system

Abstract

We provide the system. [Solution] A means of collecting energy data in real time and analyzing consumption patterns, A means for generating information to visualize the energy consumed, A means for automatically generating recommendations for improving energy efficiency based on analysis results, A method for simulating the effects of introducing renewable energy sources using predictive models, A means to provide a schedule for suppressing peak power consumption and to automatically control the device, A means of optimizing energy consumption in public facilities and infrastructure, and providing information that contributes to energy conservation in daily life, A system that includes this.

Description

Technical Field

[0004] , , , ,

[0005] , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the face of deepening environmental problems and depletion of energy resources, it is becoming increasingly important for individuals and enterprises to achieve sustainable energy use. However, there is a problem that many users have difficulty in performing effective energy management without specialized knowledge and tools for optimizing energy consumption. In addition, the lack of information for effectively utilizing renewable energy is also one of the problems.

Means for Solving the Problems

[0005] This invention provides a system that collects energy data in real time, analyzes it to understand consumption patterns, and displays them visually. Furthermore, it automatically generates recommendations for improving energy efficiency based on the analysis results. It also includes a function to simulate the effects of introducing renewable energy sources using a predictive model. Through these means, peak power consumption can be suppressed and devices can be effectively managed through automatic control. This enables sustainable energy use even without specialized knowledge.

[0006] "Energy data" refers to information about electricity consumption and usage, and is the subject of analysis for energy management and efficiency improvements.

[0007] A "consumption pattern" refers to the temporal and quantitative trends and regularities in electricity consumption over a specific period.

[0008] "Analysis methods" refer to the methods and processes used to extract specific information or insights based on collected data.

[0009] "Visualization" is the process of displaying data and information in a visual format to make it easier for humans to understand.

[0010] "Energy efficiency improvement" refers to optimizing systems and processes to achieve the same effect or performance with less energy.

[0011] "Recommendation" means proposing actions or measures that should be taken to achieve a certain objective.

[0012] "Renewable energy sources" refer to energy sources that can be obtained from the natural environment and are inexhaustible, including solar and wind power.

[0013] A "simulation" is a method of virtually reproducing phenomena or effects based on specific conditions and assumptions, and then analyzing the results.

[0014] A "prediction model" is a method for estimating future events or results using past data and algorithms.

[0015] "Peak-time suppression" refers to reducing consumption during the time period when power demand is at its maximum to achieve efficient energy management.

[0016] "Automatic control" is a method of autonomously managing the operation of devices and systems with minimal human intervention.

Brief Description of Drawings

[0017] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of a data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0018] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0021] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0022] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0023] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] As shown in Figure 1, the 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.

[0028] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0030] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0031] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0037] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0038] This invention provides a system for optimizing the energy consumption of individuals or companies and reducing their environmental impact. This system has the function of collecting energy data in real time, analyzing it, and visualizing consumption patterns. Specific embodiments of this invention are described below.

[0039] The server collects real-time data on power consumption from home and office electricity meters and IoT devices. This data is analyzed on the server to detect consumption trends and abnormal patterns. The analysis results are then organized to visualize the user's energy usage and sent to the terminal.

[0040] The terminal receives data sent from the server and displays it visually through the user interface. This allows users to see at a glance their energy usage and how much power each device is consuming.

[0041] Furthermore, this system has a function to automatically generate recommendations for improving energy efficiency based on the analyzed data. The server learns past consumption patterns and uses machine learning algorithms to propose an optimal energy-saving plan tailored to the user's lifestyle and work situation.

[0042] Furthermore, the server is equipped with a predictive model for simulating the effects of introducing renewable energy sources. Users specify the conditions of their home or office through a terminal, and the server calculates how much energy reduction the introduction of solar power generation and energy storage systems will contribute based on this information, and displays the results on the terminal.

[0043] To reduce power consumption during peak hours, the server identifies peak times based on historical data and information from the power company, and presents a schedule plan to the terminal. This plan works in conjunction with an automatic control function to optimize the operation of devices outside of peak hours.

[0044] Based on the information and recommendations provided through the device, users can manage their energy consumption appropriately to suit their lifestyle and business operations. This promotes the use of sustainable energy for society as a whole and contributes to reducing environmental impact.

[0045] The following describes the processing flow.

[0046] Step 1:

[0047] The server receives real-time power consumption data from electricity meters and IoT devices installed in homes and offices. The received data is organized by device and stored in an internal database.

[0048] Step 2:

[0049] The server analyzes the accumulated data and detects consumption patterns. In particular, algorithms are applied to identify anomalies, and consumption trends and peak times are identified. The analysis results are used for subsequent processing.

[0050] Step 3:

[0051] The terminal receives analysis results from the server and displays them in a format that is easy for the user to understand. This includes visual displays such as graphs and charts. Users can view these to understand their own consumption patterns.

[0052] Step 4:

[0053] The server uses machine learning algorithms based on past consumption data to generate an optimal energy-saving plan for the user. The generated plan is designed to be easily implemented by the user and is offered as multiple options.

[0054] Step 5:

[0055] The terminal notifies the user of power-saving plans provided by the server and presents an interface that allows the user to select a plan. The user can then review this, select an appropriate plan, and implement it.

[0056] Step 6:

[0057] The server performs calculations based on user-specified conditions to simulate the effects of introducing renewable energy sources. It visualizes how effective solar power generation and energy storage systems are and sends the results to the terminal.

[0058] Step 7:

[0059] The terminal visually presents the transmitted simulation results to the user, explaining the benefits of implementation and the potential for cost reduction. Based on this, the user can then decide whether or not to implement the system.

[0060] Step 8:

[0061] The server identifies peak power demand times and uses this to propose an optimal operating schedule for the devices. It then uses automatic control functions to adjust device operation and avoid peak power consumption.

[0062] Step 9:

[0063] The user can review the proposed schedule via the terminal and authorize the automatic control function to adjust the device's operation. Manual adjustments can also be made as needed.

[0064] (Example 1)

[0065] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0066] With increasing energy consumption, individuals and businesses face challenges in efficient energy management and sustainable resource utilization. Conventional systems do not adequately collect energy data in real time or analyze consumption patterns, making it difficult to immediately confirm concrete suggestions for improving energy efficiency or the effectiveness of introducing sustainable energy sources. This invention aims to solve such challenges in energy management.

[0067] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0068] In this invention, the server includes means for collecting energy data over time and analyzing consumption trends, means for creating information for visualizing the use of consumed energy, and means for automatically generating recommendations to improve energy utilization efficiency based on the analysis results. This enables real-time collection and analysis of energy data, efficient energy management, and effective utilization of sustainable energy sources.

[0069] "Energy data" refers to quantitative information about energy use and consumption, including data that quantifies the consumption status of electricity, gas, water, and other resources.

[0070] "Time-based data collection" refers to the process of continuously acquiring data in real time or at a specific frequency, with the aim of always maintaining up-to-date information.

[0071] "Analyzing consumption trends" refers to using collected energy data to clarify usage patterns and fluctuation trends using statistical methods and algorithms.

[0072] "Creating information for visualization" is the process of preparing data based on analysis results to be displayed in formats such as graphs and charts so that users can understand them intuitively.

[0073] "Automatically generating recommendations to improve utilization efficiency" means automatically presenting optimal action plans and strategies to reduce energy waste and improve efficiency, taking into account analytical data and user patterns.

[0074] "Using a prediction device to virtually estimate the effects of introducing sustainable energy sources" means using a simulation tool to estimate how much the introduction of sustainable energy (e.g., solar power) will contribute to actual energy reduction.

[0075] "Providing a timetable and automatically operating the equipment" refers to programming the operating schedule of equipment to reduce energy consumption during peak hours, and automatically managing the equipment's operation based on the results.

[0076] This invention provides an integrated system for optimizing energy management and promoting sustainable use. This system includes functions for collecting and analyzing energy data in real time and presenting the data visually to the user.

[0077] The server collects power consumption data from various devices, including smart meters and IoT devices, which send data to the server via network connectivity. The server uses Python's Pandas and NumPy libraries to organize and analyze the data. Statistical algorithms are used to identify consumption trends and detect outliers.

[0078] The analysis results are organized as a dashboard on the server to help users understand them. This visualization uses tools such as Matplotlib and D3.js to generate graphs and charts. The device displays the received data, allowing users to intuitively grasp their consumption patterns.

[0079] Furthermore, the server uses a generative AI model to learn from past consumption data and provides users with recommendations to improve energy efficiency. This algorithm utilizes technologies such as TENSORFLOW® and PyTorch. These recommendations are customized based on actual consumption trends.

[0080] Users input their home or office conditions into the system via a terminal, and the server simulates the effects of introducing renewable energy sources. For example, by entering a prompt such as "Annual electricity savings if 3kW solar panels are installed on the roof of my house," the effect will be estimated.

[0081] Furthermore, the server identifies peak hours based on historical consumption data and external information, and the terminal provides the user with a schedule for efficient energy use. This schedule allows the user to properly operate their equipment to reduce consumption during peak hours.

[0082] In this way, this system, designed with practical use in mind, can promote the efficient and sustainable use of energy and contribute to reducing environmental impact.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] The server collects power consumption data in real time from smart meters and IoT devices. These devices transmit consumption data to the server via a network connection. The input is power consumption data from each device. The server stores this data in a database and manages it chronologically to understand the total amount of consumption. The output is an aggregate of the raw data.

[0086] Step 2:

[0087] The server analyzes the collected power consumption data. It performs data cleansing using Python's Pandas and NumPy libraries to remove outliers. The input is the data aggregated in step 1. Time series analysis and anomaly detection algorithms are applied to identify consumption trends and anomalous patterns. The output is the cleansed time series data and the detected consumption trends.

[0088] Step 3:

[0089] The server generates visualization data for the user based on the analysis results. The input is the analysis results from step 2. Using tools such as Matplotlib and D3.js, it creates graphs and charts of consumption patterns. The output is the visualized data, which is sent from the server to the terminal.

[0090] Step 4:

[0091] The terminal displays the received visualization data on the user interface. The input is the visualization data generated in step 3. This allows the user to grasp the consumption status and consumption trends of each device at a glance. The output is the analysis results displayed on the dashboard.

[0092] Step 5:

[0093] The server generates recommendations to improve energy efficiency using a generative AI model. The input consists of historical consumption data and current usage. It leverages machine learning frameworks such as TensorFlow and PyTorch to generate efficient energy management plans. The output is a customized recommendation plan, which is also sent to the terminal.

[0094] Step 6:

[0095] The user enters a prompt message via the terminal to estimate the effectiveness of introducing sustainable energy sources. For example, one example is "the effect of installing a 3kW solar panel at home." The input is this prompt message. The server uses a simulation model to estimate the effectiveness of the introduction under the specified conditions. The output is the calculation result.

[0096] Step 7:

[0097] The server generates an optimal schedule based on historical data and power company information to reduce peak power consumption. Inputs are historical consumption data and power supply information. This allows the server to provide a consumption schedule that avoids peak times. The output is a control plan.

[0098] Step 8:

[0099] The terminal presents the user with recommended plans and schedules sent from the server. Based on this information, the user can streamline their daily energy management. The input becomes output data from the server, providing crucial information for understanding the user's behavior. The output is an energy usage plan that the user can adjust based on their individual needs.

[0100] (Application Example 1)

[0101] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0102] Energy consumption in public facilities and infrastructure is not yet sufficiently optimized, making conscious energy conservation in daily life difficult. Furthermore, there is a lack of effective means to suppress peak electricity consumption and promote sustainable energy use.

[0103] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0104] This invention includes a server that collects energy data in real time and analyzes consumption patterns, generates information for visualizing consumed energy, simulates the effects of introducing renewable energy sources using a predictive model, and provides information that optimizes energy consumption in public facilities and infrastructure and contributes to energy conservation in daily life. This enables more efficient energy use and the realization of sustainable energy use.

[0105] "Energy data" refers to data that shows information about the electricity used by individuals, public facilities, or infrastructure.

[0106] "Real-time data collection" refers to the process of acquiring energy data instantly and without delay.

[0107] "Analyzing consumption patterns" is the act of analyzing collected data to reveal trends and characteristics in electricity usage.

[0108] "Information for visualization" refers to data that provides the results of energy data analysis in a visually easy-to-understand format.

[0109] A "predictive model" is an algorithm that predicts future energy consumption and the effects of renewable energy based on past data.

[0110] "Renewable energy sources" refer to energy derived from nature, such as solar and wind power, that can be used in a sustainable manner.

[0111] "Simulating the effects of implementation" means calculating the effects of utilizing renewable energy sources in a simulated manner.

[0112] "Suppressing peak electricity consumption" refers to management aimed at reducing consumption during the time when electricity demand is highest.

[0113] "Optimizing energy consumption for public facilities and infrastructure" is the process of efficiently managing energy use throughout a city and reducing waste.

[0114] "Information that contributes to energy conservation in daily life" refers to useful data and suggestions for individuals to use energy efficiently in their daily activities.

[0115] This invention is a system that efficiently manages energy consumption and promotes sustainable energy use in public facilities and infrastructure. In this system, a server plays a major role and has multiple functions.

[0116] First, the server collects energy data in real time from public facilities, private homes, and other locations. IoT devices and smart electricity meters are used to ensure reliable data acquisition. The collected data is then stored in a database.

[0117] Next, the server analyzes the collected data to reveal consumption patterns. This analysis applies machine learning algorithms to recognize patterns and detect anomalies. The analysis results are also used as input data for predictive models that simulate the effects of introducing renewable energy.

[0118] Users receive analysis results sent from the server via their terminals and can view them as visualized information about their energy usage. Based on this information, users receive recommendations for efficient energy use. Furthermore, the server generates and provides users with specific plans to optimize energy consumption in public facilities and infrastructure.

[0119] The server also provides a schedule to reduce peak power consumption. This schedule works in conjunction with the device's automatic control function to ensure power is used during the most efficient times. It also leverages generative AI models to provide advice for further energy optimization.

[0120] As a concrete example, if it is determined that the peak electricity consumption in a public facility in a certain city is between 2 PM and 4 PM, the server will provide a plan to efficiently control lighting and air conditioning equipment to avoid that time slot. This process uses a generative AI model and prompts such as, "Analyze energy consumption pattern data in the city and generate predictions for peak hours and the effects of renewable energy introduction."

[0121] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0122] Step 1:

[0123] The server collects energy data in real time from smart electricity meters and IoT devices. The input consists of numerical data on power consumption from each device, which is stored in a database. The server then organizes the collected raw data as time-series data.

[0124] Step 2:

[0125] The server applies machine learning algorithms to analyze the data. The input is the time-series data collected in step 1. The analysis includes recognizing consumption patterns and detecting anomalies. The server uses this data to model data trends and generate a consumption pattern report.

[0126] Step 3:

[0127] The server automatically generates recommendations to improve energy efficiency based on the analysis results. The input is the consumption pattern report obtained in step 2. The server uses the generated AI model to formulate an optimization plan and creates an output that summarizes suggestions for saving electricity and using energy efficiently.

[0128] Step 4:

[0129] The server uses a predictive model to simulate the effects of introducing renewable energy sources. The inputs are historical consumption pattern data and characteristic information of renewable energy sources. Based on this data, the server outputs simulation results that quantify energy savings and cost reductions.

[0130] Step 5:

[0131] The server generates and sends to the terminal a plan to optimize the energy consumption of public facilities and infrastructure. The input is the optimization plan and simulation results obtained in the previous step, which the server organizes and provides to the terminal as visualized data that is easy for the user to understand.

[0132] Step 6:

[0133] The user receives data from the server via their device to check their energy usage and recommendations. The input is the visualized data sent in step 5. Based on the displayed information, the user manages their energy consumption and considers specific actions to save electricity.

[0134] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0135] This invention provides a system that integrates energy data and user emotional states to optimize energy management. By incorporating an emotion engine, this system understands the user's emotions and proposes corresponding improvements to energy efficiency. The system configuration and operation are described below as specific embodiments.

[0136] The server first collects energy data in real time from home and office electricity meters and IoT devices. This data allows the server to understand the power consumption of each device and analyze consumption patterns. During the analysis process, machine learning algorithms are used to detect consumption trends and anomaly patterns.

[0137] A key feature of this system is its use of an emotion engine to recognize user emotions in real time. For example, if a user is stressed, the emotion engine detects this state and reflects it in the energy management plan. When a user is relaxed, the system can proactively offer energy-saving suggestions. This data is integrated with energy data and used for analysis on the server.

[0138] The device provides information through a user-friendly interface, based on energy analysis results obtained from the server and information from the emotion engine. Through this, users can view an energy management plan tailored to their emotional state. For example, if the user is fatigued, the device might suggest adjusting the room temperature to a comfortable level to promote relaxation.

[0139] The server further simulates the effects of introducing renewable energy based on predictive models. This helps users understand which renewable energy solutions are best suited to their environment. The emotion engine also plays a role here, making suggestions that match the user's interests and motivations.

[0140] To reduce power consumption during peak hours, the server generates a schedule that takes into account historical data and the user's emotional state, and automatically controls the device. If the user feels particularly busy, the device can emphasize load-reducing measures through automatic control.

[0141] By incorporating an emotional engine in this way, the system can go beyond mere energy management and provide intelligent support that improves the user's quality of life.

[0142] The following describes the processing flow.

[0143] Step 1:

[0144] The server collects real-time power consumption data from home and office electricity meters and various IoT devices. This includes detailed measurement data on the power consumption and usage of each device.

[0145] Step 2:

[0146] The server analyzes the collected energy data to identify the consumption patterns of each device. By applying machine learning algorithms, it detects abnormal consumption patterns and peak usage times.

[0147] Step 3:

[0148] The server uses an emotion engine to collect user emotion data from the terminal camera and voice recognition devices. This includes data obtained through facial expression recognition and voice tone analysis.

[0149] Step 4:

[0150] The server integrates energy and emotional data to generate recommendations for improving energy efficiency based on the user's emotional state. For example, if the user is feeling stressed, it will suggest dimming the lights to provide a calming environment.

[0151] Step 5:

[0152] The device receives analysis results and recommendations from the server and presents them in a user-friendly interface. Using visual graphs and charts, users can review suggestions based on their own consumption patterns and emotions.

[0153] Step 6:

[0154] Users select an energy management plan based on the information presented. If they feel fatigued, they can adjust the room temperature or optimize lighting according to the device's suggestions to reduce stress.

[0155] Step 7:

[0156] The server simulates and evaluates the effects of introducing renewable energy. Based on specific conditions, it calculates cost reductions for solar power generation and energy storage systems, and sends the results to the terminal.

[0157] Step 8:

[0158] The terminal presents the user with simulation results, helping them understand the benefits and cost-effectiveness of renewable energy. If necessary, the user can consider implementing the system.

[0159] Step 9:

[0160] The server generates an optimal schedule to avoid peak power demand times and automatically controls devices based on this schedule. It also manages energy consumption in a way that minimizes stress, taking into account the user's emotional state.

[0161] Step 10:

[0162] Users monitor the automatically controlled system, manually fine-tune settings as needed, and achieve efficient energy use while improving their quality of life.

[0163] (Example 2)

[0164] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0165] Modern energy management systems often make suggestions based solely on consumption data to improve efficiency. However, it has been pointed out that users' emotional states influence energy consumption, so there is a need to optimize energy efficiency while considering emotional states. Furthermore, it is also a challenge to grasp the effects of introducing renewable energy in real time and to make suggestions tailored to the individual circumstances of each user.

[0166] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0167] In this invention, the server includes means for collecting energy data in real time and analyzing consumption patterns, means for recognizing emotional states and reflecting them in energy management plans, and means for simulating the effects of introducing renewable energy sources using predictive models. This enables effective energy management and improved quality of life by providing optimal energy-efficient suggestions tailored to the user's emotional state.

[0168] "Energy data" refers to information related to electricity and other energy consumption, showing the usage of various devices and systems in home and office environments.

[0169] "Real-time" refers to acquiring information instantly and processing it without delay, resulting in a state where information responds immediately to user actions and changes in the environment.

[0170] "Consumption patterns" refer to regularities in how energy and other resources are used, and describe usage trends based on past data.

[0171] "Analysis" refers to the process of examining data and information in detail to reveal its structure and characteristics, and in particular, it includes identifying trends and anomalies in energy data.

[0172] "Automatic generation" refers to a system creating new data or information on its own based on algorithms and rules.

[0173] A "predictive model" refers to a method or algorithm that uses historical data and statistical techniques to estimate future trends and outcomes.

[0174] "Renewable energy sources" refer to inexhaustible and indestructible energy sources found in nature, such as solar, wind, hydro, and geothermal energy.

[0175] A "simulation" is a method of analyzing the impact of specific conditions or variables on the outcome by imitating actual operations.

[0176] A "schedule" refers to a detailed plan or schedule outlining when and how specific activities or operations will be carried out.

[0177] "Emotional state" refers to the user's mental and emotional changes, including states such as stress and relaxation.

[0178] "Energy efficiency suggestions" refer to recommended actions and settings aimed at optimizing users' energy consumption and reducing waste.

[0179] This invention provides a system that comprehensively analyzes energy data and the user's emotional state in order to optimize energy management. A specific embodiment is configured as follows.

[0180] The server collects energy data in real time from home and office electricity meters and IoT devices. This includes devices such as smart meters and network-connected temperature control devices. The server receives this data and performs data analysis using machine learning libraries such as TensorFlow. This analysis models the consumption patterns of each device, enabling the detection of trends and anomalies.

[0181] The emotion engine also collects emotional information from wearable devices worn by the user. The devices measure biosignals such as heart rate and skin temperature and transmit this information to the emotion engine. The emotion engine analyzes this information to understand the user's stress level and relaxation level.

[0182] The terminal proposes an energy management plan tailored to the user based on energy analysis results obtained from the server and emotional information from the emotion engine. This interface is user-friendly; for example, if it receives data indicating the user is fatigued, it will suggest room temperature settings and lighting adjustments to promote relaxation.

[0183] In addition, the server has the capability to use predictive models to simulate the effects of introducing renewable energy. For example, it can show users which type of renewable energy is best suited to their environmental conditions. It can also utilize data from an emotion engine to provide suggestions based on user interests.

[0184] For example, if a user returns home from work feeling stressed, the system can help create a relaxing environment by changing the lighting to a warmer color or setting the room temperature to a comfortable level. An example of a prompt to input into the generating AI model would be, "Generate suggestions for optimal energy use and emotional responses for the following time period."

[0185] Thus, this system aims to provide intelligent support that contributes not only to energy management but also to improving the quality of life for users.

[0186] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0187] Step 1:

[0188] The server collects energy data in real time from electricity meters and IoT devices in homes and offices. Inputs are power consumption data from smart meters and networked temperature control devices, and outputs are real-time energy consumption records compiled from this data. Specifically, the server periodically accesses devices, retrieves power consumption figures, and stores them in a database.

[0189] Step 2:

[0190] The server analyzes the collected energy data. The input is the power consumption records obtained in step 1, and the output is the consumption patterns of each device and anomaly detection information. Here, machine learning libraries such as TensorFlow are used to analyze data trends and detect abnormal consumption behavior.

[0191] Step 3:

[0192] The user wears a wearable device for emotion recognition and provides data on their emotional state. The input is biosignal information such as heart rate and skin temperature obtained from the device, and the output is the user's emotional state (stress level and relaxation level). Specifically, the device sends the collected data to an emotion engine, where the data is analyzed.

[0193] Step 4:

[0194] The server receives emotional state data from the emotion engine and integrates it with energy data. The input is the user's emotional state data and energy consumption patterns, and the output is a pre-adjusted energy management plan tailored to the user's state. The server fuses this data to create an optimal plan based on the user's emotions.

[0195] Step 5:

[0196] The terminal receives integrated analysis results from the server and displays an energy management plan tailored to the user. The input is the analysis results from the server, and the output is an energy efficiency suggestion in a user-friendly format. Specifically, the terminal provides suggestions for lighting settings and temperature adjustments based on the user's situation, both on screen and via audio.

[0197] Step 6:

[0198] The server simulates the effects of introducing renewable energy using a predictive model and proposes the best energy solution to the user based on the results. The input is the conditions for introducing renewable energy and environmental data, and the output is the simulation results of the introduction effects. Based on the simulation results, the server makes suggestions that enable the user to utilize renewable energy most effectively.

[0199] (Application Example 2)

[0200] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0201] Conventional energy management systems simply aim to improve energy efficiency based on consumption data, without considering the individual emotional states of users. Therefore, they fail to provide energy management that aligns with user comfort and motivation, resulting in a lack of more efficient and effective energy usage methods. This invention aims to solve these problems.

[0202] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0203] In this invention, the server includes means for collecting energy data in real time and analyzing consumption patterns, means for generating information to visualize the consumed energy, means for automatically generating recommendations for improving energy efficiency based on the analysis results, and means for sensing the user's emotional state and adjusting the energy management plan accordingly. This enables more personalized energy management suggestions that take the user's emotional state into consideration.

[0204] "Energy data" refers to data showing the usage of various power-consuming devices in homes and offices.

[0205] "Consumption patterns" refer to specific tendencies or habits that indicate the times of day and situations in which energy is used.

[0206] "Means of real-time collection" refers to technological means for instantly collecting energy data and understanding current usage patterns.

[0207] "Means for automatically generating recommendations for improving energy efficiency" refers to technology that mechanically derives specific suggestions for optimizing energy use based on analyzed data.

[0208] "A method for simulating the effects of introducing renewable energy sources using predictive models" refers to a process of calculating the expected changes in efficiency and cost resulting from the utilization of renewable energy sources.

[0209] "Means of providing schedules to curb peak power consumption" refers to technologies that limit power use during periods of high demand and create plans for efficient energy utilization.

[0210] "Means for automatically controlling devices" refers to a system that automatically adjusts the operation of equipment based on pre-set conditions.

[0211] "Means of sensing the user's emotional state and adjusting the energy management plan accordingly" refers to technology that evaluates the user's mental and emotional state and customizes energy usage suggestions based on that data.

[0212] This invention is a system that optimizes energy management based on energy data and the user's emotional state. The server collects energy data in real time from various power-consuming devices in homes and offices. This data is used to analyze consumption patterns, and machine learning algorithms such as TensorFlow are applied to the analysis process. The server also has an emotion engine to sense the user's emotional state and collects data using heart rate and facial recognition technology.

[0213] This system provides automatically generated recommendations for improving energy efficiency. For example, based on analyzed data, the server may suggest adjusting room temperature or lighting settings. It can also use predictive models to simulate the effects of introducing renewable energy and visualize energy savings and cost fluctuations. In this way, a schedule is created to reduce peak power consumption, and devices are automatically controlled.

[0214] Users receive energy management suggestions based on their emotional state through the app. This allows for the efficient use of energy resources while improving comfort in daily life. For example, a user returning home after being out for a long time might have the lighting automatically changed to a warmer color to help alleviate stress.

[0215] An example of a prompt to a generative AI model is, "Based on the user's emotional state upon returning home, how can we provide the optimal indoor environment?" By using such prompts, the system can suggest energy management strategies optimized for the user.

[0216] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0217] Step 1:

[0218] The server collects energy data from each device in real time. The collected data is instantly stored in a database and used as input to understand the current power consumption of each device. The data processing performed at this stage involves organizing the collected power usage as time-series data and checking for any outliers.

[0219] Step 2:

[0220] The server analyzes the collected energy data and derives consumption patterns. Using machine learning algorithms, it identifies normal and abnormal consumption patterns and outputs the results as an analysis report. Here, it performs trend analysis by comparing with past data to identify peak times and periods when energy conservation is possible.

[0221] Step 3:

[0222] The server uses an emotion engine to detect the user's emotional state. It obtains heart rate and facial expression data from the user's smartphone sensors and uses this as input to infer the emotional state. The inference results are then used as input for specific action suggestions.

[0223] Step 4:

[0224] The server integrates the analysis results of energy data with the output of the emotion engine to generate recommendations for improving energy efficiency. This process utilizes a generative AI model to generate optimal suggestions tailored to the user's mood. These suggestions may include adjusting the temperature or lighting within the home.

[0225] Step 5:

[0226] The terminal provides the user with recommendations obtained from the server. The user receives the suggestions through the application and changes the settings as needed. Specifically, they can choose to manually adjust the room temperature setting or allow automatic control.

[0227] Step 6:

[0228] Users submit feedback via the terminal interface to evaluate the effectiveness of the recommendations. This feedback is collected by the server and used to improve the accuracy of future data analysis, as it helps improve the overall system.

[0229] These processing steps enable a highly efficient energy management system that takes into account the user's emotional state.

[0230] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0231] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0232] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0233] [Second Embodiment]

[0234] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0235] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0236] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0237] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0238] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0239] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0240] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0241] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0242] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0243] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0244] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0245] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0246] This invention provides a system for optimizing the energy consumption of individuals or companies and reducing their environmental impact. This system has the function of collecting energy data in real time, analyzing it, and visualizing consumption patterns. Specific embodiments of this invention are described below.

[0247] The server collects real-time data on power consumption from home and office electricity meters and IoT devices. This data is analyzed on the server to detect consumption trends and abnormal patterns. The analysis results are then organized to visualize the user's energy usage and sent to the terminal.

[0248] The terminal receives data sent from the server and displays it visually through the user interface. This allows users to see at a glance their energy usage and how much power each device is consuming.

[0249] Furthermore, this system has a function to automatically generate recommendations for improving energy efficiency based on the analyzed data. The server learns past consumption patterns and uses machine learning algorithms to propose an optimal energy-saving plan tailored to the user's lifestyle and work situation.

[0250] Furthermore, the server is equipped with a predictive model for simulating the effects of introducing renewable energy sources. Users specify the conditions of their home or office through a terminal, and the server calculates how much energy reduction the introduction of solar power generation and energy storage systems will contribute based on this information, and displays the results on the terminal.

[0251] To reduce power consumption during peak hours, the server identifies peak times based on historical data and information from the power company, and presents a schedule plan to the terminal. This plan works in conjunction with an automatic control function to optimize the operation of devices outside of peak hours.

[0252] Based on the information and recommendations provided through the device, users can manage their energy consumption appropriately to suit their lifestyle and business operations. This promotes the use of sustainable energy for society as a whole and contributes to reducing environmental impact.

[0253] The following describes the processing flow.

[0254] Step 1:

[0255] The server receives real-time power consumption data from electricity meters and IoT devices installed in homes and offices. The received data is organized by device and stored in an internal database.

[0256] Step 2:

[0257] The server analyzes the accumulated data and detects consumption patterns. In particular, algorithms are applied to identify anomalies, and consumption trends and peak times are identified. The analysis results are used for subsequent processing.

[0258] Step 3:

[0259] The terminal receives analysis results from the server and displays them in a format that is easy for the user to understand. This includes visual displays such as graphs and charts. Users can view these to understand their own consumption patterns.

[0260] Step 4:

[0261] The server uses machine learning algorithms based on past consumption data to generate an optimal energy-saving plan for the user. The generated plan is designed to be easily implemented by the user and is offered as multiple options.

[0262] Step 5:

[0263] The terminal notifies the user of power-saving plans provided by the server and presents an interface that allows the user to select a plan. The user can then review this, select an appropriate plan, and implement it.

[0264] Step 6:

[0265] The server performs calculations based on user-specified conditions to simulate the effects of introducing renewable energy sources. It visualizes how effective solar power generation and energy storage systems are and sends the results to the terminal.

[0266] Step 7:

[0267] The terminal visually presents the transmitted simulation results to the user, explaining the benefits of implementation and the potential for cost reduction. Based on this, the user can then decide whether or not to implement the system.

[0268] Step 8:

[0269] The server identifies peak power demand times and uses this to propose an optimal operating schedule for the devices. It then uses automatic control functions to adjust device operation and avoid peak power consumption.

[0270] Step 9:

[0271] The user can review the proposed schedule via the terminal and authorize the automatic control function to adjust the device's operation. Manual adjustments can also be made as needed.

[0272] (Example 1)

[0273] Next, we will describe Example 1. 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."

[0274] With increasing energy consumption, individuals and businesses face challenges in efficient energy management and sustainable resource utilization. Conventional systems do not adequately collect energy data in real time or analyze consumption patterns, making it difficult to immediately confirm concrete suggestions for improving energy efficiency or the effectiveness of introducing sustainable energy sources. This invention aims to solve such challenges in energy management.

[0275] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0276] In this invention, the server includes means for collecting energy data over time and analyzing consumption trends, means for creating information for visualizing the use of consumed energy, and means for automatically generating recommendations to improve energy utilization efficiency based on the analysis results. This enables real-time collection and analysis of energy data, efficient energy management, and effective utilization of sustainable energy sources.

[0277] "Energy data" refers to quantitative information about energy use and consumption, including data that quantifies the consumption status of electricity, gas, water, and other resources.

[0278] "Time-based data collection" refers to the process of continuously acquiring data in real time or at a specific frequency, with the aim of always maintaining up-to-date information.

[0279] "Analyzing consumption trends" refers to using collected energy data to clarify usage patterns and fluctuation trends using statistical methods and algorithms.

[0280] "Creating information for visualization" is the process of preparing data based on analysis results to be displayed in formats such as graphs and charts so that users can understand them intuitively.

[0281] "Automatically generating recommendations to improve utilization efficiency" means automatically presenting optimal action plans and strategies to reduce energy waste and improve efficiency, taking into account analytical data and user patterns.

[0282] "Using a prediction device to virtually estimate the effects of introducing sustainable energy sources" means using a simulation tool to estimate how much the introduction of sustainable energy (e.g., solar power) will contribute to actual energy reduction.

[0283] "Providing a schedule and automatically operating devices" refers to programming the operating schedule of devices and automatically managing the operations of the devices based on the results in order to suppress peak energy consumption.

[0284] The present invention provides an integrated system for optimizing energy management and promoting sustainable use. This system includes the function of collecting and analyzing energy data in real time and visually presenting the data to the user.

[0285] The server collects power consumption data from various devices. This includes smart meters and IoT devices, which transmit data to the server using network connections. The server uses Python's Pandas and NumPy libraries to organize and analyze the data. Statistical algorithms are used for consumption trend and outlier detection.

[0286] The analysis results are organized as a dashboard on the server to assist the user's understanding. Graphs and charts are generated using tools such as Matplotlib and D3.js for this visualization. The terminal displays the received data, allowing the user to intuitively grasp the consumption situation.

[0287] Furthermore, the server uses a generative AI model to learn past consumption data and provides recommendations to the user for improving energy efficiency. TensorFlow and PyTorch are used in this algorithm. These recommendations are customized based on actual consumption trends.

[0288] The user inputs the conditions of their home or office into the system through the terminal, and the server simulates the effect of introducing renewable energy sources. For example, by inputting a prompt sentence such as "Annual power reduction amount when installing a 3kW solar panel on the roof of the house", the effect is estimated.

[0289] Furthermore, the server identifies peak hours based on historical consumption data and external information, and the terminal provides the user with a schedule for efficient energy use. This schedule allows the user to properly operate their equipment to reduce consumption during peak hours.

[0290] In this way, this system, designed with practical use in mind, can promote the efficient and sustainable use of energy and contribute to reducing environmental impact.

[0291] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0292] Step 1:

[0293] The server collects power consumption data in real time from smart meters and IoT devices. These devices transmit consumption data to the server via a network connection. The input is power consumption data from each device. The server stores this data in a database and manages it chronologically to understand the total amount of consumption. The output is an aggregate of the raw data.

[0294] Step 2:

[0295] The server analyzes the collected power consumption data. It performs data cleansing using Python's Pandas and NumPy libraries to remove outliers. The input is the data aggregated in step 1. Time series analysis and anomaly detection algorithms are applied to identify consumption trends and anomalous patterns. The output is the cleansed time series data and the detected consumption trends.

[0296] Step 3:

[0297] The server generates visualization data for the user based on the analysis results. The input is the analysis results from step 2. Using tools such as Matplotlib and D3.js, it creates graphs and charts of consumption patterns. The output is the visualized data, which is sent from the server to the terminal.

[0298] Step 4:

[0299] The terminal displays the received visualization data on the user interface. The input is the visualization data generated in step 3. This allows the user to grasp the consumption status and consumption trends of each device at a glance. The output is the analysis results displayed on the dashboard.

[0300] Step 5:

[0301] The server generates recommendations to improve energy efficiency using a generative AI model. The input consists of historical consumption data and current usage. It leverages machine learning frameworks such as TensorFlow and PyTorch to generate efficient energy management plans. The output is a customized recommendation plan, which is also sent to the terminal.

[0302] Step 6:

[0303] The user enters a prompt message via the terminal to estimate the effectiveness of introducing sustainable energy sources. For example, one example is "the effect of installing a 3kW solar panel at home." The input is this prompt message. The server uses a simulation model to estimate the effectiveness of the introduction under the specified conditions. The output is the calculation result.

[0304] Step 7:

[0305] The server generates an optimal schedule based on past data and power company information to suppress peak power consumption. The input is past consumption data and power supply information. Thereby, a consumption schedule that avoids peak times is provided to the terminal. The output is a control plan.

[0306] Step 8:

[0307] The terminal presents the recommended plan and schedule transmitted from the server to the user. Based on this information, the user can optimize daily energy management. The input is the output data from the server, which is important information for deepening the understanding of the user's behavior. The output is an energy utilization plan that can be adjusted by the user based on individual needs.

[0308] (Application Example 1)

[0309] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0310] The optimization of energy consumption in public facilities and infrastructure is not sufficient, and there is a current situation where it is difficult to consciously save electricity in personal life. Also, there is a lack of means to effectively suppress peak power consumption and promote sustainable energy use.

[0311] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0312] In this invention, the server includes means for collecting energy data in real time and analyzing consumption patterns, means for generating information for visualizing the consumed energy, means for simulating the introduction effect of renewable energy sources using a prediction model, and means for optimizing the energy consumption of public facilities and infrastructure and providing information contributing to energy conservation in life. Thereby, it becomes possible to improve the efficiency of energy utilization and realize sustainable energy use.

[0313] "Energy data" refers to data that shows information about the electricity used by individuals, public facilities, or infrastructure.

[0314] "Real-time data collection" refers to the process of acquiring energy data instantly and without delay.

[0315] "Analyzing consumption patterns" is the act of analyzing collected data to reveal trends and characteristics in electricity usage.

[0316] "Information for visualization" refers to data that provides the results of energy data analysis in a visually easy-to-understand format.

[0317] A "predictive model" is an algorithm that predicts future energy consumption and the effects of renewable energy based on past data.

[0318] "Renewable energy sources" refer to energy derived from nature, such as solar and wind power, that can be used in a sustainable manner.

[0319] "Simulating the effects of implementation" means calculating the effects of utilizing renewable energy sources in a simulated manner.

[0320] "Suppressing peak electricity consumption" refers to management aimed at reducing consumption during the time when electricity demand is highest.

[0321] "Optimizing energy consumption for public facilities and infrastructure" is the process of efficiently managing energy use throughout a city and reducing waste.

[0322] "Information that contributes to energy conservation in daily life" refers to useful data and suggestions for individuals to use energy efficiently in their daily activities.

[0323] This invention is a system that efficiently manages energy consumption and promotes sustainable energy use in public facilities and infrastructure. In this system, a server plays a major role and has multiple functions.

[0324] First, the server collects energy data in real time from public facilities, private homes, and other locations. IoT devices and smart electricity meters are used to ensure reliable data acquisition. The collected data is then stored in a database.

[0325] Next, the server analyzes the collected data to reveal consumption patterns. This analysis applies machine learning algorithms to recognize patterns and detect anomalies. The analysis results are also used as input data for predictive models that simulate the effects of introducing renewable energy.

[0326] Users receive analysis results sent from the server via their terminals and can view them as visualized information about their energy usage. Based on this information, users receive recommendations for efficient energy use. Furthermore, the server generates and provides users with specific plans to optimize energy consumption in public facilities and infrastructure.

[0327] The server also provides a schedule to reduce peak power consumption. This schedule works in conjunction with the device's automatic control function to ensure power is used during the most efficient times. It also leverages generative AI models to provide advice for further energy optimization.

[0328] As a concrete example, if it is determined that the peak electricity consumption in a public facility in a certain city is between 2 PM and 4 PM, the server will provide a plan to efficiently control lighting and air conditioning equipment to avoid that time slot. This process uses a generative AI model and prompts such as, "Analyze energy consumption pattern data in the city and generate predictions for peak hours and the effects of renewable energy introduction."

[0329] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0330] Step 1:

[0331] The server collects energy data in real time from smart electricity meters and IoT devices. The input consists of numerical data on power consumption from each device, which is stored in a database. The server then organizes the collected raw data as time-series data.

[0332] Step 2:

[0333] The server applies machine learning algorithms to analyze the data. The input is the time-series data collected in step 1. The analysis includes recognizing consumption patterns and detecting anomalies. The server uses this data to model data trends and generate a consumption pattern report.

[0334] Step 3:

[0335] The server automatically generates recommendations to improve energy efficiency based on the analysis results. The input is the consumption pattern report obtained in step 2. The server uses the generated AI model to formulate an optimization plan and creates an output that summarizes suggestions for saving electricity and using energy efficiently.

[0336] Step 4:

[0337] The server uses a predictive model to simulate the effects of introducing renewable energy sources. The inputs are historical consumption pattern data and characteristic information of renewable energy sources. Based on this data, the server outputs simulation results that quantify energy savings and cost reductions.

[0338] Step 5:

[0339] The server generates and sends to the terminal a plan to optimize the energy consumption of public facilities and infrastructure. The input is the optimization plan and simulation results obtained in the previous step, which the server organizes and provides to the terminal as visualized data that is easy for the user to understand.

[0340] Step 6:

[0341] The user receives data from the server via their device to check their energy usage and recommendations. The input is the visualized data sent in step 5. Based on the displayed information, the user manages their energy consumption and considers specific actions to save electricity.

[0342] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0343] This invention provides a system that integrates energy data and user emotional states to optimize energy management. By incorporating an emotion engine, this system understands the user's emotions and proposes corresponding improvements to energy efficiency. The system configuration and operation are described below as specific embodiments.

[0344] The server first collects energy data in real time from home and office electricity meters and IoT devices. This data allows the server to understand the power consumption of each device and analyze consumption patterns. During the analysis process, machine learning algorithms are used to detect consumption trends and anomaly patterns.

[0345] A key feature of this system is its use of an emotion engine to recognize user emotions in real time. For example, if a user is stressed, the emotion engine detects this state and reflects it in the energy management plan. When a user is relaxed, the system can proactively offer energy-saving suggestions. This data is integrated with energy data and used for analysis on the server.

[0346] The device provides information through a user-friendly interface, based on energy analysis results obtained from the server and information from the emotion engine. Through this, users can view an energy management plan tailored to their emotional state. For example, if the user is fatigued, the device might suggest adjusting the room temperature to a comfortable level to promote relaxation.

[0347] The server further simulates the effects of introducing renewable energy based on predictive models. This helps users understand which renewable energy solutions are best suited to their environment. The emotion engine also plays a role here, making suggestions that match the user's interests and motivations.

[0348] To reduce power consumption during peak hours, the server generates a schedule that takes into account historical data and the user's emotional state, and automatically controls the device. If the user feels particularly busy, the device can emphasize load-reducing measures through automatic control.

[0349] By incorporating an emotional engine in this way, the system can go beyond mere energy management and provide intelligent support that improves the user's quality of life.

[0350] The following describes the processing flow.

[0351] Step 1:

[0352] The server collects real-time power consumption data from home and office electricity meters and various IoT devices. This includes detailed measurement data on the power consumption and usage of each device.

[0353] Step 2:

[0354] The server analyzes the collected energy data to identify the consumption patterns of each device. By applying machine learning algorithms, it detects abnormal consumption patterns and peak usage times.

[0355] Step 3:

[0356] The server uses an emotion engine to collect user emotion data from the terminal camera and voice recognition devices. This includes data obtained through facial expression recognition and voice tone analysis.

[0357] Step 4:

[0358] The server integrates energy and emotional data to generate recommendations for improving energy efficiency based on the user's emotional state. For example, if the user is feeling stressed, it will suggest dimming the lights to provide a calming environment.

[0359] Step 5:

[0360] The device receives analysis results and recommendations from the server and presents them in a user-friendly interface. Using visual graphs and charts, users can review suggestions based on their own consumption patterns and emotions.

[0361] Step 6:

[0362] Users select an energy management plan based on the information presented. If they feel fatigued, they can adjust the room temperature or optimize lighting according to the device's suggestions to reduce stress.

[0363] Step 7:

[0364] The server simulates and evaluates the effects of introducing renewable energy. Based on specific conditions, it calculates cost reductions for solar power generation and energy storage systems, and sends the results to the terminal.

[0365] Step 8:

[0366] The terminal presents the user with simulation results, helping them understand the benefits and cost-effectiveness of renewable energy. If necessary, the user can consider implementing the system.

[0367] Step 9:

[0368] The server generates an optimal schedule to avoid peak power demand times and automatically controls devices based on this schedule. It also manages energy consumption in a way that minimizes stress, taking into account the user's emotional state.

[0369] Step 10:

[0370] Users monitor the automatically controlled system, manually fine-tune settings as needed, and achieve efficient energy use while improving their quality of life.

[0371] (Example 2)

[0372] Next, we will describe Example 2. 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".

[0373] Modern energy management systems often make suggestions based solely on consumption data to improve efficiency. However, it has been pointed out that users' emotional states influence energy consumption, so there is a need to optimize energy efficiency while considering emotional states. Furthermore, it is also a challenge to grasp the effects of introducing renewable energy in real time and to make suggestions tailored to the individual circumstances of each user.

[0374] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0375] In this invention, the server includes means for collecting energy data in real time and analyzing consumption patterns, means for recognizing emotional states and reflecting them in energy management plans, and means for simulating the effects of introducing renewable energy sources using predictive models. This enables effective energy management and improved quality of life by providing optimal energy-efficient suggestions tailored to the user's emotional state.

[0376] "Energy data" refers to information related to electricity and other energy consumption, showing the usage of various devices and systems in home and office environments.

[0377] "Real-time" refers to acquiring information instantly and processing it without delay, resulting in a state where information responds immediately to user actions and changes in the environment.

[0378] "Consumption patterns" refer to regularities in how energy and other resources are used, and describe usage trends based on past data.

[0379] "Analysis" refers to the process of examining data and information in detail to reveal its structure and characteristics, and in particular, it includes identifying trends and anomalies in energy data.

[0380] "Automatic generation" refers to a system creating new data or information on its own based on algorithms and rules.

[0381] A "predictive model" refers to a method or algorithm that uses historical data and statistical techniques to estimate future trends and outcomes.

[0382] "Renewable energy sources" refer to inexhaustible and indestructible energy sources found in nature, such as solar, wind, hydro, and geothermal energy.

[0383] A "simulation" is a method of analyzing the impact of specific conditions or variables on the outcome by imitating actual operations.

[0384] A "schedule" refers to a detailed plan or schedule outlining when and how specific activities or operations will be carried out.

[0385] "Emotional state" refers to the user's mental and emotional changes, including states such as stress and relaxation.

[0386] "Energy efficiency suggestions" refer to recommended actions and settings aimed at optimizing users' energy consumption and reducing waste.

[0387] This invention provides a system that comprehensively analyzes energy data and the user's emotional state in order to optimize energy management. A specific embodiment is configured as follows.

[0388] The server collects energy data in real time from home and office electricity meters and IoT devices. This includes devices such as smart meters and network-connected temperature control devices. The server receives this data and performs data analysis using machine learning libraries such as TensorFlow. This analysis models the consumption patterns of each device, enabling the detection of trends and anomalies.

[0389] The emotion engine also collects emotional information from wearable devices worn by the user. The devices measure biosignals such as heart rate and skin temperature and transmit this information to the emotion engine. The emotion engine analyzes this information to understand the user's stress level and relaxation level.

[0390] The terminal proposes an energy management plan tailored to the user based on energy analysis results obtained from the server and emotional information from the emotion engine. This interface is user-friendly; for example, if it receives data indicating the user is fatigued, it will suggest room temperature settings and lighting adjustments to promote relaxation.

[0391] In addition, the server has the capability to use predictive models to simulate the effects of introducing renewable energy. For example, it can show users which type of renewable energy is best suited to their environmental conditions. It can also utilize data from an emotion engine to provide suggestions based on user interests.

[0392] For example, if a user returns home from work feeling stressed, the system can help create a relaxing environment by changing the lighting to a warmer color or setting the room temperature to a comfortable level. An example of a prompt to input into the generating AI model would be, "Generate suggestions for optimal energy use and emotional responses for the following time period."

[0393] Thus, this system aims to provide intelligent support that contributes not only to energy management but also to improving the quality of life for users.

[0394] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0395] Step 1:

[0396] The server collects energy data in real time from electricity meters and IoT devices in homes and offices. Inputs are power consumption data from smart meters and networked temperature control devices, and outputs are real-time energy consumption records compiled from this data. Specifically, the server periodically accesses devices, retrieves power consumption figures, and stores them in a database.

[0397] Step 2:

[0398] The server analyzes the collected energy data. The input is the power consumption records obtained in step 1, and the output is the consumption patterns of each device and anomaly detection information. Here, machine learning libraries such as TensorFlow are used to analyze data trends and detect abnormal consumption behavior.

[0399] Step 3:

[0400] The user wears a wearable device for emotion recognition and provides data on their emotional state. The input is biosignal information such as heart rate and skin temperature obtained from the device, and the output is the user's emotional state (stress level and relaxation level). Specifically, the device sends the collected data to an emotion engine, where the data is analyzed.

[0401] Step 4:

[0402] The server receives emotional state data from the emotion engine and integrates it with energy data. The input is the user's emotional state data and energy consumption patterns, and the output is a pre-adjusted energy management plan tailored to the user's state. The server fuses this data to create an optimal plan based on the user's emotions.

[0403] Step 5:

[0404] The terminal receives integrated analysis results from the server and displays an energy management plan tailored to the user. The input is the analysis results from the server, and the output is an energy efficiency suggestion in a user-friendly format. Specifically, the terminal provides suggestions for lighting settings and temperature adjustments based on the user's situation, both on screen and via audio.

[0405] Step 6:

[0406] The server simulates the effects of introducing renewable energy using a predictive model and proposes the best energy solution to the user based on the results. The input is the conditions for introducing renewable energy and environmental data, and the output is the simulation results of the introduction effects. Based on the simulation results, the server makes suggestions that enable the user to utilize renewable energy most effectively.

[0407] (Application Example 2)

[0408] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0409] Conventional energy management systems simply aim to improve energy efficiency based on consumption data, without considering the individual emotional states of users. Therefore, they fail to provide energy management that aligns with user comfort and motivation, resulting in a lack of more efficient and effective energy usage methods. This invention aims to solve these problems.

[0410] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0411] In this invention, the server includes means for collecting energy data in real time and analyzing consumption patterns, means for generating information to visualize the consumed energy, means for automatically generating recommendations for improving energy efficiency based on the analysis results, and means for sensing the user's emotional state and adjusting the energy management plan accordingly. This enables more personalized energy management suggestions that take the user's emotional state into consideration.

[0412] "Energy data" refers to data showing the usage of various power-consuming devices in homes and offices.

[0413] "Consumption patterns" refer to specific tendencies or habits that indicate the times of day and situations in which energy is used.

[0414] "Means of real-time collection" refers to technological means for instantly collecting energy data and understanding current usage patterns.

[0415] "Means for automatically generating recommendations for improving energy efficiency" refers to technology that mechanically derives specific suggestions for optimizing energy use based on analyzed data.

[0416] "A method for simulating the effects of introducing renewable energy sources using predictive models" refers to a process of calculating the expected changes in efficiency and cost resulting from the utilization of renewable energy sources.

[0417] "Means of providing schedules to curb peak power consumption" refers to technologies that limit power use during periods of high demand and create plans for efficient energy utilization.

[0418] "Means for automatically controlling devices" refers to a system that automatically adjusts the operation of equipment based on pre-set conditions.

[0419] "Means of sensing the user's emotional state and adjusting the energy management plan accordingly" refers to technology that evaluates the user's mental and emotional state and customizes energy usage suggestions based on that data.

[0420] This invention is a system that optimizes energy management based on energy data and the user's emotional state. The server collects energy data in real time from various power-consuming devices in homes and offices. This data is used to analyze consumption patterns, and machine learning algorithms such as TensorFlow are applied to the analysis process. The server also has an emotion engine to sense the user's emotional state and collects data using heart rate and facial recognition technology.

[0421] This system provides automatically generated recommendations for improving energy efficiency. For example, based on analyzed data, the server may suggest adjusting room temperature or lighting settings. It can also use predictive models to simulate the effects of introducing renewable energy and visualize energy savings and cost fluctuations. In this way, a schedule is created to reduce peak power consumption, and devices are automatically controlled.

[0422] Users receive energy management suggestions based on their emotional state through the app. This allows for the efficient use of energy resources while improving comfort in daily life. For example, a user returning home after being out for a long time might have the lighting automatically changed to a warmer color to help alleviate stress.

[0423] An example of a prompt to a generative AI model is, "Based on the user's emotional state upon returning home, how can we provide the optimal indoor environment?" By using such prompts, the system can suggest energy management strategies optimized for the user.

[0424] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0425] Step 1:

[0426] The server collects energy data from each device in real time. The collected data is instantly stored in a database and used as input to understand the current power consumption of each device. The data processing performed at this stage involves organizing the collected power usage as time-series data and checking for any outliers.

[0427] Step 2:

[0428] The server analyzes the collected energy data and derives consumption patterns. Using machine learning algorithms, it identifies normal and abnormal consumption patterns and outputs the results as an analysis report. Here, it performs trend analysis by comparing with past data to identify peak times and periods when energy conservation is possible.

[0429] Step 3:

[0430] The server uses an emotion engine to detect the user's emotional state. It obtains heart rate and facial expression data from the user's smartphone sensors and uses this as input to infer the emotional state. The inference results are then used as input for specific action suggestions.

[0431] Step 4:

[0432] The server integrates the analysis results of energy data with the output of the emotion engine to generate recommendations for improving energy efficiency. This process utilizes a generative AI model to generate optimal suggestions tailored to the user's mood. These suggestions may include adjusting the temperature or lighting within the home.

[0433] Step 5:

[0434] The terminal provides the user with recommendations obtained from the server. The user receives the suggestions through the application and changes the settings as needed. Specifically, they can choose to manually adjust the room temperature setting or allow automatic control.

[0435] Step 6:

[0436] Users submit feedback via the terminal interface to evaluate the effectiveness of the recommendations. This feedback is collected by the server and used to improve the accuracy of future data analysis, as it helps improve the overall system.

[0437] These processing steps enable a highly efficient energy management system that takes into account the user's emotional state.

[0438] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0439] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0440] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0441] [Third Embodiment]

[0442] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0443] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0444] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0445] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0446] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0447] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0448] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0449] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0450] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0451] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0452] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0453] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0454] This invention provides a system for optimizing the energy consumption of individuals or companies and reducing their environmental impact. This system has the function of collecting energy data in real time, analyzing it, and visualizing consumption patterns. Specific embodiments of this invention are described below.

[0455] The server collects real-time data on power consumption from home and office electricity meters and IoT devices. This data is analyzed on the server to detect consumption trends and abnormal patterns. The analysis results are then organized to visualize the user's energy usage and sent to the terminal.

[0456] The terminal receives data sent from the server and displays it visually through the user interface. This allows users to see at a glance their energy usage and how much power each device is consuming.

[0457] Furthermore, this system has a function to automatically generate recommendations for improving energy efficiency based on the analyzed data. The server learns past consumption patterns and uses machine learning algorithms to propose an optimal energy-saving plan tailored to the user's lifestyle and work situation.

[0458] Furthermore, the server is equipped with a predictive model for simulating the effects of introducing renewable energy sources. Users specify the conditions of their home or office through a terminal, and the server calculates how much energy reduction the introduction of solar power generation and energy storage systems will contribute based on this information, and displays the results on the terminal.

[0459] To reduce power consumption during peak hours, the server identifies peak times based on historical data and information from the power company, and presents a schedule plan to the terminal. This plan works in conjunction with an automatic control function to optimize the operation of devices outside of peak hours.

[0460] Based on the information and recommendations provided through the device, users can manage their energy consumption appropriately to suit their lifestyle and business operations. This promotes the use of sustainable energy for society as a whole and contributes to reducing environmental impact.

[0461] The following describes the processing flow.

[0462] Step 1:

[0463] The server receives real-time power consumption data from electricity meters and IoT devices installed in homes and offices. The received data is organized by device and stored in an internal database.

[0464] Step 2:

[0465] The server analyzes the accumulated data and detects consumption patterns. In particular, algorithms are applied to identify anomalies, and consumption trends and peak times are identified. The analysis results are used for subsequent processing.

[0466] Step 3:

[0467] The terminal receives analysis results from the server and displays them in a format that is easy for the user to understand. This includes visual displays such as graphs and charts. Users can view these to understand their own consumption patterns.

[0468] Step 4:

[0469] The server uses machine learning algorithms based on past consumption data to generate an optimal energy-saving plan for the user. The generated plan is designed to be easily implemented by the user and is offered as multiple options.

[0470] Step 5:

[0471] The terminal notifies the user of power-saving plans provided by the server and presents an interface that allows the user to select a plan. The user can then review this, select an appropriate plan, and implement it.

[0472] Step 6:

[0473] The server performs calculations based on user-specified conditions to simulate the effects of introducing renewable energy sources. It visualizes how effective solar power generation and energy storage systems are and sends the results to the terminal.

[0474] Step 7:

[0475] The terminal visually presents the transmitted simulation results to the user, explaining the benefits of implementation and the potential for cost reduction. Based on this, the user can then decide whether or not to implement the system.

[0476] Step 8:

[0477] The server identifies peak power demand times and uses this to propose an optimal operating schedule for the devices. It then uses automatic control functions to adjust device operation and avoid peak power consumption.

[0478] Step 9:

[0479] The user can review the proposed schedule via the terminal and authorize the automatic control function to adjust the device's operation. Manual adjustments can also be made as needed.

[0480] (Example 1)

[0481] Next, we will describe Example 1. 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."

[0482] With increasing energy consumption, individuals and businesses face challenges in efficient energy management and sustainable resource utilization. Conventional systems do not adequately collect energy data in real time or analyze consumption patterns, making it difficult to immediately confirm concrete suggestions for improving energy efficiency or the effectiveness of introducing sustainable energy sources. This invention aims to solve such challenges in energy management.

[0483] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0484] In this invention, the server includes means for collecting energy data over time and analyzing consumption trends, means for creating information for visualizing the use of consumed energy, and means for automatically generating recommendations to improve energy utilization efficiency based on the analysis results. This enables real-time collection and analysis of energy data, efficient energy management, and effective utilization of sustainable energy sources.

[0485] "Energy data" refers to quantitative information about energy use and consumption, including data that quantifies the consumption status of electricity, gas, water, and other resources.

[0486] "Time-based data collection" refers to the process of continuously acquiring data in real time or at a specific frequency, with the aim of always maintaining up-to-date information.

[0487] "Analyzing consumption trends" refers to using collected energy data to clarify usage patterns and fluctuation trends using statistical methods and algorithms.

[0488] "Creating information for visualization" is the process of preparing data based on analysis results to be displayed in formats such as graphs and charts so that users can understand them intuitively.

[0489] "Automatically generating recommendations to improve utilization efficiency" means automatically presenting optimal action plans and strategies to reduce energy waste and improve efficiency, taking into account analytical data and user patterns.

[0490] "Using a prediction device to virtually estimate the effects of introducing sustainable energy sources" means using a simulation tool to estimate how much the introduction of sustainable energy (e.g., solar power) will contribute to actual energy reduction.

[0491] "Providing a timetable and automatically operating the equipment" refers to programming the operating schedule of equipment to reduce energy consumption during peak hours, and automatically managing the equipment's operation based on the results.

[0492] This invention provides an integrated system for optimizing energy management and promoting sustainable use. This system includes functions for collecting and analyzing energy data in real time and presenting the data visually to the user.

[0493] The server collects power consumption data from various devices, including smart meters and IoT devices, which send data to the server via network connectivity. The server uses Python's Pandas and NumPy libraries to organize and analyze the data. Statistical algorithms are used to identify consumption trends and detect outliers.

[0494] The analysis results are organized as a dashboard on the server to help users understand them. This visualization uses tools such as Matplotlib and D3.js to generate graphs and charts. The device displays the received data, allowing users to intuitively grasp their consumption patterns.

[0495] Furthermore, the server uses a generative AI model to learn from past consumption data and provides users with recommendations to improve energy efficiency. This algorithm utilizes TensorFlow and PyTorch. These recommendations are customized based on actual consumption trends.

[0496] Users input their home or office conditions into the system via a terminal, and the server simulates the effects of introducing renewable energy sources. For example, by entering a prompt such as "Annual electricity savings if 3kW solar panels are installed on the roof of my house," the effect will be estimated.

[0497] Furthermore, the server identifies peak hours based on historical consumption data and external information, and the terminal provides the user with a schedule for efficient energy use. This schedule allows the user to properly operate their equipment to reduce consumption during peak hours.

[0498] In this way, this system, designed with practical use in mind, can promote the efficient and sustainable use of energy and contribute to reducing environmental impact.

[0499] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0500] Step 1:

[0501] The server collects power consumption data in real time from smart meters and IoT devices. These devices transmit consumption data to the server via a network connection. The input is power consumption data from each device. The server stores this data in a database and manages it chronologically to understand the total amount of consumption. The output is an aggregate of the raw data.

[0502] Step 2:

[0503] The server analyzes the collected power consumption data. It performs data cleansing using Python's Pandas and NumPy libraries to remove outliers. The input is the data aggregated in step 1. Time series analysis and anomaly detection algorithms are applied to identify consumption trends and anomalous patterns. The output is the cleansed time series data and the detected consumption trends.

[0504] Step 3:

[0505] The server generates visualization data for the user based on the analysis results. The input is the analysis results from step 2. Using tools such as Matplotlib and D3.js, it creates graphs and charts of consumption patterns. The output is the visualized data, which is sent from the server to the terminal.

[0506] Step 4:

[0507] The terminal displays the received visualization data on the user interface. The input is the visualization data generated in step 3. This allows the user to grasp the consumption status and consumption trends of each device at a glance. The output is the analysis results displayed on the dashboard.

[0508] Step 5:

[0509] The server generates recommendations to improve energy efficiency using a generative AI model. The input consists of historical consumption data and current usage. It leverages machine learning frameworks such as TensorFlow and PyTorch to generate efficient energy management plans. The output is a customized recommendation plan, which is also sent to the terminal.

[0510] Step 6:

[0511] The user enters a prompt message via the terminal to estimate the effectiveness of introducing sustainable energy sources. For example, one example is "the effect of installing a 3kW solar panel at home." The input is this prompt message. The server uses a simulation model to estimate the effectiveness of the introduction under the specified conditions. The output is the calculation result.

[0512] Step 7:

[0513] The server generates an optimal schedule based on historical data and power company information to reduce peak power consumption. Inputs are historical consumption data and power supply information. This allows the server to provide a consumption schedule that avoids peak times. The output is a control plan.

[0514] Step 8:

[0515] The terminal presents the user with recommended plans and schedules sent from the server. Based on this information, the user can streamline their daily energy management. The input becomes output data from the server, providing crucial information for understanding the user's behavior. The output is an energy usage plan that the user can adjust based on their individual needs.

[0516] (Application Example 1)

[0517] Next, we will explain Application Example 1. In the following explanation, 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."

[0518] Energy consumption in public facilities and infrastructure is not yet sufficiently optimized, making conscious energy conservation in daily life difficult. Furthermore, there is a lack of effective means to suppress peak electricity consumption and promote sustainable energy use.

[0519] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0520] This invention includes a server that collects energy data in real time and analyzes consumption patterns, generates information for visualizing consumed energy, simulates the effects of introducing renewable energy sources using a predictive model, and provides information that optimizes energy consumption in public facilities and infrastructure and contributes to energy conservation in daily life. This enables more efficient energy use and the realization of sustainable energy use.

[0521] "Energy data" refers to data that shows information about the electricity used by individuals, public facilities, or infrastructure.

[0522] "Real-time data collection" refers to the process of acquiring energy data instantly and without delay.

[0523] "Analyzing consumption patterns" is the act of analyzing collected data to reveal trends and characteristics in electricity usage.

[0524] "Information for visualization" refers to data that provides the results of energy data analysis in a visually easy-to-understand format.

[0525] A "predictive model" is an algorithm that predicts future energy consumption and the effects of renewable energy based on past data.

[0526] "Renewable energy sources" refer to energy derived from nature, such as solar and wind power, that can be used in a sustainable manner.

[0527] "Simulating the effects of implementation" means calculating the effects of utilizing renewable energy sources in a simulated manner.

[0528] "Suppressing peak electricity consumption" refers to management aimed at reducing consumption during the time when electricity demand is highest.

[0529] "Optimizing energy consumption for public facilities and infrastructure" is the process of efficiently managing energy use throughout a city and reducing waste.

[0530] "Information that contributes to energy conservation in daily life" refers to useful data and suggestions for individuals to use energy efficiently in their daily activities.

[0531] This invention is a system that efficiently manages energy consumption and promotes sustainable energy use in public facilities and infrastructure. In this system, a server plays a major role and has multiple functions.

[0532] First, the server collects energy data in real time from public facilities, private homes, and other locations. IoT devices and smart electricity meters are used to ensure reliable data acquisition. The collected data is then stored in a database.

[0533] Next, the server analyzes the collected data to reveal consumption patterns. This analysis applies machine learning algorithms to recognize patterns and detect anomalies. The analysis results are also used as input data for predictive models that simulate the effects of introducing renewable energy.

[0534] Users receive analysis results sent from the server via their terminals and can view them as visualized information about their energy usage. Based on this information, users receive recommendations for efficient energy use. Furthermore, the server generates and provides users with specific plans to optimize energy consumption in public facilities and infrastructure.

[0535] The server also provides a schedule to reduce peak power consumption. This schedule works in conjunction with the device's automatic control function to ensure power is used during the most efficient times. It also leverages generative AI models to provide advice for further energy optimization.

[0536] As a concrete example, if it is determined that the peak electricity consumption in a public facility in a certain city is between 2 PM and 4 PM, the server will provide a plan to efficiently control lighting and air conditioning equipment to avoid that time slot. This process uses a generative AI model and prompts such as, "Analyze energy consumption pattern data in the city and generate predictions for peak hours and the effects of renewable energy introduction."

[0537] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0538] Step 1:

[0539] The server collects energy data in real time from smart electricity meters and IoT devices. The input consists of numerical data on power consumption from each device, which is stored in a database. The server then organizes the collected raw data as time-series data.

[0540] Step 2:

[0541] The server applies machine learning algorithms to analyze the data. The input is the time-series data collected in step 1. The analysis includes recognizing consumption patterns and detecting anomalies. The server uses this data to model data trends and generate a consumption pattern report.

[0542] Step 3:

[0543] The server automatically generates recommendations to improve energy efficiency based on the analysis results. The input is the consumption pattern report obtained in step 2. The server uses the generated AI model to formulate an optimization plan and creates an output that summarizes suggestions for saving electricity and using energy efficiently.

[0544] Step 4:

[0545] The server uses a predictive model to simulate the effects of introducing renewable energy sources. The inputs are historical consumption pattern data and characteristic information of renewable energy sources. Based on this data, the server outputs simulation results that quantify energy savings and cost reductions.

[0546] Step 5:

[0547] The server generates and sends to the terminal a plan to optimize the energy consumption of public facilities and infrastructure. The input is the optimization plan and simulation results obtained in the previous step, which the server organizes and provides to the terminal as visualized data that is easy for the user to understand.

[0548] Step 6:

[0549] The user receives data from the server via their device to check their energy usage and recommendations. The input is the visualized data sent in step 5. Based on the displayed information, the user manages their energy consumption and considers specific actions to save electricity.

[0550] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0551] This invention provides a system that integrates energy data and user emotional states to optimize energy management. By incorporating an emotion engine, this system understands the user's emotions and proposes corresponding improvements to energy efficiency. The system configuration and operation are described below as specific embodiments.

[0552] The server first collects energy data in real time from home and office electricity meters and IoT devices. This data allows the server to understand the power consumption of each device and analyze consumption patterns. During the analysis process, machine learning algorithms are used to detect consumption trends and anomaly patterns.

[0553] A key feature of this system is its use of an emotion engine to recognize user emotions in real time. For example, if a user is stressed, the emotion engine detects this state and reflects it in the energy management plan. When a user is relaxed, the system can proactively offer energy-saving suggestions. This data is integrated with energy data and used for analysis on the server.

[0554] The device provides information through a user-friendly interface, based on energy analysis results obtained from the server and information from the emotion engine. Through this, users can view an energy management plan tailored to their emotional state. For example, if the user is fatigued, the device might suggest adjusting the room temperature to a comfortable level to promote relaxation.

[0555] The server further simulates the effects of introducing renewable energy based on predictive models. This helps users understand which renewable energy solutions are best suited to their environment. The emotion engine also plays a role here, making suggestions that match the user's interests and motivations.

[0556] To reduce power consumption during peak hours, the server generates a schedule that takes into account historical data and the user's emotional state, and automatically controls the device. If the user feels particularly busy, the device can emphasize load-reducing measures through automatic control.

[0557] By incorporating an emotional engine in this way, the system can go beyond mere energy management and provide intelligent support that improves the user's quality of life.

[0558] The following describes the processing flow.

[0559] Step 1:

[0560] The server collects real-time power consumption data from home and office electricity meters and various IoT devices. This includes detailed measurement data on the power consumption and usage of each device.

[0561] Step 2:

[0562] The server analyzes the collected energy data to identify the consumption patterns of each device. By applying machine learning algorithms, it detects abnormal consumption patterns and peak usage times.

[0563] Step 3:

[0564] The server uses an emotion engine to collect user emotion data from the terminal camera and voice recognition devices. This includes data obtained through facial expression recognition and voice tone analysis.

[0565] Step 4:

[0566] The server integrates energy and emotional data to generate recommendations for improving energy efficiency based on the user's emotional state. For example, if the user is feeling stressed, it will suggest dimming the lights to provide a calming environment.

[0567] Step 5:

[0568] The device receives analysis results and recommendations from the server and presents them in a user-friendly interface. Using visual graphs and charts, users can review suggestions based on their own consumption patterns and emotions.

[0569] Step 6:

[0570] Users select an energy management plan based on the information presented. If they feel fatigued, they can adjust the room temperature or optimize lighting according to the device's suggestions to reduce stress.

[0571] Step 7:

[0572] The server simulates and evaluates the effects of introducing renewable energy. Based on specific conditions, it calculates cost reductions for solar power generation and energy storage systems, and sends the results to the terminal.

[0573] Step 8:

[0574] The terminal presents the user with simulation results, helping them understand the benefits and cost-effectiveness of renewable energy. If necessary, the user can consider implementing the system.

[0575] Step 9:

[0576] The server generates an optimal schedule to avoid peak power demand times and automatically controls devices based on this schedule. It also manages energy consumption in a way that minimizes stress, taking into account the user's emotional state.

[0577] Step 10:

[0578] Users monitor the automatically controlled system, manually fine-tune settings as needed, and achieve efficient energy use while improving their quality of life.

[0579] (Example 2)

[0580] Next, we will describe Example 2. 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."

[0581] Modern energy management systems often make suggestions based solely on consumption data to improve efficiency. However, it has been pointed out that users' emotional states influence energy consumption, so there is a need to optimize energy efficiency while considering emotional states. Furthermore, it is also a challenge to grasp the effects of introducing renewable energy in real time and to make suggestions tailored to the individual circumstances of each user.

[0582] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0583] In this invention, the server includes means for collecting energy data in real time and analyzing consumption patterns, means for recognizing emotional states and reflecting them in energy management plans, and means for simulating the effects of introducing renewable energy sources using predictive models. This enables effective energy management and improved quality of life by providing optimal energy-efficient suggestions tailored to the user's emotional state.

[0584] "Energy data" refers to information related to electricity and other energy consumption, showing the usage of various devices and systems in home and office environments.

[0585] "Real-time" refers to acquiring information instantly and processing it without delay, resulting in a state where information responds immediately to user actions and changes in the environment.

[0586] "Consumption patterns" refer to regularities in how energy and other resources are used, and describe usage trends based on past data.

[0587] "Analysis" refers to the process of examining data and information in detail to reveal its structure and characteristics, and in particular, it includes identifying trends and anomalies in energy data.

[0588] "Automatic generation" refers to a system creating new data or information on its own based on algorithms and rules.

[0589] A "predictive model" refers to a method or algorithm that uses historical data and statistical techniques to estimate future trends and outcomes.

[0590] "Renewable energy sources" refer to inexhaustible and indestructible energy sources found in nature, such as solar, wind, hydro, and geothermal energy.

[0591] A "simulation" is a method of analyzing the impact of specific conditions or variables on the outcome by imitating actual operations.

[0592] A "schedule" refers to a detailed plan or schedule outlining when and how specific activities or operations will be carried out.

[0593] "Emotional state" refers to the user's mental and emotional changes, including states such as stress and relaxation.

[0594] "Energy efficiency suggestions" refer to recommended actions and settings aimed at optimizing users' energy consumption and reducing waste.

[0595] This invention provides a system that comprehensively analyzes energy data and the user's emotional state in order to optimize energy management. A specific embodiment is configured as follows.

[0596] The server collects energy data in real time from home and office electricity meters and IoT devices. This includes devices such as smart meters and network-connected temperature control devices. The server receives this data and performs data analysis using machine learning libraries such as TensorFlow. This analysis models the consumption patterns of each device, enabling the detection of trends and anomalies.

[0597] The emotion engine also collects emotional information from wearable devices worn by the user. The devices measure biosignals such as heart rate and skin temperature and transmit this information to the emotion engine. The emotion engine analyzes this information to understand the user's stress level and relaxation level.

[0598] The terminal proposes an energy management plan tailored to the user based on energy analysis results obtained from the server and emotional information from the emotion engine. This interface is user-friendly; for example, if it receives data indicating the user is fatigued, it will suggest room temperature settings and lighting adjustments to promote relaxation.

[0599] In addition, the server has the capability to use predictive models to simulate the effects of introducing renewable energy. For example, it can show users which type of renewable energy is best suited to their environmental conditions. It can also utilize data from an emotion engine to provide suggestions based on user interests.

[0600] For example, if a user returns home from work feeling stressed, the system can help create a relaxing environment by changing the lighting to a warmer color or setting the room temperature to a comfortable level. An example of a prompt to input into the generating AI model would be, "Generate suggestions for optimal energy use and emotional responses for the following time period."

[0601] Thus, this system aims to provide intelligent support that contributes not only to energy management but also to improving the quality of life for users.

[0602] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0603] Step 1:

[0604] The server collects energy data in real time from electricity meters and IoT devices in homes and offices. Inputs are power consumption data from smart meters and networked temperature control devices, and outputs are real-time energy consumption records compiled from this data. Specifically, the server periodically accesses devices, retrieves power consumption figures, and stores them in a database.

[0605] Step 2:

[0606] The server analyzes the collected energy data. The input is the power consumption records obtained in step 1, and the output is the consumption patterns of each device and anomaly detection information. Here, machine learning libraries such as TensorFlow are used to analyze data trends and detect abnormal consumption behavior.

[0607] Step 3:

[0608] The user wears a wearable device for emotion recognition and provides data on their emotional state. The input is biosignal information such as heart rate and skin temperature obtained from the device, and the output is the user's emotional state (stress level and relaxation level). Specifically, the device sends the collected data to an emotion engine, where the data is analyzed.

[0609] Step 4:

[0610] The server receives emotional state data from the emotion engine and integrates it with energy data. The input is the user's emotional state data and energy consumption patterns, and the output is a pre-adjusted energy management plan tailored to the user's state. The server fuses this data to create an optimal plan based on the user's emotions.

[0611] Step 5:

[0612] The terminal receives integrated analysis results from the server and displays an energy management plan tailored to the user. The input is the analysis results from the server, and the output is an energy efficiency suggestion in a user-friendly format. Specifically, the terminal provides suggestions for lighting settings and temperature adjustments based on the user's situation, both on screen and via audio.

[0613] Step 6:

[0614] The server simulates the effects of introducing renewable energy using a predictive model and proposes the best energy solution to the user based on the results. The input is the conditions for introducing renewable energy and environmental data, and the output is the simulation results of the introduction effects. Based on the simulation results, the server makes suggestions that enable the user to utilize renewable energy most effectively.

[0615] (Application Example 2)

[0616] Next, we will explain Application Example 2. In the following explanation, 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."

[0617] Conventional energy management systems simply aim to improve energy efficiency based on consumption data, without considering the individual emotional states of users. Therefore, they fail to provide energy management that aligns with user comfort and motivation, resulting in a lack of more efficient and effective energy usage methods. This invention aims to solve these problems.

[0618] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0619] In this invention, the server includes means for collecting energy data in real time and analyzing consumption patterns, means for generating information to visualize the consumed energy, means for automatically generating recommendations for improving energy efficiency based on the analysis results, and means for sensing the user's emotional state and adjusting the energy management plan accordingly. This enables more personalized energy management suggestions that take the user's emotional state into consideration.

[0620] "Energy data" refers to data showing the usage of various power-consuming devices in homes and offices.

[0621] "Consumption patterns" refer to specific tendencies or habits that indicate the times of day and situations in which energy is used.

[0622] "Means of real-time collection" refers to technological means for instantly collecting energy data and understanding current usage patterns.

[0623] "Means for automatically generating recommendations for improving energy efficiency" refers to technology that mechanically derives specific suggestions for optimizing energy use based on analyzed data.

[0624] "A method for simulating the effects of introducing renewable energy sources using predictive models" refers to a process of calculating the expected changes in efficiency and cost resulting from the utilization of renewable energy sources.

[0625] "Means of providing schedules to curb peak power consumption" refers to technologies that limit power use during periods of high demand and create plans for efficient energy utilization.

[0626] "Means for automatically controlling devices" refers to a system that automatically adjusts the operation of equipment based on pre-set conditions.

[0627] "Means of sensing the user's emotional state and adjusting the energy management plan accordingly" refers to technology that evaluates the user's mental and emotional state and customizes energy usage suggestions based on that data.

[0628] This invention is a system that optimizes energy management based on energy data and the user's emotional state. The server collects energy data in real time from various power-consuming devices in homes and offices. This data is used to analyze consumption patterns, and machine learning algorithms such as TensorFlow are applied to the analysis process. The server also has an emotion engine to sense the user's emotional state and collects data using heart rate and facial recognition technology.

[0629] This system provides automatically generated recommendations for improving energy efficiency. For example, based on analyzed data, the server may suggest adjusting room temperature or lighting settings. It can also use predictive models to simulate the effects of introducing renewable energy and visualize energy savings and cost fluctuations. In this way, a schedule is created to reduce peak power consumption, and devices are automatically controlled.

[0630] Users receive energy management suggestions based on their emotional state through the app. This allows for the efficient use of energy resources while improving comfort in daily life. For example, a user returning home after being out for a long time might have the lighting automatically changed to a warmer color to help alleviate stress.

[0631] An example of a prompt to a generative AI model is, "Based on the user's emotional state upon returning home, how can we provide the optimal indoor environment?" By using such prompts, the system can suggest energy management strategies optimized for the user.

[0632] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0633] Step 1:

[0634] The server collects energy data from each device in real time. The collected data is instantly stored in a database and used as input to understand the current power consumption of each device. The data processing performed at this stage involves organizing the collected power usage as time-series data and checking for any outliers.

[0635] Step 2:

[0636] The server analyzes the collected energy data and derives consumption patterns. Using machine learning algorithms, it identifies normal and abnormal consumption patterns and outputs the results as an analysis report. Here, it performs trend analysis by comparing with past data to identify peak times and periods when energy conservation is possible.

[0637] Step 3:

[0638] The server uses an emotion engine to detect the user's emotional state. It obtains heart rate and facial expression data from the user's smartphone sensors and uses this as input to infer the emotional state. The inference results are then used as input for specific action suggestions.

[0639] Step 4:

[0640] The server integrates the analysis results of energy data with the output of the emotion engine to generate recommendations for improving energy efficiency. This process utilizes a generative AI model to generate optimal suggestions tailored to the user's mood. These suggestions may include adjusting the temperature or lighting within the home.

[0641] Step 5:

[0642] The terminal provides the user with recommendations obtained from the server. The user receives the suggestions through the application and changes the settings as needed. Specifically, they can choose to manually adjust the room temperature setting or allow automatic control.

[0643] Step 6:

[0644] Users submit feedback via the terminal interface to evaluate the effectiveness of the recommendations. This feedback is collected by the server and used to improve the accuracy of future data analysis, as it helps improve the overall system.

[0645] These processing steps enable a highly efficient energy management system that takes into account the user's emotional state.

[0646] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0647] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0648] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0649] [Fourth Embodiment]

[0650] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0651] As shown in Figure 7, the 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.

[0652] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0653] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0654] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0655] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0656] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0657] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0658] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0659] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0660] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0661] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0662] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0663] This invention provides a system for optimizing the energy consumption of individuals or companies and reducing their environmental impact. This system has the function of collecting energy data in real time, analyzing it, and visualizing consumption patterns. Specific embodiments of this invention are described below.

[0664] The server collects real-time data on power consumption from home and office electricity meters and IoT devices. This data is analyzed on the server to detect consumption trends and abnormal patterns. The analysis results are then organized to visualize the user's energy usage and sent to the terminal.

[0665] The terminal receives data sent from the server and displays it visually through the user interface. This allows users to see at a glance their energy usage and how much power each device is consuming.

[0666] Furthermore, this system has a function to automatically generate recommendations for improving energy efficiency based on the analyzed data. The server learns past consumption patterns and uses machine learning algorithms to propose an optimal energy-saving plan tailored to the user's lifestyle and work situation.

[0667] Furthermore, the server is equipped with a predictive model for simulating the effects of introducing renewable energy sources. Users specify the conditions of their home or office through a terminal, and the server calculates how much energy reduction the introduction of solar power generation and energy storage systems will contribute based on this information, and displays the results on the terminal.

[0668] To reduce power consumption during peak hours, the server identifies peak times based on historical data and information from the power company, and presents a schedule plan to the terminal. This plan works in conjunction with an automatic control function to optimize the operation of devices outside of peak hours.

[0669] Based on the information and recommendations provided through the device, users can manage their energy consumption appropriately to suit their lifestyle and business operations. This promotes the use of sustainable energy for society as a whole and contributes to reducing environmental impact.

[0670] The following describes the processing flow.

[0671] Step 1:

[0672] The server receives real-time power consumption data from electricity meters and IoT devices installed in homes and offices. The received data is organized by device and stored in an internal database.

[0673] Step 2:

[0674] The server analyzes the accumulated data and detects consumption patterns. In particular, algorithms are applied to identify anomalies, and consumption trends and peak times are identified. The analysis results are used for subsequent processing.

[0675] Step 3:

[0676] The terminal receives analysis results from the server and displays them in a format that is easy for the user to understand. This includes visual displays such as graphs and charts. Users can view these to understand their own consumption patterns.

[0677] Step 4:

[0678] The server uses machine learning algorithms based on past consumption data to generate an optimal energy-saving plan for the user. The generated plan is designed to be easily implemented by the user and is offered as multiple options.

[0679] Step 5:

[0680] The terminal notifies the user of power-saving plans provided by the server and presents an interface that allows the user to select a plan. The user can then review this, select an appropriate plan, and implement it.

[0681] Step 6:

[0682] The server performs calculations based on user-specified conditions to simulate the effects of introducing renewable energy sources. It visualizes how effective solar power generation and energy storage systems are and sends the results to the terminal.

[0683] Step 7:

[0684] The terminal visually presents the transmitted simulation results to the user, explaining the benefits of implementation and the potential for cost reduction. Based on this, the user can then decide whether or not to implement the system.

[0685] Step 8:

[0686] The server identifies peak power demand times and uses this to propose an optimal operating schedule for the devices. It then uses automatic control functions to adjust device operation and avoid peak power consumption.

[0687] Step 9:

[0688] The user can review the proposed schedule via the terminal and authorize the automatic control function to adjust the device's operation. Manual adjustments can also be made as needed.

[0689] (Example 1)

[0690] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0691] With increasing energy consumption, individuals and businesses face challenges in efficient energy management and sustainable resource utilization. Conventional systems do not adequately collect energy data in real time or analyze consumption patterns, making it difficult to immediately confirm concrete suggestions for improving energy efficiency or the effectiveness of introducing sustainable energy sources. This invention aims to solve such challenges in energy management.

[0692] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0693] In this invention, the server includes means for collecting energy data over time and analyzing consumption trends, means for creating information for visualizing the use of consumed energy, and means for automatically generating recommendations to improve energy utilization efficiency based on the analysis results. This enables real-time collection and analysis of energy data, efficient energy management, and effective utilization of sustainable energy sources.

[0694] "Energy data" refers to quantitative information about energy use and consumption, including data that quantifies the consumption status of electricity, gas, water, and other resources.

[0695] "Time-based data collection" refers to the process of continuously acquiring data in real time or at a specific frequency, with the aim of always maintaining up-to-date information.

[0696] "Analyzing consumption trends" refers to using collected energy data to clarify usage patterns and fluctuation trends using statistical methods and algorithms.

[0697] "Creating information for visualization" is the process of preparing data based on analysis results to be displayed in formats such as graphs and charts so that users can understand them intuitively.

[0698] "Automatically generating recommendations to improve utilization efficiency" means automatically presenting optimal action plans and strategies to reduce energy waste and improve efficiency, taking into account analytical data and user patterns.

[0699] "Using a prediction device to virtually estimate the effects of introducing sustainable energy sources" means using a simulation tool to estimate how much the introduction of sustainable energy (e.g., solar power) will contribute to actual energy reduction.

[0700] "Providing a timetable and automatically operating the equipment" refers to programming the operating schedule of equipment to reduce energy consumption during peak hours, and automatically managing the equipment's operation based on the results.

[0701] This invention provides an integrated system for optimizing energy management and promoting sustainable use. This system includes functions for collecting and analyzing energy data in real time and presenting the data visually to the user.

[0702] The server collects power consumption data from various devices, including smart meters and IoT devices, which send data to the server via network connectivity. The server uses Python's Pandas and NumPy libraries to organize and analyze the data. Statistical algorithms are used to identify consumption trends and detect outliers.

[0703] The analysis results are organized as a dashboard on the server to help users understand them. This visualization uses tools such as Matplotlib and D3.js to generate graphs and charts. The device displays the received data, allowing users to intuitively grasp their consumption patterns.

[0704] Furthermore, the server uses a generative AI model to learn from past consumption data and provides users with recommendations to improve energy efficiency. This algorithm utilizes TensorFlow and PyTorch. These recommendations are customized based on actual consumption trends.

[0705] Users input their home or office conditions into the system via a terminal, and the server simulates the effects of introducing renewable energy sources. For example, by entering a prompt such as "Annual electricity savings if 3kW solar panels are installed on the roof of my house," the effect will be estimated.

[0706] Furthermore, the server identifies peak hours based on historical consumption data and external information, and the terminal provides the user with a schedule for efficient energy use. This schedule allows the user to properly operate their equipment to reduce consumption during peak hours.

[0707] In this way, this system, designed with practical use in mind, can promote the efficient and sustainable use of energy and contribute to reducing environmental impact.

[0708] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0709] Step 1:

[0710] The server collects power consumption data in real time from smart meters and IoT devices. These devices transmit consumption data to the server via a network connection. The input is power consumption data from each device. The server stores this data in a database and manages it chronologically to understand the total amount of consumption. The output is an aggregate of the raw data.

[0711] Step 2:

[0712] The server analyzes the collected power consumption data. It performs data cleansing using Python's Pandas and NumPy libraries to remove outliers. The input is the data aggregated in step 1. Time series analysis and anomaly detection algorithms are applied to identify consumption trends and anomalous patterns. The output is the cleansed time series data and the detected consumption trends.

[0713] Step 3:

[0714] The server generates visualization data for the user based on the analysis results. The input is the analysis results from step 2. Using tools such as Matplotlib and D3.js, it creates graphs and charts of consumption patterns. The output is the visualized data, which is sent from the server to the terminal.

[0715] Step 4:

[0716] The terminal displays the received visualization data on the user interface. The input is the visualization data generated in step 3. This allows the user to grasp the consumption status and consumption trends of each device at a glance. The output is the analysis results displayed on the dashboard.

[0717] Step 5:

[0718] The server generates recommendations to improve energy efficiency using a generative AI model. The input consists of historical consumption data and current usage. It leverages machine learning frameworks such as TensorFlow and PyTorch to generate efficient energy management plans. The output is a customized recommendation plan, which is also sent to the terminal.

[0719] Step 6:

[0720] The user enters a prompt message via the terminal to estimate the effectiveness of introducing sustainable energy sources. For example, one example is "the effect of installing a 3kW solar panel at home." The input is this prompt message. The server uses a simulation model to estimate the effectiveness of the introduction under the specified conditions. The output is the calculation result.

[0721] Step 7:

[0722] The server generates an optimal schedule based on historical data and power company information to reduce peak power consumption. Inputs are historical consumption data and power supply information. This allows the server to provide a consumption schedule that avoids peak times. The output is a control plan.

[0723] Step 8:

[0724] The terminal presents the user with recommended plans and schedules sent from the server. Based on this information, the user can streamline their daily energy management. The input becomes output data from the server, providing crucial information for understanding the user's behavior. The output is an energy usage plan that the user can adjust based on their individual needs.

[0725] (Application Example 1)

[0726] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0727] Energy consumption in public facilities and infrastructure is not yet sufficiently optimized, making conscious energy conservation in daily life difficult. Furthermore, there is a lack of effective means to suppress peak electricity consumption and promote sustainable energy use.

[0728] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0729] This invention includes a server that collects energy data in real time and analyzes consumption patterns, generates information for visualizing consumed energy, simulates the effects of introducing renewable energy sources using a predictive model, and provides information that optimizes energy consumption in public facilities and infrastructure and contributes to energy conservation in daily life. This enables more efficient energy use and the realization of sustainable energy use.

[0730] "Energy data" refers to data that shows information about the electricity used by individuals, public facilities, or infrastructure.

[0731] "Real-time data collection" refers to the process of acquiring energy data instantly and without delay.

[0732] "Analyzing consumption patterns" is the act of analyzing collected data to reveal trends and characteristics in electricity usage.

[0733] "Information for visualization" refers to data that provides the results of energy data analysis in a visually easy-to-understand format.

[0734] A "predictive model" is an algorithm that predicts future energy consumption and the effects of renewable energy based on past data.

[0735] "Renewable energy sources" refer to energy derived from nature, such as solar and wind power, that can be used in a sustainable manner.

[0736] "Simulating the effects of implementation" means calculating the effects of utilizing renewable energy sources in a simulated manner.

[0737] "Suppressing peak electricity consumption" refers to management aimed at reducing consumption during the time when electricity demand is highest.

[0738] "Optimizing energy consumption for public facilities and infrastructure" is the process of efficiently managing energy use throughout a city and reducing waste.

[0739] "Information that contributes to energy conservation in daily life" refers to useful data and suggestions for individuals to use energy efficiently in their daily activities.

[0740] This invention is a system that efficiently manages energy consumption and promotes sustainable energy use in public facilities and infrastructure. In this system, a server plays a major role and has multiple functions.

[0741] First, the server collects energy data in real time from public facilities, private homes, and other locations. IoT devices and smart electricity meters are used to ensure reliable data acquisition. The collected data is then stored in a database.

[0742] Next, the server analyzes the collected data to reveal consumption patterns. This analysis applies machine learning algorithms to recognize patterns and detect anomalies. The analysis results are also used as input data for predictive models that simulate the effects of introducing renewable energy.

[0743] Users receive analysis results sent from the server via their terminals and can view them as visualized information about their energy usage. Based on this information, users receive recommendations for efficient energy use. Furthermore, the server generates and provides users with specific plans to optimize energy consumption in public facilities and infrastructure.

[0744] The server also provides a schedule to reduce peak power consumption. This schedule works in conjunction with the device's automatic control function to ensure power is used during the most efficient times. It also leverages generative AI models to provide advice for further energy optimization.

[0745] As a concrete example, if it is determined that the peak electricity consumption in a public facility in a certain city is between 2 PM and 4 PM, the server will provide a plan to efficiently control lighting and air conditioning equipment to avoid that time slot. This process uses a generative AI model and prompts such as, "Analyze energy consumption pattern data in the city and generate predictions for peak hours and the effects of renewable energy introduction."

[0746] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0747] Step 1:

[0748] The server collects energy data in real time from smart electricity meters and IoT devices. The input consists of numerical data on power consumption from each device, which is stored in a database. The server then organizes the collected raw data as time-series data.

[0749] Step 2:

[0750] The server applies machine learning algorithms to analyze the data. The input is the time-series data collected in step 1. The analysis includes recognizing consumption patterns and detecting anomalies. The server uses this data to model data trends and generate a consumption pattern report.

[0751] Step 3:

[0752] The server automatically generates recommendations to improve energy efficiency based on the analysis results. The input is the consumption pattern report obtained in step 2. The server uses the generated AI model to formulate an optimization plan and creates an output that summarizes suggestions for saving electricity and using energy efficiently.

[0753] Step 4:

[0754] The server uses a predictive model to simulate the effects of introducing renewable energy sources. The inputs are historical consumption pattern data and characteristic information of renewable energy sources. Based on this data, the server outputs simulation results that quantify energy savings and cost reductions.

[0755] Step 5:

[0756] The server generates and sends to the terminal a plan to optimize the energy consumption of public facilities and infrastructure. The input is the optimization plan and simulation results obtained in the previous step, which the server organizes and provides to the terminal as visualized data that is easy for the user to understand.

[0757] Step 6:

[0758] The user receives data from the server via their device to check their energy usage and recommendations. The input is the visualized data sent in step 5. Based on the displayed information, the user manages their energy consumption and considers specific actions to save electricity.

[0759] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0760] This invention provides a system that integrates energy data and user emotional states to optimize energy management. By incorporating an emotion engine, this system understands the user's emotions and proposes corresponding improvements to energy efficiency. The system configuration and operation are described below as specific embodiments.

[0761] The server first collects energy data in real time from home and office electricity meters and IoT devices. This data allows the server to understand the power consumption of each device and analyze consumption patterns. During the analysis process, machine learning algorithms are used to detect consumption trends and anomaly patterns.

[0762] A key feature of this system is its use of an emotion engine to recognize user emotions in real time. For example, if a user is stressed, the emotion engine detects this state and reflects it in the energy management plan. When a user is relaxed, the system can proactively offer energy-saving suggestions. This data is integrated with energy data and used for analysis on the server.

[0763] The device provides information through a user-friendly interface, based on energy analysis results obtained from the server and information from the emotion engine. Through this, users can view an energy management plan tailored to their emotional state. For example, if the user is fatigued, the device might suggest adjusting the room temperature to a comfortable level to promote relaxation.

[0764] The server further simulates the effects of introducing renewable energy based on predictive models. This helps users understand which renewable energy solutions are best suited to their environment. The emotion engine also plays a role here, making suggestions that match the user's interests and motivations.

[0765] To reduce power consumption during peak hours, the server generates a schedule that takes into account historical data and the user's emotional state, and automatically controls the device. If the user feels particularly busy, the device can emphasize load-reducing measures through automatic control.

[0766] By incorporating an emotional engine in this way, the system can go beyond mere energy management and provide intelligent support that improves the user's quality of life.

[0767] The following describes the processing flow.

[0768] Step 1:

[0769] The server collects real-time power consumption data from home and office electricity meters and various IoT devices. This includes detailed measurement data on the power consumption and usage of each device.

[0770] Step 2:

[0771] The server analyzes the collected energy data to identify the consumption patterns of each device. By applying machine learning algorithms, it detects abnormal consumption patterns and peak usage times.

[0772] Step 3:

[0773] The server uses an emotion engine to collect user emotion data from the terminal camera and voice recognition devices. This includes data obtained through facial expression recognition and voice tone analysis.

[0774] Step 4:

[0775] The server integrates energy and emotional data to generate recommendations for improving energy efficiency based on the user's emotional state. For example, if the user is feeling stressed, it will suggest dimming the lights to provide a calming environment.

[0776] Step 5:

[0777] The device receives analysis results and recommendations from the server and presents them in a user-friendly interface. Using visual graphs and charts, users can review suggestions based on their own consumption patterns and emotions.

[0778] Step 6:

[0779] Users select an energy management plan based on the information presented. If they feel fatigued, they can adjust the room temperature or optimize lighting according to the device's suggestions to reduce stress.

[0780] Step 7:

[0781] The server simulates and evaluates the effects of introducing renewable energy. Based on specific conditions, it calculates cost reductions for solar power generation and energy storage systems, and sends the results to the terminal.

[0782] Step 8:

[0783] The terminal presents the user with simulation results, helping them understand the benefits and cost-effectiveness of renewable energy. If necessary, the user can consider implementing the system.

[0784] Step 9:

[0785] The server generates an optimal schedule to avoid peak power demand times and automatically controls devices based on this schedule. It also manages energy consumption in a way that minimizes stress, taking into account the user's emotional state.

[0786] Step 10:

[0787] Users monitor the automatically controlled system, manually fine-tune settings as needed, and achieve efficient energy use while improving their quality of life.

[0788] (Example 2)

[0789] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0790] Modern energy management systems often make suggestions based solely on consumption data to improve efficiency. However, it has been pointed out that users' emotional states influence energy consumption, so there is a need to optimize energy efficiency while considering emotional states. Furthermore, it is also a challenge to grasp the effects of introducing renewable energy in real time and to make suggestions tailored to the individual circumstances of each user.

[0791] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0792] In this invention, the server includes means for collecting energy data in real time and analyzing consumption patterns, means for recognizing emotional states and reflecting them in energy management plans, and means for simulating the effects of introducing renewable energy sources using predictive models. This enables effective energy management and improved quality of life by providing optimal energy-efficient suggestions tailored to the user's emotional state.

[0793] "Energy data" refers to information related to electricity and other energy consumption, showing the usage of various devices and systems in home and office environments.

[0794] "Real-time" refers to acquiring information instantly and processing it without delay, resulting in a state where information responds immediately to user actions and changes in the environment.

[0795] "Consumption patterns" refer to regularities in how energy and other resources are used, and describe usage trends based on past data.

[0796] "Analysis" refers to the process of examining data and information in detail to reveal its structure and characteristics, and in particular, it includes identifying trends and anomalies in energy data.

[0797] "Automatic generation" refers to a system creating new data or information on its own based on algorithms and rules.

[0798] A "predictive model" refers to a method or algorithm that uses historical data and statistical techniques to estimate future trends and outcomes.

[0799] "Renewable energy sources" refer to inexhaustible and indestructible energy sources found in nature, such as solar, wind, hydro, and geothermal energy.

[0800] A "simulation" is a method of analyzing the impact of specific conditions or variables on the outcome by imitating actual operations.

[0801] A "schedule" refers to a detailed plan or schedule outlining when and how specific activities or operations will be carried out.

[0802] "Emotional state" refers to the user's mental and emotional changes, including states such as stress and relaxation.

[0803] "Energy efficiency suggestions" refer to recommended actions and settings aimed at optimizing users' energy consumption and reducing waste.

[0804] This invention provides a system that comprehensively analyzes energy data and the user's emotional state in order to optimize energy management. A specific embodiment is configured as follows.

[0805] The server collects energy data in real time from home and office electricity meters and IoT devices. This includes devices such as smart meters and network-connected temperature control devices. The server receives this data and performs data analysis using machine learning libraries such as TensorFlow. This analysis models the consumption patterns of each device, enabling the detection of trends and anomalies.

[0806] The emotion engine also collects emotional information from wearable devices worn by the user. The devices measure biosignals such as heart rate and skin temperature and transmit this information to the emotion engine. The emotion engine analyzes this information to understand the user's stress level and relaxation level.

[0807] The terminal proposes an energy management plan tailored to the user based on energy analysis results obtained from the server and emotional information from the emotion engine. This interface is user-friendly; for example, if it receives data indicating the user is fatigued, it will suggest room temperature settings and lighting adjustments to promote relaxation.

[0808] In addition, the server has the capability to use predictive models to simulate the effects of introducing renewable energy. For example, it can show users which type of renewable energy is best suited to their environmental conditions. It can also utilize data from an emotion engine to provide suggestions based on user interests.

[0809] For example, if a user returns home from work feeling stressed, the system can help create a relaxing environment by changing the lighting to a warmer color or setting the room temperature to a comfortable level. An example of a prompt to input into the generating AI model would be, "Generate suggestions for optimal energy use and emotional responses for the following time period."

[0810] Thus, this system aims to provide intelligent support that contributes not only to energy management but also to improving the quality of life for users.

[0811] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0812] Step 1:

[0813] The server collects energy data in real time from electricity meters and IoT devices in homes and offices. Inputs are power consumption data from smart meters and networked temperature control devices, and outputs are real-time energy consumption records compiled from this data. Specifically, the server periodically accesses devices, retrieves power consumption figures, and stores them in a database.

[0814] Step 2:

[0815] The server analyzes the collected energy data. The input is the power consumption records obtained in step 1, and the output is the consumption patterns of each device and anomaly detection information. Here, machine learning libraries such as TensorFlow are used to analyze data trends and detect abnormal consumption behavior.

[0816] Step 3:

[0817] The user wears a wearable device for emotion recognition and provides data on their emotional state. The input is biosignal information such as heart rate and skin temperature obtained from the device, and the output is the user's emotional state (stress level and relaxation level). Specifically, the device sends the collected data to an emotion engine, where the data is analyzed.

[0818] Step 4:

[0819] The server receives emotional state data from the emotion engine and integrates it with energy data. The input is the user's emotional state data and energy consumption patterns, and the output is a pre-adjusted energy management plan tailored to the user's state. The server fuses this data to create an optimal plan based on the user's emotions.

[0820] Step 5:

[0821] The terminal receives integrated analysis results from the server and displays an energy management plan tailored to the user. The input is the analysis results from the server, and the output is an energy efficiency suggestion in a user-friendly format. Specifically, the terminal provides suggestions for lighting settings and temperature adjustments based on the user's situation, both on screen and via audio.

[0822] Step 6:

[0823] The server simulates the effects of introducing renewable energy using a predictive model and proposes the best energy solution to the user based on the results. The input is the conditions for introducing renewable energy and environmental data, and the output is the simulation results of the introduction effects. Based on the simulation results, the server makes suggestions that enable the user to utilize renewable energy most effectively.

[0824] (Application Example 2)

[0825] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0826] Conventional energy management systems simply aim to improve energy efficiency based on consumption data, without considering the individual emotional states of users. Therefore, they fail to provide energy management that aligns with user comfort and motivation, resulting in a lack of more efficient and effective energy usage methods. This invention aims to solve these problems.

[0827] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0828] In this invention, the server includes means for collecting energy data in real time and analyzing consumption patterns, means for generating information to visualize the consumed energy, means for automatically generating recommendations for improving energy efficiency based on the analysis results, and means for sensing the user's emotional state and adjusting the energy management plan accordingly. This enables more personalized energy management suggestions that take the user's emotional state into consideration.

[0829] "Energy data" refers to data showing the usage of various power-consuming devices in homes and offices.

[0830] "Consumption patterns" refer to specific tendencies or habits that indicate the times of day and situations in which energy is used.

[0831] "Means of real-time collection" refers to technological means for instantly collecting energy data and understanding current usage patterns.

[0832] "Means for automatically generating recommendations for improving energy efficiency" refers to technology that mechanically derives specific suggestions for optimizing energy use based on analyzed data.

[0833] "A method for simulating the effects of introducing renewable energy sources using predictive models" refers to a process of calculating the expected changes in efficiency and cost resulting from the utilization of renewable energy sources.

[0834] "Means of providing schedules to curb peak power consumption" refers to technologies that limit power use during periods of high demand and create plans for efficient energy utilization.

[0835] "Means for automatically controlling devices" refers to a system that automatically adjusts the operation of equipment based on pre-set conditions.

[0836] "Means of sensing the user's emotional state and adjusting the energy management plan accordingly" refers to technology that evaluates the user's mental and emotional state and customizes energy usage suggestions based on that data.

[0837] This invention is a system that optimizes energy management based on energy data and the user's emotional state. The server collects energy data in real time from various power-consuming devices in homes and offices. This data is used to analyze consumption patterns, and machine learning algorithms such as TensorFlow are applied to the analysis process. The server also has an emotion engine to sense the user's emotional state and collects data using heart rate and facial recognition technology.

[0838] This system provides automatically generated recommendations for improving energy efficiency. For example, based on analyzed data, the server may suggest adjusting room temperature or lighting settings. It can also use predictive models to simulate the effects of introducing renewable energy and visualize energy savings and cost fluctuations. In this way, a schedule is created to reduce peak power consumption, and devices are automatically controlled.

[0839] Users receive energy management suggestions based on their emotional state through the app. This allows for the efficient use of energy resources while improving comfort in daily life. For example, a user returning home after being out for a long time might have the lighting automatically changed to a warmer color to help alleviate stress.

[0840] An example of a prompt to a generative AI model is, "Based on the user's emotional state upon returning home, how can we provide the optimal indoor environment?" By using such prompts, the system can suggest energy management strategies optimized for the user.

[0841] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0842] Step 1:

[0843] The server collects energy data from each device in real time. The collected data is instantly stored in a database and used as input to understand the current power consumption of each device. The data processing performed at this stage involves organizing the collected power usage as time-series data and checking for any outliers.

[0844] Step 2:

[0845] The server analyzes the collected energy data and derives consumption patterns. Using machine learning algorithms, it identifies normal and abnormal consumption patterns and outputs the results as an analysis report. Here, it performs trend analysis by comparing with past data to identify peak times and periods when energy conservation is possible.

[0846] Step 3:

[0847] The server uses an emotion engine to detect the user's emotional state. It obtains heart rate and facial expression data from the user's smartphone sensors and uses this as input to infer the emotional state. The inference results are then used as input for specific action suggestions.

[0848] Step 4:

[0849] The server integrates the analysis results of energy data with the output of the emotion engine to generate recommendations for improving energy efficiency. This process utilizes a generative AI model to generate optimal suggestions tailored to the user's mood. These suggestions may include adjusting the temperature or lighting within the home.

[0850] Step 5:

[0851] The terminal provides the user with recommendations obtained from the server. The user receives the suggestions through the application and changes the settings as needed. Specifically, they can choose to manually adjust the room temperature setting or allow automatic control.

[0852] Step 6:

[0853] Users submit feedback via the terminal interface to evaluate the effectiveness of the recommendations. This feedback is collected by the server and used to improve the accuracy of future data analysis, as it helps improve the overall system.

[0854] These processing steps enable a highly efficient energy management system that takes into account the user's emotional state.

[0855] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0856] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0857] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0858] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0859] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0860] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0861] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0862] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0863] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0864] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0865] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0866] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0867] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0868] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0869] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0870] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0871] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0872] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0873] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0874] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0875] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0876] The following is further disclosed regarding the embodiments described above.

[0877] (Claim 1)

[0878] A means of collecting energy data in real time and analyzing consumption patterns,

[0879] A means for generating information to visualize the energy consumed,

[0880] A means for automatically generating recommendations for improving energy efficiency based on analysis results,

[0881] A method for simulating the effects of introducing renewable energy sources using predictive models,

[0882] It provides a schedule to suppress peak power consumption and a means to automatically control devices,

[0883] A system that includes this.

[0884] (Claim 2)

[0885] The system according to claim 1, which applies a machine learning algorithm to data analysis and makes optimized suggestions according to the user's energy usage pattern.

[0886] (Claim 3)

[0887] The system according to claim 1, comprising a function for calculating energy reduction and cost reduction effects in a simulation of renewable energy sources.

[0888] "Example 1"

[0889] (Claim 1)

[0890] A means of collecting energy data over time and analyzing consumption trends,

[0891] A means of creating information to visualize the usage information of consumed energy,

[0892] A means for automatically generating recommendations to improve energy utilization efficiency based on analysis results,

[0893] A method for virtually estimating the effects of introducing sustainable energy sources using a prediction device,

[0894] It provides a timetable to limit energy consumption during peak hours and means to automatically operate the equipment.

[0895] A device that includes this.

[0896] (Claim 2)

[0897] The system according to claim 1, which utilizes machine learning techniques in data analysis to provide optimized suggestions tailored to the user's energy consumption patterns.

[0898] (Claim 3)

[0899] The system according to claim 1, comprising a function for calculating energy reductions and cost reduction effects in a hypothetical simulation of sustainable energy sources.

[0900] "Application Example 1"

[0901] (Claim 1)

[0902] A means of collecting energy data in real time and analyzing consumption patterns,

[0903] A means for generating information to visualize the energy consumed,

[0904] A means for automatically generating recommendations for improving energy efficiency based on analysis results,

[0905] A method for simulating the effects of introducing renewable energy sources using predictive models,

[0906] A means to provide a schedule for suppressing peak power consumption and to automatically control the device,

[0907] A means of optimizing energy consumption in public facilities and infrastructure, and providing information that contributes to energy conservation in daily life,

[0908] A system that includes this.

[0909] (Claim 2)

[0910] The system according to claim 1, which applies a machine learning algorithm to data analysis and makes optimized suggestions according to the user's energy usage pattern.

[0911] (Claim 3)

[0912] The system according to claim 1, which has a function to calculate energy reduction and cost reduction effects in a simulation of renewable energy sources, and aims to improve the energy efficiency of the entire city.

[0913] "Example 2 of combining an emotion engine"

[0914] (Claim 1)

[0915] A means of collecting energy data in real time and analyzing consumption patterns,

[0916] A means for generating information to visualize the energy consumed,

[0917] A means for automatically generating recommendations for improving energy efficiency based on analysis results,

[0918] A method for simulating the effects of introducing renewable energy sources using predictive models,

[0919] A means to provide a schedule for suppressing peak power consumption and to automatically control the device,

[0920] A means of recognizing emotional states and reflecting them in an energy management plan,

[0921] A means of providing the most suitable energy-efficient suggestions to the user based on their emotional state,

[0922] A system that includes this.

[0923] (Claim 2)

[0924] The system according to claim 1, which applies a machine learning algorithm to data analysis and provides optimized suggestions according to the user's energy usage patterns and emotional state.

[0925] (Claim 3)

[0926] The system according to claim 1, which has a function to calculate energy reduction and cost reduction effects in a simulation of renewable energy sources and to make suggestions according to the user's emotional state.

[0927] "Application example 2 when combining with an emotional engine"

[0928] (Claim 1)

[0929] A means of collecting energy data in real time and analyzing consumption patterns,

[0930] A means for generating information to visualize the energy consumed,

[0931] A means for automatically generating recommendations for improving energy efficiency based on analysis results,

[0932] A method for simulating the effects of introducing renewable energy sources using predictive models,

[0933] A means to provide a schedule for suppressing peak power consumption and to automatically control the device,

[0934] A means of sensing the user's emotional state and adjusting the energy management plan based on that,

[0935] A system that includes this.

[0936] (Claim 2)

[0937] The system according to claim 1, which applies machine learning algorithms to data analysis and provides optimized suggestions according to the user's energy usage patterns and emotional state.

[0938] (Claim 3)

[0939] The system according to claim 1, comprising a function to calculate energy reductions and cost reduction effects in a simulation of renewable energy sources, and to customize these suggestions based on the user's sentiment. [Explanation of symbols]

[0940] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting energy data in real time and analyzing consumption patterns, A means for generating information to visualize the energy consumed, A means for automatically generating recommendations for improving energy efficiency based on analysis results, A method for simulating the effects of introducing renewable energy sources using predictive models, A means to provide a schedule for suppressing peak power consumption and to automatically control the device, A means of optimizing energy consumption in public facilities and infrastructure, and providing information that contributes to energy conservation in daily life, A system that includes this.

2. The system according to claim 1, which applies a machine learning algorithm to data analysis and makes optimized suggestions according to the user's energy usage pattern.

3. The system according to claim 1, which has a function to calculate energy reduction and cost reduction effects in a simulation of renewable energy sources, and aims to improve the energy efficiency of the entire city.

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