Data" refers to various data measured and acquired by the system, such as the amount of electricity generated and consumed.

[1456] "Forecasting" means estimating future power generation and consumption based on collected data.

[1457] "Directing power usage" means proposing and delivering optimal power usage methods to each household based on collected data and predictions.

[1458] "Dynamic pricing" is a pricing method that changes electricity prices based on real-time conditions of electricity supply and demand.

[1459] "Price information" refers to information including pricing and calculation results related to the purchase and sale of electricity.

[1460] "Electricity usage data" refers to historical information on the amount of electricity actually used within a household.

[1461] "Analysis" means the process of analyzing collected data and converting the information into a useful form.

[1462] "Emotion data" is data that indicates the emotional state of the user as determined by their facial expressions and voice.

[1463] "Energy saving actions" refer to specific actions or measures that should be taken to reduce energy consumption.

[1464] "User status" is information that indicates the user's current emotions and behavioral status.

[1465] The above definitions allow a clear understanding of each element of the invention and its function.

[1466] This invention relates to a system that improves the efficiency of home energy storage device installation and power management. This system collects information on energy storage device installation requests from households, reduces costs through bulk ordering, and uses AI to predict and optimize power consumption. It also collects user emotional data and suggests energy-saving actions based on emotions, improving the user experience.

[1467] Hardware and Software

[1468] server:

[1469] Database (storage of information on desired installation of power storage devices, and data on power generation and consumption)

[1470] AI algorithms (power forecasting, dynamic pricing, sentiment analysis)

[1471] Communication module (transmits and receives data from home devices)

[1472] Terminals (home devices):

[1473] Smartphone, tablet, PC (for entering and checking information)

[1474] Smart meters (for collecting data on electricity generation and consumption)

[1475] Emotion engine (for collecting emotion data)

[1476] Basic operation

[1477] Collection of information on home energy storage device installation requests:

[1478] Users enter their desired installation information for the storage device via a dedicated application on their smartphone or PC and send it to the server.

[1479] Supplier selection and bulk ordering:

[1480] Based on the installation information collected by the server, the system selects the most suitable supplier and places a bulk order for the energy storage devices, reducing costs compared to purchasing them individually.

[1481] Electricity generation and consumption data collection:

[1482] It collects electricity data in real time from smart meters and energy management systems (EMS) in homes and sends it to a server.

[1483] Prediction and optimization:

[1484] The server uses AI algorithms to analyze the collected data and predict the next day's electricity generation and consumption, and based on this, generates and presents an optimal electricity usage plan for each household.

[1485] Dynamic Pricing:

[1486] The server uses an AI algorithm to calculate the electricity buying and selling price based on real-time data on electricity demand and supply. The calculated price information is sent to the terminal, and the user can decide whether to buy or sell electricity.

[1487] Collecting emotional data and suggesting energy-saving actions:

[1488] The device uses an emotion engine to collect emotional data from the user's facial expressions and voice, and sends it to the server. The server analyzes the emotional data and power usage patterns, and generates and suggests energy-saving actions based on the user's emotional state.

[1489] Specific examples

[1490] If 100 households in a region wish to install a power storage device, each household can enter their information through a dedicated app. The server compiles this information and places a bulk order with significant cost benefits. Based on the power generation and consumption data collected from each household's smart meter, the system calculates the optimal power usage plan for the next day and notifies the smartphone or PC. Also, if the weather forecast predicts clear skies, the system will prioritize storing surplus power, thereby achieving efficient energy use. Furthermore, when the user is feeling stressed, the system will suggest actions to help them relax (such as adjusting the lighting or playing music).

[1491] Prompt Sentence Examples

[1492] Please explain the specific implementation procedures and effects of an energy management system that combines residential solar power generation and storage batteries, as shown below.

[1493] A system that collects battery installation request information from each household on a server and places a bulk order

[1494] A function that uses AI to predict power generation and consumption and provide optimal power usage plans

[1495] Dynamic pricing calculates electricity prices in real time and notifies users

[1496] A function that recognizes the user's emotions and suggests optimal energy-saving actions based on those emotions.

[1497] With the above configuration, the system of the present invention can improve the efficiency of installation and power management of home power storage devices, and can improve the energy consumption and quality of life of users.

[1498] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1499] Step 1:

[1500] Collecting information on home energy storage device installation requests

[1501] Input: The user enters desired information about the installation of the energy storage device (such as name, address, and desired installation date) through a dedicated application.

[1502] How it works: User input information is sent from the smartphone or PC to the server.

[1503] Output: The server stores these preferences in a database.

[1504] Step 2:

[1505] Supplier selection and bulk ordering

[1506] Input: The server compiles the storage battery installation request information stored in the database.

[1507] How it works: The server compares the prices, delivery times, and performance data of each supplier based on the aggregated data, selects the best supplier, and places a bulk order.

[1508] Output: A purchase order is placed with the best supplier and the required number of storage units is determined.

[1509] Step 3:

[1510] Collecting data on electricity generation and consumption

[1511] Input: Real-time electricity generation and consumption data collected from home smart meters and energy management systems.

[1512] How it works: The devices send this data to a server, which stores the data collected from all homes in a database.

[1513] Output: The electricity generation and consumption data stored on the server is updated.

[1514] Step 4:

[1515] Forecasting and optimizing power generation and consumption

[1516] Input: Historical electricity generation and consumption data stored on the server, along with related information such as weather forecasts.

[1517] How it works: The server uses AI algorithms to predict the next day's electricity generation and consumption. Based on this, the server generates an optimal electricity usage plan (when to store electricity and when to use it).

[1518] Output: A specific electricity usage plan for each household is generated and sent to the terminal.

[1519] Step 5:

[1520] Dynamic pricing calculation and notification

[1521] Input: Local electricity demand and supply data collected in real time by the server.

[1522] How it works: The server uses an AI algorithm to calculate the optimal price for selling and purchasing electricity.

[1523] Output: The calculated purchase and sale price information is sent to the home device.

[1524] Step 6:

[1525] Energy consumption data collection and analysis

[1526] Input: Electricity usage data collected from households.

[1527] How it works: The server analyzes this data using AI algorithms to generate energy consumption patterns and energy-saving recommendations.

[1528] Output: The generated recommendations are sent to the home device and notified to the user.

[1529] Step 7:

[1530] Collecting emotional data and suggesting energy-saving actions

[1531] Input: Emotional data such as facial expressions and voice of the user collected by the device.

[1532] How it works: The device sends this data to the server, which then analyzes it using an emotion engine. Based on the analysis, it generates energy-saving actions that correspond to the user's emotional state.

[1533] Output: The proposed energy saving actions are notified to the home device.

[1534] Through these processing steps, the system can integrate and manage the efficient deployment of energy storage devices, the optimization of power usage patterns, dynamic pricing, and emotion-based energy-saving behavior, thereby improving users' energy consumption and quality of life.

[1535] (Application example 2)

[1536] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1537] Efficient installation and operation of storage batteries is important to promote self-consumption of surplus electricity from residential solar power generation systems. However, purchasing and installing individual storage batteries is costly, and an advanced management system is required to efficiently use and optimally buy and sell electricity. Furthermore, flexible energy management that takes into account the emotional state of the user is required to balance power consumption with user comfort. A comprehensive system is needed to solve these problems.

[1538] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting storage battery installation preference information from households, means for ordering storage batteries in bulk, means for reserving centralized storage battery installation locations, means for providing installation progress information, means for collecting data on power generation and consumption, means for predicting power generation and consumption based on the collected data, means for instructing optimal power usage, means for collecting data on power supply and demand, means for calculating buying and selling prices, means for providing calculated price information, means for collecting and analyzing power usage data, means for providing the analysis results, means for collecting emotion data, means for adjusting settings based on the emotion data, and means for optimizing lighting and air conditioning settings based on the emotion data. This enables cost reduction through efficient installation and bulk purchase of storage batteries, and further realizes optimal power usage using AI and comfortable energy management based on the user's emotional state.

[1539] "Means for collecting information on battery installation preferences from households" refers to devices or systems that collect information on each household's desire to install a battery.

[1540] "Means for bulk ordering of batteries" refers to a method or system for ordering a large number of batteries at once.

[1541] "Means for securing locations for centralized installation of storage batteries" refers to methods or systems for centralizing and securing suitable locations for installing storage batteries.

[1542] "Means for providing installation progress information" refers to a device or system that provides information about the installation progress of a storage battery.

[1543] "Means for collecting data on electricity generation and consumption" refers to devices or systems that collect data on electricity generation and consumption.

[1544] "Means for predicting power generation and consumption based on collected data" refers to a method or system for predicting future power generation and consumption based on collected data.

[1545] "Means for instructing optimal power usage" refers to devices or systems that issue instructions for efficient power usage based on predictive data.

[1546] "Means for collecting electricity demand and supply data" refers to devices and systems that collect information on electricity demand and supply.

[1547] "Means for calculating purchase and sale prices" refers to methods and systems for calculating the purchase and sale prices of electricity based on collected data.

[1548] "Means for providing calculated price information" refers to a device or system that provides calculated price information to a user.

[1549] "Means for collecting and analyzing electricity usage data" refers to devices and systems that collect and analyze data related to electricity usage.

[1550] "Means for providing analysis results" refers to a device or system that provides the results of analyzed data to the user.

[1551] "Means for collecting emotional data" refers to a device or system that collects data about a user's emotional state.

[1552] "Means for adjusting settings based on emotional data" refers to a method or system for adjusting various system settings based on collected emotional data.

[1553] "Means for optimizing lighting and air conditioning settings based on emotional data" refers to devices and systems that optimize lighting and air conditioning settings based on emotional data.

[1554] This invention is a comprehensive system for promoting self-consumption of surplus electricity from a home solar power generation system or for efficient power management in a physical store. The system includes an emotion engine for optimizing lighting and air conditioning based on the user's emotional state.

[1555] System Configuration

[1556] The system includes several means:

[1557] 1. A means of collecting information on households' desire to install storage batteries

[1558] 2. A way to order batteries in bulk

[1559] 3. Measures to secure locations for centralized battery installation

[1560] 4. Means of providing installation progress information

[1561] 5. Means of collecting data on electricity generation and consumption

[1562] 6. A means of forecasting power generation and consumption based on collected data

[1563] 7. Means of directing optimal power usage

[1564] 8. Means of collecting electricity demand and supply data

[1565] 9. Means of calculating the purchase and sale price

[1566] 10. Means of Providing Calculated Pricing Information

[1567] 11. Means of collecting and analyzing electricity usage data

[1568] 12. Means of Providing Analysis Results

[1569] 13. Means of collecting emotional data

[1570] 14. A way to adjust settings based on emotional data

[1571] 15. A way to optimize lighting and air conditioning settings based on emotional data

[1572] What the program does

[1573] The server collects information on each household's battery installation preferences and places a bulk order, reducing installation costs. The server also secures centralized installation locations for the batteries and provides real-time updates on the installation progress.

[1574] The server collects data on each household's electricity generation and consumption and uses AI to predict future power generation and consumption. This allows it to create an optimal power usage plan and provide instructions to the user. It also collects data on electricity demand and supply and calculates electricity buying and selling prices in real time. The server then provides this price information to the user's device.

[1575] The emotion engine collects emotional data from the user's facial expressions and voice. The server analyzes the emotional data and adjusts lighting and air conditioning settings based on the user's emotional state, thereby improving the user's comfort.

[1576] Specific examples

[1577] For example, by using this system in a physical store, the following effects can be expected. First, the AI ​​predicts times when electricity usage will be high in the store and performs optimal energy management. Furthermore, the emotion engine recognizes customer emotions in real time, and if a customer appears stressed, it will dim the lights and adjust the air conditioning to provide a comfortable environment. These settings can be performed automatically by the server, eliminating the need for users to do anything.

[1578] Prompt Sentence Examples

[1579] An example of a prompt to be input to a generative AI model might be:

[1580] "Apply a system for managing excess electricity from home solar power generation systems to a physical store. Use AI to predict electricity usage and propose programs to optimize lighting and air conditioning settings based on the emotions of staff and customers."

[1581] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1582] Step 1:

[1583] The server collects battery installation preference information from households. The installation preference information (e.g., desired installation time, battery size, etc.) is sent from each household's terminal and stored as data. This allows the overall demand for battery installation to be grasped.

[1584] Step 2:

[1585] The server orders batteries in bulk. Based on the collected installation information, it generates and sends bulk ordering data to suppliers. Bulk orders can reduce costs.

[1586] Step 3:

[1587] The server secures a centralized installation location for the storage batteries. After the ordered storage batteries arrive, it aggregates and adjusts the installation locations for each household and secures a centralized installation location. It analyzes the geographical information from each household and generates a list of optimal installation locations.

[1588] Step 4:

[1589] The server provides installation progress information. It monitors the progress of the battery storage installation in real time and sends that information to the home device. The progress information includes the installation completion date and time and ongoing status data.

[1590] Step 5:

[1591] The server collects data on the amount of electricity generated and consumed. This data is input in real time from each household's solar power generation system and consumption devices, and the data is stored and managed.

[1592] Step 6:

[1593] The server predicts power generation and consumption amounts based on the collected data. The collected power generation and consumption data is input into an AI algorithm to predict power generation and consumption amounts for the next day. An energy management plan is generated based on the predicted data.

[1594] Step 7:

[1595] The server then instructs the optimal use of electricity. Based on the predicted power generation and consumption, it generates power usage instructions for each household and sends them to the terminal. Specifically, this includes battery charging and discharging schedules and timings for efficient power usage.

[1596] Step 8:

[1597] The server collects and stores data on electricity demand and supply. This data is input in real time and includes supply data from the power supply company and demand data from users.

[1598] Step 9:

[1599] The server calculates the buying and selling prices. Based on the collected electricity demand and supply data, an AI algorithm calculates the optimal buying and selling price. The price calculation algorithm takes into account market trends and seasonal fluctuations.

[1600] Step 10:

[1601] The server provides the calculated price information. The calculated purchase and sale price information is sent to a home terminal and notified to the user. The user receives the notification and makes a decision to execute the purchase and sale as necessary.

[1602] Step 11:

[1603] The server collects and analyzes real-time power usage data sent from home devices and analyzes it using AI algorithms to identify power usage patterns and generate recommendations for improving energy efficiency.

[1604] Step 12:

[1605] The server provides the analysis results. The analyzed power usage data is sent to the home device, where specific suggestions (e.g., recommended usage times for high-efficiency devices) are presented to promote the optimization of energy consumption.

[1606] Step 13:

[1607] The server collects emotion data by receiving the user's facial expressions and voice as analysis results from the emotion engine connected to the home device.

[1608] Step 14:

[1609] The server adjusts settings based on the emotion data. Based on the emotion data, it generates settings (e.g., lighting and air conditioning adjustments) to improve the user's comfort and sends them to the home device.

[1610] Step 15:

[1611] The server optimizes lighting and air conditioning settings based on the emotional data. It analyzes the emotional data and applies the most appropriate lighting and air conditioning settings to the home devices. By automatically reflecting the settings that the user finds most comfortable, it is possible to achieve both energy efficiency and comfort.

[1612] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1613] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1614] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1615] [Fourth embodiment]

[1616] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1617] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1618] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1619] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1620] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1621] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1622] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1623] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1624] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1625] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1626] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1627] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1628] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1629] ---

[1630] This invention is a system that promotes the self-consumption of surplus electricity generated by residential solar power generation systems for which the feed-in tariff (FIT) system has expired, and promotes preparation for power shortages and disaster-resistant urban development. This system includes a means for collecting information on battery installation requests from households and maximizing cost benefits through bulk orders. It also utilizes AI to optimize the flow of electricity and achieve efficient power consumption within the community.

[1631] 1. Bulk purchase and centralized installation of storage batteries

[1632] The server collects battery installation request information from each household. This information is then stored in a database, along with the number of people wanting to install a battery and the desired installation time. The server then selects the most suitable supplier based on the aggregated data and places a bulk order. This is more cost-effective than purchasing batteries individually.

[1633] For example, if 100 households in a region request the installation of storage batteries, the server will compile this information and place a bulk order. The obtained storage batteries will be installed at a centralized installation location in cooperation with the local government. Users can check the installation progress information in real time using their smartphones or PCs.

[1634] 2. Optimizing power flow using AI

[1635] The server collects data on power generation and consumption from each home's solar power generation system and power consumption devices. Based on this data, the server uses an AI algorithm to predict the next day's power generation and consumption. Based on the prediction results, the server creates an optimal power storage plan and sends appropriate power usage instructions to each household.

[1636] For example, if the weather forecast for the next day is sunny, the server will predict high power generation and instruct the system to prioritize storing surplus power. Conversely, during times of high consumption, the system will instruct the system to supply power from the storage battery, achieving efficient power usage. Users can check the AI's instructions via their device and manually adjust the settings as needed.

[1637] 3. Dynamic pricing of electricity using AI

[1638] The server collects local electricity demand and supply data in real time and calculates the buying and selling price using an AI algorithm. Based on this calculation, the device notifies the user of dynamic price information. This allows users to buy and sell electricity at a more advantageous price than under a contract with the power company.

[1639] For example, during times when demand for electricity in a region is high and supply is insufficient, the server will send notifications to terminals encouraging users to sell electricity at a higher price. Conversely, during times of excess supply, the server will offer lower prices to encourage users to buy electricity. Users can make buying and selling decisions based on this price information.

[1640] 4. AI-based data collection and analysis

[1641] The server collects real-time electricity usage data from each household and analyzes it using AI algorithms. Based on the results, recommendations for energy consumption patterns and energy conservation are generated and provided to users via their devices.

[1642] For example, if a household shows high power consumption during a certain time period, the server will detect this pattern and notify the device of specific actions to save energy (such as reducing power use at night). Users can then check their own energy consumption patterns and adjust their settings to achieve further energy savings.

[1643] In this way, the system of the present invention utilizes AI at each stage to achieve efficient power management and consumption, improving energy efficiency at the household level while also realizing sustainable energy management across the entire region.

[1644] The processing flow will be explained below.

[1645] ---

[1646] 1. Bulk purchase and centralized installation of storage batteries

[1647] Step 1:

[1648] The user inputs the desired battery installation information into the terminal, which then sends the information to the server.

[1649] Step 2:

[1650] The server stores and aggregates battery installation request information in a database.

[1651] Step 3:

[1652] The server selects the most suitable supplier based on the aggregated data and places a bulk order.

[1653] Step 4:

[1654] The server will work with local governments to secure a centralized installation location.

[1655] Step 5:

[1656] The server updates installation progress information in real time and notifies the user via the terminal.

[1657] ---

[1658] 2. Optimizing power flow using AI

[1659] Step 1:

[1660] The device measures the amount of electricity generated and consumed within the home in real time and sends the data to a server.

[1661] Step 2:

[1662] The server uses the collected data to run an AI algorithm and predict the next day's power generation and consumption.

[1663] Step 3:

[1664] Based on the prediction results, the server generates an optimal energy storage plan and instructions for power usage for each household.

[1665] Step 4:

[1666] The server generates instructions and distributes them to each household's terminal.

[1667] Step 5:

[1668] The user checks the instructions received through the device and manually adjusts the settings if necessary.

[1669] ---

[1670] 3. Dynamic pricing of electricity using AI

[1671] Step 1:

[1672] The server collects real-time data on electricity demand and supply within the region.

[1673] Step 2:

[1674] The server uses an AI algorithm to analyze the collected data and calculate the optimal buying and selling price.

[1675] Step 3:

[1676] The server sends the calculated purchase and sale price information to each household's terminal.

[1677] Step 4:

[1678] The device sends push notifications to the user, providing buying and selling price information.

[1679] Step 5:

[1680] The user checks the buying and selling price information on the terminal and decides whether to buy or sell electricity.

[1681] ---

[1682] 4. AI-based data collection and analysis

[1683] Step 1:

[1684] The terminal collects electricity usage data for each household in real time and sends it to a server.

[1685] Step 2:

[1686] The server stores the collected data in a database.

[1687] Step 3:

[1688] The server uses AI algorithms to analyze the data and identify energy consumption patterns.

[1689] Step 4:

[1690] The server generates energy-saving actions based on the analysis results and sends them to the terminal.

[1691] Step 5:

[1692] The device notifies the user of the analysis results and energy-saving actions.

[1693] Step 6:

[1694] The user checks the analysis results on the device and adjusts settings to save energy.

[1695] ---

[1696] The above are the specific steps and operation details of each process.

[1697] Example 1

[1698] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1699] Many households face the challenge of efficiently self-consuming surplus electricity generated by residential solar power generation systems, for which the Feed-in Tariff (FIT) scheme has expired. Furthermore, the high cost of purchasing and installing energy storage devices for individual households is another major obstacle. Furthermore, efficient power management and consumption across an entire region requires the prediction and optimization of power generation and consumption, and responses to power shortages and disasters are also required. Given this background, there is a demand for systems that enable the efficient purchase and installation of energy storage devices and power management.

[1700] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1701] In this invention, the server includes means for collecting energy storage device installation request information from households, means for ordering energy storage devices in bulk, means for reserving centralized installation locations for the energy storage devices, means for providing installation progress information, means for collecting data on power generation and consumption, means for predicting power generation and consumption based on the collected data, means for instructing optimal power usage, means for collecting data on power supply and demand, means for calculating buying and selling prices, means for providing calculated price information, means for collecting and analyzing power usage data, and means for providing analysis results. This makes it possible to efficiently install energy storage devices, reduce costs, and predict and optimize power generation and consumption, thereby realizing efficient power management and consumption throughout the entire region.

[1702] "Household" refers to an individual or group of occupants, a unit of living and consuming energy within a particular dwelling.

[1703] An "energy storage device" is a device for storing excess electricity generated by home solar power generation, and includes batteries and storage cells.

[1704] "Installation preference information" is data indicating the user's wishes and requests regarding the installation of an energy storage device, and specifically includes information such as the desired installation date, budget, and installation location.

[1705] "Bulk ordering means" refers to a method or process for placing bulk orders with suppliers based on the installation preference information for energy storage devices collected from multiple households.

[1706] "Aggregate installation site" refers to a concentrated installation area for multiple homes selected for efficient installation of energy storage devices.

[1707] "Installation progress information" refers to data regarding the progress of the installation work of the energy storage device, and is provided so that the user can check it.

[1708] "Electricity generation" refers to the amount of electricity generated by solar power generation systems, etc.

[1709] "Consumption" refers to the amount of electricity actually used by a household.

[1710] "Collected data" refers to various data obtained from households, such as electricity generation, consumption, and installation preferences.

[1711] "Forecasting means" refers to the method or process of forecasting future electricity generation and consumption based on collected data using machine learning algorithms, statistical models, etc.

[1712] "Means for instructing optimal power usage" refers to a method or process that provides specific instructions for optimizing power usage within a home based on the prediction results.

[1713] "Electricity demand" refers to the total amount of electricity required within a market or a specific region.

[1714] "Supply data" refers to data regarding the amount of electricity supplied from power plants, solar power generation systems, etc.

[1715] "Purchase price" refers to the transaction price for buying and selling electricity, which is determined based on supply and demand.

[1716] "Means of calculation" refers to the method or process for calculating the purchase and sale price of electricity.

[1717] "Means of providing" refers to the method or process for notifying the user of the necessary information or instructions.

[1718] "Electricity usage data" refers to data relating to individual electricity usage within a household or community.

[1719] "Analytical means" refers to the methods and processes used to analyze collected electricity usage data and identify patterns and trends.

[1720] The "analysis results" are the results of analyzing the power usage data, and include energy consumption patterns and recommendations for energy conservation.

[1721] This invention is a system for promoting the self-consumption of surplus electricity generated by residential solar power generation systems for which the feed-in tariff (FIT) system has expired, and for promoting preparations for power shortages and the creation of disaster-resistant towns. This system includes a means for collecting information on energy storage device installation requests from households and maximizing cost benefits through bulk orders, as well as a means for optimizing the flow of electricity using AI to achieve efficient electricity consumption within a community.

[1722] Bulk purchasing and centralized installation of energy storage devices

[1723] The server collects energy storage device installation preference information from each household. This process is carried out using a web or mobile application (e.g., React or Flutter) with data provided by each household via the internet. The collected installation preference information is stored in a database (e.g., MySQL or PostgreSQL). The server then uses the aggregated data to select the most suitable supplier and place a bulk order. This bulk order is more cost-effective than individual purchases by each household.

[1724] For example, if 100 households in a region wish to install energy storage devices, the server will consolidate this information and place a bulk order. The ordered energy storage devices will be installed at a centralized installation location in cooperation with the local government. Users can check the installation progress information in real time using their smartphones or PCs.

[1725] Optimizing power flow with AI

[1726] The server collects data on power generation and consumption from each household's solar power generation system and consumption devices. This data collection is done using IoT devices (e.g., smart meters) and communication protocols (e.g., MQTT). The server processes the collected data and predicts the next day's generation and consumption amounts based on machine learning algorithms (e.g., TensorFlow or PyTorch). Based on the prediction results, it creates an optimal energy storage plan and sends appropriate power usage instructions to each household.

[1727] For example, if the weather forecast for the next day is sunny, the server will predict a high amount of power generation and instruct the system to prioritize storing surplus power. Conversely, during times of high consumption, the system will instruct the system to supply power from the storage battery, achieving efficient power usage. Users can check the AI's instructions via their device and manually adjust the settings as needed.

[1728] Dynamic pricing of electricity prices using AI

[1729] The server collects local electricity demand and supply data in real time and calculates buying and selling prices using an AI algorithm. The algorithm used for this calculation is suitable for analyzing time-series data (for example, the ARIMA model or LSTM). The server then sends the calculated price information to the user's device via a notification system (for example, Firebase Cloud Messaging).

[1730] For example, during times when demand for electricity in a region is high and supply is insufficient, the server will send notifications to terminals encouraging users to sell electricity at a higher price. Conversely, during times of excess supply, the server will offer lower prices to encourage users to purchase electricity. Users can make buying and selling decisions based on this price information.

[1731] AI-based collection and analysis of electricity usage data

[1732] The server collects real-time electricity usage data from each household and analyzes it using AI algorithms. This analysis utilizes data stored in a database (e.g., MongoDB or SQLite). Based on the analysis results, energy consumption patterns and energy-saving recommendations are generated and provided to users through a notification system.

[1733] For example, if a particular household shows high power consumption during a particular time period, the server will detect that pattern and notify the device of specific actions to save energy (such as reducing power consumption at night). Users can then check their own energy consumption patterns and adjust their settings to further save energy.

[1734] Example prompts for generative AI models

[1735] "Please explain the design of an optimal power management system for self-consumption of surplus electricity from residential solar power generation. Specifically, please show the system's processing steps, including bulk purchasing and centralized installation of energy storage devices, optimization of power flow and dynamic pricing using AI, and energy conservation recommendations."

[1736] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1737] Step 1: Collect information on installation preferences from households

[1738] The user uses a smartphone or PC to input desired installation information for the energy storage device (desired installation date, budget, installation location, etc.). The input information is sent from the device to the server. The input is done using a web application or mobile application (e.g., React or Flutter). The server stores the received desired installation information in a database (e.g., MySQL or PostgreSQL). This allows the server to obtain the desired installation information for use in subsequent processing. Specifically, the user enters the information they wish to provide and presses the "Submit" button, which sends the information to the server.

[1739] input:

[1740] Installation information entered by the user (desired installation date, budget, installation location, etc.)

[1741] output:

[1742] Installation preference information stored in the database

[1743] Step 2: Place a bulk order based on your installation requirements

[1744] The server aggregates the installation preference information collected from households. Based on this information, the server selects the most suitable supplier. For example, a cost-based optimization algorithm is used for the selection. The server then places a bulk order with the supplier. Specifically, the server automatically generates order data and sends it to the supplier. This allows the server to aggregate the installation preferences of multiple households and place an order with the supplier, achieving cost-effectiveness.

[1745] input:

[1746] Installation preference information stored in the database

[1747] output:

[1748] Bulk order data from suppliers

[1749] Step 3: Provide installation progress information

[1750] The server periodically receives installation progress information from the supplier after receiving the order and stores it in a database. The terminal provides the installation progress information to the user in real time. Specifically, the progress information is notified via a smartphone or PC application. This allows the user to constantly check the installation status of the energy storage device.

[1751] input:

[1752] Installation progress information received from the supplier

[1753] output:

[1754] Notifying users of installation progress information

[1755] Step 4: Collecting electricity production and consumption data

[1756] The server collects data on electricity generation and consumption from each home's solar power generation system and consumption devices. This data is collected using IoT devices (e.g., smart meters) and communication protocols (e.g., MQTT). The server stores the collected data in a database. Specifically, the smart meters send data to the server in real time, and the server records it in the database.

[1757] input:

[1758] Electricity generation and consumption data received from smart meters

[1759] output:

[1760] Power generation and consumption data stored in a database

[1761] Step 5: Forecast production and consumption

[1762] The server uses machine learning algorithms (such as TensorFlow or PyTorch) to predict the next day's generation and consumption amounts based on the collected data on power generation and consumption. Specifically, the server inputs the data into the algorithm, generates predictions, and stores them in a database. This makes it easier to plan the next day's power usage.

[1763] input:

[1764] Power generation and consumption data stored in a database

[1765] output:

[1766] Machine learning algorithm predicts next day's production and consumption

[1767] Step 6: Delivering optimal power usage instructions

[1768] The server creates an optimal power usage plan based on the prediction results and notifies each household. The notification is sent to the device in real time using, for example, Firebase Cloud Messaging. Specifically, the server generates optimal power usage plan data and distributes it to the user's device via the notification system. This allows the user to receive instructions on efficient power usage.

[1769] input:

[1770] Production and consumption forecast results using machine learning algorithms

[1771] output:

[1772] Notification of optimal power usage instructions to each household

[1773] Step 7: Collect electricity demand and supply data

[1774] The server collects real-time electricity demand and supply data within the region. This data is also stored in a database and used for subsequent processing. Specifically, the server analyzes and stores data collected from electricity meters within the region.

[1775] input:

[1776] Power supply and demand data received from power meters

[1777] output:

[1778] Power demand and supply data stored in a database

[1779] Step 8: Calculate the purchase price

[1780] The server calculates the buying and selling prices using AI algorithms (such as LSTM or ARIMA models) based on the collected electricity demand and supply data. Specifically, the AI ​​algorithm analyzes the data and calculates the buying and selling prices in real time, making dynamic pricing possible.

[1781] input:

[1782] Power demand and supply data stored in a database

[1783] output:

[1784] Calculated purchase and sale price data

[1785] Step 9: Notification of purchase and sale price information

[1786] The server sends the calculated buying and selling price information to the user's device via a notification system. The user buys and sells electricity based on this information. Specifically, the server sends the price information to the device using a notification system such as Firebase Cloud Messaging.

[1787] input:

[1788] Calculated purchase and sale price data

[1789] output:

[1790] Notification of buying and selling price information to users

[1791] Step 10: Collect and analyze electricity usage data

[1792] The server collects electricity usage data from each household in real time and analyzes it using AI algorithms (e.g., k-means clustering and deep learning). The analysis results are stored in a database and provided to users. Specifically, the server analyzes the collected data, stores the results, and notifies them.

[1793] input:

[1794] Electricity usage data collected from households

[1795] output:

[1796] AI analysis results and notification data

[1797] (Application example 1)

[1798] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1799] It is becoming increasingly important to effectively utilize surplus electricity from residential solar power generation systems, promote self-consumption, and realize efficient electricity management across the entire region. In particular, it is necessary not only to efficiently utilize electricity within individual households, but also to provide economic benefits through the sharing and trading of surplus electricity. There is also a need to balance supply and demand through dynamic electricity pricing and provide attractive price information to consumers in real time. Therefore, it is necessary to provide a comprehensive system to solve these issues, which have been difficult to address with conventional systems.

[1800] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1801] In this invention, the server includes means for collecting storage battery installation request information from households, means for ordering storage batteries in bulk, means for reserving centralized storage battery installation locations, means for providing installation progress information, means for collecting data on power generation and consumption, means for predicting power generation and consumption based on the collected data, means for instructing optimal power usage, means for collecting data on power supply and demand, means for calculating buying and selling prices, means for providing dynamic price information based on the predicted data and notifying the user, means for storing surplus power in a digital wallet as power tokens, means for enabling the buying and selling of power tokens to other users, means for collecting and analyzing power usage data, and means for providing the analysis results. This makes it possible to effectively manage surplus power generated by home solar power generation and provide optimal power usage and economic benefits.

[1802] "Storage battery installation request information" is information collected from homes and facilities that wish to install power storage equipment.

[1803] A "centralized battery installation site" is a location where batteries are installed together in order to efficiently manage electricity throughout an entire region.

[1804] "Installation progress information" is information indicating how far the installation work of the storage battery has progressed.

[1805] An "electricity token" is a digital record of excess electricity generated by a home solar power system.

[1806] A "digital wallet" is an electronic wallet for managing tokens or currencies recorded in digital form.

[1807] "Dynamic price information" refers to price information that fluctuates in real time depending on the electricity supply and demand situation.

[1808] "Marketplace" means an electronic exchange for the buying and selling of energy tokens between users.

[1809] "Electricity usage data" is information on the amount of electricity consumed by each household or facility.

[1810] "Analysis results" are the results of analysis using AI and other technologies based on collected data.

[1811] "Predictive data" is data that predicts future electricity generation and consumption using AI algorithms.

[1812] This invention is a comprehensive energy management system aimed at utilizing surplus electricity generated by residential solar power generation, with a particular focus on digital wallet applications including energy tokens and dynamic pricing functionality.

[1813] The server collects battery installation requests from each household and places a bulk order for the batteries. It also secures centralized battery installation locations and provides installation progress information. It collects data on power generation and consumption, and uses AI algorithms to predict power generation and consumption based on this data. Specific software used includes machine learning libraries such as Scikit-learn. Based on the predictions, it instructs on optimal power usage and notifies households in real time.

[1814] Furthermore, data on electricity supply and demand is collected and buying and selling prices are dynamically calculated using AI algorithms. The calculated price information is provided to users, and dynamic price information is provided based on forecast data. This allows users to buy and sell energy tokens at the most advantageous price. Energy tokens are a digital record of surplus electricity and are stored in a digital wallet. Users can buy and sell energy tokens with other users on the buying and selling marketplace using their own devices (smartphones, tablets, etc.).

[1815] For example, if a household generates 10 kWh and consumes 5 kWh in a day, the difference of 5 kWh is stored in the digital wallet as electricity tokens. Then, the AI ​​predicts whether the weather will be sunny the next day and notifies the user in real time of the times when electricity prices will be high. In this way, users can efficiently buy and sell surplus electricity.

[1816] The server also constantly collects power usage data and provides the analysis results to the user. Based on the analysis results, the user can understand power consumption patterns and take power-saving measures as necessary, thereby achieving further energy savings.

[1817] Prompt Sentence Examples

[1818] "Please create an example implementation of a system that manages surplus electricity generated by a home solar power generation system as electricity tokens and notifies prices in real time via a smartphone app. Please also add a marketplace function that allows users to buy and sell electricity tokens."

[1819] In this way, the present invention provides a comprehensive energy management system that covers all processes from battery installation to energy token management and dynamic pricing notification.

[1820] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1821] Step 1:

[1822] The server collects information from each household regarding their preference for battery installation.

[1823] Input: Battery installation request information from households

[1824] Data processing and calculation: The collected information is stored in a database, and the number of people who wish to install the device and the desired installation date are compiled.

[1825] Output: List of battery installation applicants

[1826] Step 2:

[1827] The server orders storage batteries in bulk based on the installation preference information collected.

[1828] Input: List of people wanting to install a storage battery

[1829] Data processing and data calculation: Supplier selection and bulk order optimization calculation.

[1830] Output: Order data for the best supplier

[1831] Step 3:

[1832] The server secures a centralized installation location for the storage batteries and provides installation progress information.

[1833] Input: Bulk order data, installation request list

[1834] Data processing and data calculation: Select the installation location and manage the installation schedule.

[1835] Output: Update installation progress information

[1836] Step 4:

[1837] The server collects data on the amount of electricity generated and consumed by each household.

[1838] Input: Power generation and consumption data for each household

[1839] Data processing and calculation: The amount of power generated and consumed is calculated based on the collected data.

[1840] Output: Data set of daily and hourly energy production and consumption

[1841] Step 5:

[1842] The server uses AI algorithms to predict power generation and consumption based on the collected data.

[1843] Input: Dataset of daily and hourly energy production and consumption

[1844] Data processing and data calculations: Machine learning models are used to predict the next day's electricity generation and consumption.

[1845] Output: Forecast data

[1846] Step 6:

[1847] The server instructs on optimal power usage and notifies households in real time.

[1848] Input: Forecast data

[1849] Data processing and calculation: Generate an optimal power usage plan and notify each household.

[1850] Output: Power usage instruction

[1851] Step 7:

[1852] The server collects data on electricity demand and supply and dynamically calculates buying and selling prices.

[1853] Input: Demand data, Supply data

[1854] Data processing and calculation: Calculate appropriate electricity prices using AI algorithms.

[1855] Output: Dynamic price information

[1856] Step 8:

[1857] The server provides and notifies the user of dynamic pricing information based on the forecast data.

[1858] Input: Dynamic Price Information

[1859] Data processing and data calculation: price information is generated and sent to the user terminal.

[1860] Output: Price notification to user

[1861] Step 9:

[1862] The server stores the surplus electricity in a digital wallet as electricity tokens.

[1863] Input: Surplus power data

[1864] Data processing and data calculation: Surplus electricity is tokenized and stored in a digital wallet.

[1865] Output: Power tokens

[1866] Step 10:

[1867] The server allows users to buy and sell energy tokens to other users and transacts them on the marketplace.

[1868] Input: Power tokens, desired trading information

[1869] Data processing and data calculation: Matching token purchases and sales and updating transaction records.

[1870] Output: Transaction completion notification

[1871] Step 11:

[1872] The server collects power usage data and provides the analysis results to the user.

[1873] Input: Power usage data

[1874] Data processing and data calculation: Perform data analysis and generate energy-saving recommendations.

[1875] Output: Notification of analysis results

[1876] A sample prompt is, "Please create an implementation example of a system that manages surplus electricity generated by a home solar power generation system as electricity tokens and notifies prices in real time via a smartphone app. Please also add a marketplace function that allows users to buy and sell electricity tokens."

[1877] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1878] ---

[1879] This invention is a system that promotes the self-consumption of surplus electricity generated by residential solar power generation systems for which the feed-in tariff (FIT) system has expired, and promotes preparations for power shortages and the creation of disaster-resistant towns. This system includes a means for collecting information on battery installation requests from households and maximizing cost benefits through bulk orders. It also utilizes AI to optimize the flow of electricity and achieve efficient power consumption within the region. Furthermore, it integrates an emotion engine that recognizes the user's emotions and suggests optimal energy-saving actions based on the user's energy consumption patterns.

[1880] 1. Bulk purchase and centralized installation of storage batteries

[1881] The server collects battery installation request information from each household. This information is then stored in a database, along with the number of people wanting to install a battery and the desired installation time. The server then selects the most suitable supplier based on the aggregated data and places a bulk order. This is more cost-effective than purchasing batteries individually.

[1882] For example, if 100 households in a region request the installation of storage batteries, the server will compile this information and place a bulk order. The obtained storage batteries will be installed at a centralized installation location in cooperation with the local government. Users can check the installation progress information in real time using their smartphones or PCs.

[1883] 2. Optimizing power flow using AI

[1884] The server collects data on power generation and consumption from each home's solar power generation system and power consumption devices. Based on this data, the server uses an AI algorithm to predict the next day's power generation and consumption. Based on the prediction results, the server creates an optimal power storage plan and sends appropriate power usage instructions to each household.

[1885] For example, if the weather forecast for the next day is sunny, the server will predict high power generation and instruct the system to prioritize storing surplus power. Conversely, during times of high consumption, the system will instruct the system to supply power from the storage battery, achieving efficient power usage. Users can check the AI's instructions via their device and manually adjust the settings as needed.

[1886] 3. Dynamic pricing of electricity using AI

[1887] The server collects local electricity demand and supply data in real time and calculates the buying and selling price using an AI algorithm. Based on this calculation, the device notifies the user of dynamic price information. This allows users to buy and sell electricity at a more advantageous price than under a contract with the power company.

[1888] For example, during times when demand for electricity in a region is high and supply is insufficient, the server will send notifications to terminals encouraging users to sell electricity at a higher price. Conversely, during times of excess supply, the server will offer lower prices to encourage users to buy electricity. Users can make buying and selling decisions based on this price information.

[1889] 4. AI-based data collection and analysis

[1890] The server collects real-time electricity usage data from each household and analyzes it using AI algorithms. Based on the results, recommendations for energy consumption patterns and energy conservation are generated and provided to users via their devices.

[1891] For example, if a household shows high power consumption during a certain time period, the server will detect this pattern and notify the device of specific actions to save energy (such as reducing power use at night). Users can then check their own energy consumption patterns and adjust their settings to achieve further energy savings.

[1892] 5. Integration of emotion engine that recognizes user emotions

[1893] The emotion engine collects emotional data from the user's facial expressions and voice and sends it to the server, which then analyzes the user's emotional state and identifies factors that affect power usage patterns.

[1894] For example, if the device detects that the user is feeling stressed, the server can use that data to adjust energy-saving behaviors, such as suggesting actions that help with relaxation (adjusting lighting, setting the air conditioner temperature, playing music, etc.), thereby improving the user's comfort.

[1895] 6. Adjusting energy-saving actions based on emotion data

[1896] The server combines the collected emotional data with power consumption data to generate optimal energy-saving actions, enabling flexible energy management according to the user's emotional state.

[1897] For example, when the user is relaxing, the server will prioritize actions to reduce power consumption. Conversely, when the user is energetic and active, the server will suggest settings that prioritize comfort. The user can easily adjust the energy-saving settings based on this.

[1898] By combining user emotion recognition, the system of the present invention offers greater flexibility and user experience than conventional energy-saving systems, thereby improving not only energy efficiency but also the user's quality of life.

[1899] The processing flow will be explained below.

[1900] ---

[1901] 1. Bulk purchase and centralized installation of storage batteries

[1902] Step 1:

[1903] The user inputs the desired battery installation information into the terminal, which then sends the information to the server.

[1904] Step 2:

[1905] The server stores and aggregates battery installation request information in a database.

[1906] Step 3:

[1907] The server selects the most suitable supplier based on the aggregated data and places a bulk order.

[1908] Step 4:

[1909] The server will work with local governments to secure a centralized installation location.

[1910] Step 5:

[1911] The server updates installation progress information in real time and notifies the user via the terminal.

[1912] ---

[1913] 2. Optimizing power flow using AI

[1914] Step 1:

[1915] The device measures the amount of electricity generated and consumed within the home in real time and sends the data to a server.

[1916] Step 2:

[1917] The server uses the collected data to run an AI algorithm and predict the next day's power generation and consumption.

[1918] Step 3:

[1919] Based on the prediction results, the server generates an optimal energy storage plan and instructions for power usage for each household.

[1920] Step 4:

[1921] The server generates instructions and distributes them to each household's terminal.

[1922] Step 5:

[1923] The user checks the instructions received through the device and manually adjusts the settings if necessary.

[1924] ---

[1925] 3. Dynamic pricing of electricity using AI

[1926] Step 1:

[1927] The server collects real-time data on electricity demand and supply within the region.

[1928] Step 2:

[1929] The server uses an AI algorithm to analyze the collected data and calculate the optimal buying and selling price.

[1930] Step 3:

[1931] The server sends the calculated purchase and sale price information to each household's terminal.

[1932] Step 4:

[1933] The device sends push notifications to the user, providing buying and selling price information.

[1934] Step 5:

[1935] The user checks the buying and selling price information on the terminal and decides whether to buy or sell electricity.

[1936] ---

[1937] 4. AI-based data collection and analysis

[1938] Step 1:

[1939] The terminal collects electricity usage data for each household in real time and sends it to a server.

[1940] Step 2:

[1941] The server stores the collected data in a database.

[1942] Step 3:

[1943] The server uses AI algorithms to analyze the data and identify energy consumption patterns.

[1944] Step 4:

[1945] The server generates energy-saving actions based on the analysis results and sends them to the terminal.

[1946] Step 5:

[1947] The device notifies the user of the analysis results and energy-saving actions.

[1948] Step 6:

[1949] The user checks the analysis results on the device and adjusts settings to save energy.

[1950] ---

[1951] 5. Integration of emotion engine that recognizes user emotions

[1952] Step 1:

[1953] The device analyzes the user's facial expressions and voice in real time to generate emotional data.

[1954] Step 2:

[1955] The device transmits the emotion data to the server.

[1956] Step 3:

[1957] The server analyzes the user's emotional state based on the emotional data.

[1958] Step 4:

[1959] The server combines emotional state and power consumption data to identify factors that influence energy consumption patterns.

[1960] ---

[1961] 6. Adjusting energy-saving actions based on emotion data

[1962] Step 1:

[1963] The server analyzes the emotion data and power consumption data and generates energy-saving actions.

[1964] Step 2:

[1965] The server sends energy-saving actions to each household's terminal.

[1966] Step 3:

[1967] The terminal notifies the user of energy-saving actions based on the emotion data.

[1968] Step 4:

[1969] The user checks the proposed energy-saving action on the terminal and decides whether to execute it.

[1970] ---

[1971] The above are the specific processing steps and operation details of the invention combined with the emotion engine.

[1972] Example 2

[1973] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1974] With the end of the Feed-in Tariff (FIT) system in modern society, there is a need to promote the self-consumption of surplus electricity from residential solar power generation while also preparing for power shortages and creating disaster-resistant communities. However, purchasing and installing energy storage devices individually for each household poses challenges, including high costs and the hassle of installation. There are also concerns about reduced efficiency due to suboptimal power usage patterns. Furthermore, there are no systems that take into account the influence of user emotions on power consumption behavior.

[1975] The specification process by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for collecting information on desired installation of household power storage devices, a means for ordering power storage devices in bulk, a means for reserving centralized installation locations for the power storage devices, a means for providing installation progress information, a means for collecting data on power generation and consumption, a means for predicting power generation and consumption based on the collected data, a means for instructing optimal power usage, a means for collecting data on power supply and demand, a means for calculating buying and selling prices, a means for providing calculated price information, a means for collecting and analyzing power usage data, a means for providing the analysis results, a means for collecting emotion data and analyzing factors affecting power usage patterns, and a means for generating and providing energy-saving actions based on the user's emotional state. This reduces the high cost and installation effort for each household, enables optimization of power usage patterns, and efficient energy management. Furthermore, flexible energy-saving actions based on the user's emotions can improve the user experience and quality of life.

[1976] A "home energy storage device" is a device with an energy storage function that is installed in a home and stores surplus electricity generated by solar power generation and other means, and supplies it when needed.

[1977] The "installation preference information" is information indicating that a household wishes to install a power storage device, and includes details such as the number of applicants and the desired installation time.

[1978] "Bulk ordering" refers to ordering multiple household energy storage devices at once, and is an ordering method that offers cost benefits over ordering individually.

[1979] "Concentrated installation site" means a specific location set aside temporarily or permanently for the efficient installation of energy storage devices.

[1980] "Installation progress information" is information indicating whether the installation of the power storage device is proceeding as planned, and is information that provides the progress status of the installation process in real time.

[1981] "Power generation amount" is data indicating the amount of power generated by the photovoltaic power generation system during a certain time period.

[1982] "Consumption" is data indicating the amount of electricity used within the home.

[1983] "Collected Data" refers to various data measured and acquired by the system, such as the amount of electricity generated and consumed.

[1984] "Forecasting" means estimating future power generation and consumption based on collected data.

[1985] "Directing power usage" means proposing and delivering optimal power usage methods to each household based on collected data and predictions.

[1986] "Dynamic pricing" is a pricing method that changes electricity prices based on real-time conditions of electricity supply and demand.

[1987] "Price information" refers to information including pricing and calculation results related to the purchase and sale of electricity.

[1988] "Electricity usage data" refers to historical information on the amount of electricity actually used within a household.

[1989] "Analysis" means the process of analyzing collected data and converting the information into a useful form.

[1990] "Emotion data" is data that indicates the emotional state of the user as determined by their facial expressions and voice.

[1991] "Energy saving actions" refer to specific actions or measures that should be taken to reduce energy consumption.

[1992] "User status" is information that indicates the user's current emotions and behavioral status.

[1993] The above definitions allow a clear understanding of each element of the invention and its function.

[1994] This invention relates to a system that improves the efficiency of home energy storage device installation and power management. This system collects information on energy storage device installation requests from households, reduces costs through bulk ordering, and uses AI to predict and optimize power consumption. It also collects user emotional data and ...

Claims

1. A means of collecting information on battery installation requests from households; A means of ordering batteries in bulk; A means of securing a centralized installation location for storage batteries; a means for providing installation progress information; means for collecting electricity generation and consumption data; a means for predicting power generation and consumption based on the collected data; a means for directing optimal power utilization; a means of collecting electricity supply and demand data; A means for calculating a purchase price; a means for providing the calculated price information; a means for collecting and analyzing electricity usage data; a means for providing the analysis results; A system including:

2. and means for transmitting optimal power usage instructions to a home power-using device. The system of claim 1 .

3. Further includes means for notifying the purchase and sale price information to a home terminal. The system of claim 1 .

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