system

Abstract

We provide the system. [Solution] A data acquisition method for obtaining power consumption information, A forecasting method that performs time-series data analysis based on acquired power consumption information to forecast demand, An optimization means for generating an efficient energy use plan based on predicted demand, A display means for visually presenting power consumption information and optimized plans to the user, A system that includes this.

Description

Technical Field

[0001] The technology of this 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 the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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 recent years, due to the increase in energy consumption, fluctuations in energy prices, and the growing awareness of climate change, efficient and sustainable energy management is required in homes and businesses. However, conventional energy management systems rely on individual devices and manual monitoring, making it difficult to grasp the real-time energy consumption situation. For this reason, users cannot easily set an optimal energy usage plan, resulting in wasted costs and continued use of energy with a high environmental impact. It is an object of the present invention to provide a system that improves such a situation and realizes effective and convenient energy management.

Means for Solving the Problems

[0005] This invention provides an energy management system that includes data acquisition means for acquiring power consumption information, forecasting means for performing demand forecasting, optimization means for generating an optimal energy use plan, and display means for presenting information to the user. The data acquisition means collects data in real time from sensors and external information sources. The forecasting means performs time-series analysis based on the collected data to predict future energy demand with high accuracy. The optimization means generates an optimal energy use plan that avoids peak demand based on this forecast data. Furthermore, the display means allows the user to check the situation in real time and select from multiple energy use scenarios. This enables the user to optimize energy use and achieve cost reduction and environmental impact reduction.

[0006] "Electricity consumption information" refers to data that shows the electricity usage in a specific space or time.

[0007] "Data acquisition means" refers to a device or method for collecting power consumption information from sensors, smart meters, etc.

[0008] "Demand forecasting" is the process of predicting future electricity consumption based on past data and external factors.

[0009] "Predictive means" refers to algorithms and devices used to calculate future demand for electricity consumption.

[0010] An "optimization means" is a function or device for creating an efficient electricity usage plan based on predicted energy demand.

[0011] "Display means" refers to displays or interfaces that visually provide users with power consumption information and forecast results.

[0012] An "energy use plan" is a plan outlining how energy will be used within a specified period.

[0013] "User" refers to an individual or organization that operates the energy management system and utilizes its output results.

[0014] A "scenario" refers to multiple energy usage patterns predicted based on different assumptions and conditions. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This 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] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This 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 Embodiment 2 when a sentiment engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when a sentiment engine is combined.

Mode for Carrying Out the Invention

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of 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.

[0019] 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.

[0020] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0021] 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).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] The energy management system of the present invention aims to improve the efficiency of power consumption by integrating data acquisition means, prediction means, optimization means, and display means. The specific operation of each component is described below.

[0037] Data acquisition means

[0038] The server collects real-time power consumption information from smart meters and IoT sensors installed in homes and businesses. In addition, it acquires external factor information such as weather data and electricity market price information and stores it in a database. This allows various factors that affect energy use to be taken into consideration.

[0039] Prediction methods

[0040] The server learns consumption patterns using machine learning algorithms based on the acquired data. Based on past data, it predicts future energy demand and identifies peak times and consumption levels. This enables accurate prediction of energy consumption.

[0041] Optimization methods

[0042] The server uses the forecast results to automatically generate an efficient energy usage plan. For example, it suggests scheduling adjustments to reduce peak power consumption and proposes ways to reduce unnecessary energy use. This plan is customized to the user's energy-saving goals.

[0043] Display means

[0044] The device provides users with a dashboard that visualizes real-time energy consumption, predicted consumption patterns, and optimization plans. Based on this information, users can adjust their energy usage as needed to reduce costs.

[0045] Specific example

[0046] As a concrete example of its application, consider household energy management during the daytime in summer. The server monitors power consumption in real time and predicts when air conditioner usage will reach its peak. To avoid the peak, the server suggests raising the air conditioner's temperature setting or using other low-power modes. The terminal immediately notifies the user of this suggestion, and the user can adjust accordingly, improving the efficiency of power usage.

[0047] In this way, the system of the present invention effectively manages users' energy use through real-time data analysis and AI prediction, thereby supporting sustainable energy use.

[0048] The following describes the processing flow.

[0049] Step 1:

[0050] The server collects real-time power consumption data from smart meters and IoT sensors. It also obtains weather information and market electricity price data via external APIs. This data is stored in a database and used for subsequent analysis.

[0051] Step 2:

[0052] The server preprocesses the collected data. This includes filtering outliers and imputing missing data. Then, it prepares the data for learning past consumption patterns using time series analysis.

[0053] Step 3:

[0054] The server uses the organized data to run machine learning algorithms and predict future electricity demand. This predictive model is used to identify peak demand and promote efficient energy use.

[0055] Step 4:

[0056] The server uses optimization techniques based on predicted data to generate an efficient energy usage plan. This includes suggestions such as peak shifting and power usage limits.

[0057] Step 5:

[0058] The device displays real-time energy consumption, forecasts, and optimized plans to the user on a dashboard. This allows the user to see the current situation and recommended actions at a glance.

[0059] Step 6:

[0060] Users can compare different energy usage scenarios using the simulation function provided by the device. This allows users to select the most effective energy management method and make decisions about its implementation.

[0061] Step 7:

[0062] The server collects the results of user actions as data again and uses this feedback to retrain the model. This improves the accuracy of future predictions and enables further energy efficiency.

[0063] (Example 1)

[0064] 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."

[0065] In modern society, the efficient use of energy resources is a crucial issue. Accurately predicting actual energy demand and optimizing energy use accordingly contributes to the realization of a sustainable society. However, traditional energy management systems struggle to respond to real-time demand fluctuations and lack concrete means to predict and mitigate peak demand. Against this backdrop, a system is needed to achieve more accurate and dynamic energy management.

[0066] 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.

[0067] In this invention, the server includes information gathering means for acquiring power consumption information, forecasting means for performing numerical analysis based on the acquired power consumption information and conducting demand forecasting, and optimization means for generating an efficient energy use plan based on the forecasted demand. This makes it possible to learn consumption patterns, identify peak demand, and dynamically adjust the energy use plan in real time based on that demand.

[0068] An "information gathering tool" is a mechanism that acquires data on energy consumption in households and businesses in real time and stores it in a database.

[0069] "Numerical analysis" is a technology that uses machine learning algorithms to analyze consumption patterns based on collected electricity consumption data and predict future energy demand.

[0070] A "predictive method" is a technique for accurately predicting future energy demand based on past consumption data.

[0071] "Optimization methods" refer to the process of formulating an energy use optimization plan based on predicted energy demand and proposing a specific schedule to reduce peak consumption.

[0072] A "display means" is an interface that visually displays to the user real-time power consumption status, predicted demand, and an optimized energy plan.

[0073] "Learning consumption patterns" is the process of using machine learning to analyze past energy usage history and identify future usage trends.

[0074] "Real-time dynamic adjustment" refers to the ability to instantly update energy use plans when energy demand forecasts or optimization plans change, and to continuously implement optimized energy use.

[0075] The energy management system in this invention aims to improve the efficiency of power consumption by integrating information gathering, prediction, optimization, and display. The specific operation of each component is described below.

[0076] Information gathering

[0077] The server acquires real-time power consumption information from smart meters and IoT sensors installed in homes and businesses. This information is securely transmitted to the server via a data collection protocol and stored in a database. Furthermore, the server acquires weather information and market price information from external data sources, comprehensively capturing various factors that affect energy consumption.

[0078] prediction

[0079] The server preprocesses the information in the database using Python's Pandas and NumPy libraries and applies it to machine learning algorithms. Models using Scikit-learn and TENSORFLOW® learn consumption patterns and predict future energy demand based on past data. This predictive model makes it possible to identify peak demand in advance.

[0080] optimization

[0081] The server generates efficient energy usage plans using Pyomo and SciPy based on predicted energy demand. For example, it suggests schedules to limit peak consumption or adjusts the use of specific devices to reduce unnecessary consumption. This creates a customized plan in real time that is tailored to the user's energy-saving goals.

[0082] display

[0083] The device provides users with real-time power consumption data, predicted patterns, and optimized plans via a web-based dashboard. Applications using Flask or Django visualize this information, allowing users to adjust their energy usage based on this information to reduce costs and achieve efficient energy management.

[0084] Specific example

[0085] For example, consider the energy usage of a home during the daytime in summer. The server predicts the time of day when the air conditioner will be used most and creates a suggestion to raise the temperature to avoid that peak. The terminal immediately notifies the user of this suggestion, and by adjusting the air conditioner settings, the user can use electricity efficiently.

[0086] Example of a prompt

[0087] "Predict the peak electricity demand for a specific household tomorrow and generate specific methods to reduce the peak load."

[0088] In this way, the system of the present invention uses machine learning and data analysis to provide users with optimal energy use and support sustainable consumption.

[0089] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0090] Step 1:

[0091] The server acquires power consumption information in real time from smart meters and IoT sensors. It receives power data transmitted from these sensors as input and stores it in a database. External factors such as weather data and electricity market price information are also acquired and integrated into the database. Throughout this process, secure communication is maintained based on data collection protocols.

[0092] Step 2:

[0093] The server preprocesses the information stored in the database using data processing libraries such as Pandas and NumPy. The input for preprocessing is the stored raw data, and the output is clean data with missing values ​​and outliers removed. This clean data is then prepared for machine learning in the next step.

[0094] Step 3:

[0095] The server inputs clean data into a machine learning model using Scikit-learn or TensorFlow to learn consumption patterns. Here, the input is preprocessed energy consumption data, and the output is a prediction of future energy demand. This makes it possible to identify peak demand. Training the model involves regression analysis and predictive calculations based on historical data.

[0096] Step 4:

[0097] The server uses optimization algorithms built with Pyomo and SciPy based on the forecast results to generate an efficient energy use plan. It takes forecasted demand data as input and includes an adjusted energy schedule as output. Specifically, it adjusts the operating times of specific devices and develops plans that reduce peak power usage.

[0098] Step 5:

[0099] The terminal receives display data sent from the server. It receives energy consumption information, forecast data, and optimized plans as input, and visualizes this information for the user on a dashboard. The output is a visual information display, based on a web application using Flask or Django. Users can adjust their energy usage based on this information.

[0100] Step 6:

[0101] Users adjust their energy usage behavior based on information presented on the device. Input is the information provided by the device, and output is the user's manual or automated actions. This allows users to reduce electricity costs and improve energy efficiency.

[0102] (Application Example 1)

[0103] 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."

[0104] Improving energy efficiency in physical stores directly leads to reduced environmental impact and lower operating costs. However, conventional technologies have not adequately analyzed energy usage within stores in real time and provided appropriate plans. Therefore, there is a need for new technologies that can improve store operational efficiency through the automation and optimization of energy management.

[0105] 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.

[0106] In this invention, the server includes information acquisition means for obtaining information related to power usage, prediction means for performing time-series analysis based on the acquired information and conducting consumption forecasts, and plan generation means for generating an effective energy usage plan based on the predicted consumption. This makes it possible to monitor the energy consumption status of physical stores in real time and automatically generate an effective plan.

[0107] "Information related to electricity usage" refers to data that shows the amount and patterns of electricity consumed in homes, businesses, stores, etc.

[0108] "Information acquisition means" refers to a device or system that collects information related to electricity usage using smart meters or various sensors.

[0109] Time series analysis is a method that analyzes acquired data in chronological order to reveal consumption trends and patterns.

[0110] "Consumption forecasting" is the process of estimating future energy consumption based on past data.

[0111] "Predicted consumption" refers to the projected future electricity usage calculated through time-series analysis.

[0112] An "effective energy use plan" is a feasible plan designed to maximize energy efficiency and reduce costs.

[0113] A "plan generation means" is a device or system that creates and proposes an optimized energy use plan based on predicted consumption.

[0114] A "physical store" refers to a physical location where commercial activities take place and where customers can visit in person.

[0115] A "prompt statement" is an instruction statement that is input into a generative AI model to generate information.

[0116] "Support measures" refer to devices or systems equipped with visual information provision or simulation functions to assist users in making decisions.

[0117] The system that realizes this invention supports the operation of physical stores by efficiently collecting and analyzing information related to electricity usage and generating an optimized energy usage plan.

[0118] The server collects real-time electricity usage data from within the store through an information acquisition system using smart meters and various IoT sensors. This data is then analyzed via a time-series analysis module to reveal consumption patterns and trends, and to predict future electricity usage.

[0119] Based on predicted data, the server's planning generation module utilizes machine learning algorithms with TensorFlow to create an efficient energy usage plan. This includes scheduling adjustments to avoid peak power consumption times within the store and measures to reduce unnecessary energy use.

[0120] The terminal visually presents energy consumption information and optimized plans to the user through a Plotly-based user interface. Based on this information, the user can make decisions regarding energy use in physical stores.

[0121] Furthermore, by utilizing a generative AI model that uses prompts, users can easily ask questions about energy management and receive appropriate advice. An example of a prompt is, "Create a Python script that predicts peak usage times based on energy consumption data from physical stores and proposes an optimal energy usage plan."

[0122] As a concrete example, a cafe in a bustling commercial district has achieved cost reductions by effectively managing electricity consumption during peak hours. In this way, the system can be customized to meet the needs of individual stores, supporting sustainable business operations.

[0123] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0124] Step 1:

[0125] The server collects electricity usage data in real time from smart meters and various IoT sensors. Electricity usage data is provided as input, and the server stores this data in a database. The output here is the electricity usage information stored in the database.

[0126] Step 2:

[0127] The server applies a time-series analysis algorithm to the collected power usage data. The input is the power usage information obtained in step 1, and based on this, it analyzes consumption patterns and predicts future power demand. The output is a graph of the predicted consumption pattern.

[0128] Step 3:

[0129] The server uses predicted consumption data to generate an efficient energy use plan using a machine learning algorithm powered by TensorFlow. The input is the predicted consumption data from step 2, and the program proposes an optimal energy use schedule. The output is the optimized energy use plan.

[0130] Step 4:

[0131] The terminal uses Plotly to visually present energy consumption information and the generated energy usage plan to the user. The input is the data obtained in steps 1, 2, and 3, which the terminal graphically displays on the dashboard. The output is visualized energy usage information that the user can review.

[0132] Step 5:

[0133] The user inputs prompts and questions to the system via a generative AI model. Based on this input, the server provides the user with appropriate energy optimization advice. The generated output consists of specific action plans and additional information that the user can implement.

[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 an energy usage plan that reflects the user's emotional state by integrating an emotion engine in addition to a power consumption management system. This system consists of data acquisition means, prediction means, optimization means, display means, and an emotion engine.

[0136] Emotional Engine

[0137] 1. Emotion recognition

[0138] The server extracts emotional data from the user's facial expressions and voice through camera and microphone sensors. This data is analyzed using a machine learning model to determine the user's current emotional state (e.g., stress, relaxation, joy).

[0139] 2. Use of emotional data

[0140] The server adjusts the energy usage plan generated by optimization means based on emotional data obtained from the emotion engine. For example, if the user is stressed, the system will suggest an energy usage plan that promotes relaxation.

[0141] Data acquisition and prediction

[0142] The server collects power consumption information from smart meters and weather data as an external factor. Using this database, machine learning algorithms are used to predict future power demand.

[0143] Optimization methods

[0144] The server generates an energy usage plan tailored to individual needs based on predictive and emotional data. For example, if a user is fatigued, it suggests a temperature setting that maintains comfort while avoiding peak hours.

[0145] Display means

[0146] The device presents the user with a dashboard showing their power consumption status, forecast results, and optimized plans. In addition, the user's emotional state is visually represented, allowing them to understand how the plan has been adjusted.

[0147] Specific example

[0148] For example, at night when a user feels tired, the server generates a plan to adjust the lighting brightness appropriately. The terminal notifies the user of this plan, and the user changes their environment accordingly. In this way, energy consumption is optimized while also caring for the user's emotional state.

[0149] The system of this invention utilizes emotional data for energy management, thereby realizing a more precise and personalized method of energy use.

[0150] The following describes the processing flow.

[0151] Step 1:

[0152] The server acquires real-time power consumption data from smart meters and sensors. Simultaneously, it collects weather data and market electricity prices using external APIs and stores this information in a database.

[0153] Step 2:

[0154] The server uses collected power consumption data and external factor data to perform time-series data analysis using machine learning algorithms. This allows it to predict future power demand and identify peak demand times.

[0155] Step 3:

[0156] The server acquires the user's facial expressions and voice data through the camera and microphone. This data is analyzed by an emotion engine to evaluate the user's emotional state in real time. This evaluation information is managed within the system along with other data.

[0157] Step 4:

[0158] The server generates an energy usage plan using optimization methods, taking into account predicted power demand and the user's emotional state. Based on the emotional data, it suggests adjusting the temperature and lighting to provide a relaxing environment for the user.

[0159] Step 5:

[0160] The device presents the generated energy usage plan to the user via a dashboard interface. The dashboard visually displays details of the current power consumption, demand forecast, and an optimized plan tailored to the user's emotional state.

[0161] Step 6:

[0162] Users can review the energy usage plan presented through their device and manually adjust settings as needed. They can also utilize the simulation function to compare different scenarios and adopt the optimal energy management approach.

[0163] Step 7:

[0164] The server collects and records feedback derived from user behavior and emotions. This data is used to retrain predictive models and the emotion engine, helping to continuously improve the overall system accuracy and user experience.

[0165] (Example 2)

[0166] 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".

[0167] The challenge lies in achieving efficient energy management while simultaneously proposing individualized energy usage plans that are sensitive to the emotional state of users. Conventional energy management systems provide plans based on electricity consumption and demand forecasts, but they do not take into account the emotional needs of users, thus improving the user experience is essential.

[0168] 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.

[0169] In this invention, the server includes information acquisition means, demand forecasting means, emotion recognition means, optimization means, and display means. This makes it possible to propose an effective and emotionally sensitive energy usage plan based on the user's power consumption patterns and emotional state.

[0170] "Information acquisition means" refers to a device or function that collects power consumption information and external environmental information.

[0171] A "demand forecasting tool" is a device or function that performs time-series data analysis on acquired information to predict future electricity demand.

[0172] "Emotion recognition means" refers to a device or function that analyzes a user's voice and video to determine their emotional state.

[0173] "Optimization means" refers to a device or function that generates an efficient and personalized energy usage plan based on predicted demand and the user's emotional state.

[0174] "Display means" refers to a device or function for visually presenting to the user information such as power consumption, optimization plans, and emotional state.

[0175] This invention is a system that integrates an emotion engine into power consumption management and provides an energy usage plan tailored to the user's emotional state. This system consists of multiple components such as a server, terminals, and sensors.

[0176] The server first uses information acquisition tools to collect power consumption information from smart meters and environmental data from external data sources. The server also monitors the user's facial expressions and voice through sensors such as cameras and microphones, and analyzes emotional data using emotion recognition tools. Machine learning models are used to determine the user's emotional state.

[0177] The server uses demand forecasting tools to predict future electricity demand from acquired power consumption information and environmental data. This process employs algorithms such as time series analysis. Subsequently, optimization tools are used to generate an energy usage plan tailored to the individual user, based on the predicted demand and emotional state. This plan takes into account factors such as reducing peak load and improving comfort.

[0178] The device displays an optimized energy usage plan to the user in a dashboard format. The display visually presents power consumption, emotional state, and plan adjustments. Based on this information, the user can adjust their environment to improve both energy efficiency and comfort simultaneously.

[0179] For example, if a user is fatigued, the server generates an energy usage plan that adjusts the room lighting to a warmer color and the music volume, and notifies the user of this suggestion via their device. In this way, an integrated plan for energy and emotional management is provided. This system is expected to improve the efficiency of energy management and enhance the user experience.

[0180] A concrete example of a prompt for a generative AI model might be, "Please suggest an energy usage plan that takes into account the user's current emotional state." This prompt allows the system to generate a plan that considers the user's emotional state.

[0181] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0182] Step 1:

[0183] The server collects power consumption information from smart meters and environmental data from external data sources using information acquisition methods. The inputs are power consumption information and environmental data, which are used directly in the next processing step. Specifically, it periodically acquires data from sensors and stores it in a database.

[0184] Step 2:

[0185] The server collects the user's facial expressions and voice through the camera and microphone, and analyzes the emotional data using emotion recognition technology. The input is the user's facial expressions and voice information, and the output is the emotional state (e.g., stress, relaxation, joy). Specifically, the acquired data is input into a machine learning model to determine the emotional state.

[0186] Step 3:

[0187] The server uses demand forecasting tools to perform time-series analysis based on the power consumption information and environmental data collected in Step 1 to predict future power demand. The input is power consumption information and environmental data, and the output is the predicted power demand. Specifically, it uses machine learning algorithms to analyze demand trends.

[0188] Step 4:

[0189] The server uses optimization techniques to generate an energy usage plan based on the emotional state in step 2 and the power demand forecast in step 3. The inputs are the emotional state and the power demand forecast, and the output is the optimized energy usage plan. Specifically, it creates a plan to avoid predicted peak times and adds settings that take the emotional state into consideration.

[0190] Step 5:

[0191] The terminal displays a dashboard-style presentation of an energy usage plan, power consumption status, and emotional state optimized for the user. Inputs are the generated energy usage plan and analyzed data, while output is a visual display on the dashboard. Specifically, the latest plan information is placed on the GUI for easy user access.

[0192] (Application Example 2)

[0193] 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".

[0194] Modern autonomous vehicles, a vital mode of transportation, require improved passenger comfort and safety. However, conventional systems struggle to instantly adjust the in-car environment to reflect the passenger's emotional state, resulting in an inability to effectively reduce passenger stress and discomfort. To address this, it is necessary to rapidly optimize the in-car environment in response to changes in the passenger's emotions and efficiently manage energy consumption.

[0195] 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.

[0196] In this invention, the server includes data acquisition means for obtaining power consumption information, emotion recognition means for recognizing the emotional state of the occupants, and environment adjustment means for adjusting the in-vehicle environment based on the recognized emotional state. This makes it possible to optimize energy use while responding quickly to the emotional state of the occupants.

[0197] "Power consumption information" refers to data that shows how electricity is being used, including consumption per hour and power consumption for specific devices.

[0198] "Data acquisition means" refers to a device or method for collecting necessary data such as power consumption information and external factor information.

[0199] "Demand forecasting" is the process of predicting future electricity consumption based on acquired data, and includes time-series data analysis.

[0200] "Optimization means" refers to techniques or methods for designing efficient energy use plans in response to predicted demand.

[0201] "Display means" refers to an interface or display device for visually presenting information or plans to a user.

[0202] "Emotion recognition means" refers to technology or devices that analyze the facial expressions and voices of crew members to determine their emotional state at a given time.

[0203] "Environmental adjustment means" refers to a device or method for appropriately changing the in-vehicle environment based on recognized emotional states.

[0204] This invention provides a system primarily for autonomous vehicles. The system aims to improve occupant safety and comfort, as well as to optimize energy consumption. Its main components, such as servers, terminals, and users, function in a collaborative manner.

[0205] The server collects power consumption information using data acquisition methods. Based on this, it predicts future power demand based on time-series data analysis and generates an efficient energy use plan through optimization methods. It also uses emotion recognition methods such as cameras and microphones to recognize the emotional state of the occupants. This emotion data is used as an environmental adjustment method to adjust the vehicle's air conditioning, lighting, and other settings.

[0206] The device uses a display to present users with power consumption information and optimized plans tailored to their emotional state in a dashboard format. This allows users to visually understand how their plans have been adjusted.

[0207] For example, if passengers experience stress during a long drive, the server can adjust the lighting in real time and play relaxing music to improve passenger comfort. The software used includes OpenCV and an Emotion Recognition model, which enables rapid recognition and analysis of emotions, allowing for appropriate environmental adjustments.

[0208] An example of a prompt message would be: "In autonomous vehicles, we propose a method to understand how occupants are feeling in real time and optimize the in-vehicle environment according to their emotions."

[0209] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0210] Step 1:

[0211] The server collects vehicle power consumption information through data acquisition methods. Inputs include power usage data from devices within the vehicle and external weather information. This data is converted into a format suitable for time-series data analysis, preparing it for analysis.

[0212] Step 2:

[0213] The server performs time-series data analysis based on the acquired power consumption information and conducts demand forecasting. The input is the data collected in step 1, and the output is forecast data showing future power demand. This forecast result is obtained using a machine learning algorithm.

[0214] Step 3:

[0215] The server uses emotion recognition technology to determine the emotional state of the occupants. Real-time video and audio data from in-vehicle cameras and microphones are used as input and analyzed by an Emotion Recognition model. The output is a classification result based on the occupants' emotional state (e.g., relaxed, stressed).

[0216] Step 4:

[0217] The server generates an efficient energy use plan using optimization methods based on predicted demand data and emotional state. Inputs include demand forecast data and emotional state data, and the output is the adjusted energy use plan. Specific actions such as adjusting air conditioning temperature and lighting are proposed.

[0218] Step 5:

[0219] The terminal presents the user with power consumption information and the generated energy usage plan via a display device. This information is visualized on a dashboard. The input is the plan generated in step 4, and the output is the information visually presented to the user.

[0220] Step 6:

[0221] The user can approve or modify the adjustments suggested by the system based on the information provided. If adjustments are made, the information is sent back to the server, and the plan is updated as necessary.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] [Second Embodiment]

[0226] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0227] 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.

[0228] 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).

[0229] 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.

[0230] 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.

[0231] 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).

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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".

[0238] The energy management system of the present invention aims to improve the efficiency of power consumption by integrating data acquisition means, prediction means, optimization means, and display means. The specific operation of each component is described below.

[0239] Data acquisition means

[0240] The server collects real-time power consumption information from smart meters and IoT sensors installed in homes and businesses. In addition, it acquires external factor information such as weather data and electricity market price information and stores it in a database. This allows various factors that affect energy use to be taken into consideration.

[0241] Prediction methods

[0242] The server learns consumption patterns using machine learning algorithms based on the acquired data. Based on past data, it predicts future energy demand and identifies peak times and consumption levels. This enables accurate prediction of energy consumption.

[0243] Optimization methods

[0244] The server uses the forecast results to automatically generate an efficient energy usage plan. For example, it suggests scheduling adjustments to reduce peak power consumption and proposes ways to reduce unnecessary energy use. This plan is customized to the user's energy-saving goals.

[0245] Display means

[0246] The device provides users with a dashboard that visualizes real-time energy consumption, predicted consumption patterns, and optimization plans. Based on this information, users can adjust their energy usage as needed to reduce costs.

[0247] Specific example

[0248] As a concrete example of its application, consider household energy management during the daytime in summer. The server monitors power consumption in real time and predicts when air conditioner usage will reach its peak. To avoid the peak, the server suggests raising the air conditioner's temperature setting or using other low-power modes. The terminal immediately notifies the user of this suggestion, and the user can adjust accordingly, improving the efficiency of power usage.

[0249] In this way, the system of the present invention effectively manages users' energy use through real-time data analysis and AI prediction, thereby supporting sustainable energy use.

[0250] The following describes the processing flow.

[0251] Step 1:

[0252] The server collects real-time power consumption data from smart meters and IoT sensors. It also obtains weather information and market electricity price data via external APIs. This data is stored in a database and used for subsequent analysis.

[0253] Step 2:

[0254] The server preprocesses the collected data. This includes filtering outliers and imputing missing data. Then, it prepares the data for learning past consumption patterns using time series analysis.

[0255] Step 3:

[0256] The server uses the organized data to run machine learning algorithms and predict future electricity demand. This predictive model is used to identify peak demand and promote efficient energy use.

[0257] Step 4:

[0258] The server uses optimization techniques based on predicted data to generate an efficient energy usage plan. This includes suggestions such as peak shifting and power usage limits.

[0259] Step 5:

[0260] The device displays real-time energy consumption, forecasts, and optimized plans to the user on a dashboard. This allows the user to see the current situation and recommended actions at a glance.

[0261] Step 6:

[0262] Users can compare different energy usage scenarios using the simulation function provided by the device. This allows users to select the most effective energy management method and make decisions about its implementation.

[0263] Step 7:

[0264] The server collects the results of user actions as data again and uses this feedback to retrain the model. This improves the accuracy of future predictions and enables further energy efficiency.

[0265] (Example 1)

[0266] 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."

[0267] In modern society, the efficient use of energy resources is a crucial issue. Accurately predicting actual energy demand and optimizing energy use accordingly contributes to the realization of a sustainable society. However, traditional energy management systems struggle to respond to real-time demand fluctuations and lack concrete means to predict and mitigate peak demand. Against this backdrop, a system is needed to achieve more accurate and dynamic energy management.

[0268] 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.

[0269] In this invention, the server includes information gathering means for acquiring power consumption information, forecasting means for performing numerical analysis based on the acquired power consumption information and conducting demand forecasting, and optimization means for generating an efficient energy use plan based on the forecasted demand. This makes it possible to learn consumption patterns, identify peak demand, and dynamically adjust the energy use plan in real time based on that demand.

[0270] An "information gathering tool" is a mechanism that acquires data on energy consumption in households and businesses in real time and stores it in a database.

[0271] "Numerical analysis" is a technology that uses machine learning algorithms to analyze consumption patterns based on collected electricity consumption data and predict future energy demand.

[0272] A "predictive method" is a technique for accurately predicting future energy demand based on past consumption data.

[0273] "Optimization methods" refer to the process of formulating an energy use optimization plan based on predicted energy demand and proposing a specific schedule to reduce peak consumption.

[0274] A "display means" is an interface that visually displays to the user real-time power consumption status, predicted demand, and an optimized energy plan.

[0275] "Learning consumption patterns" is the process of using machine learning to analyze past energy usage history and identify future usage trends.

[0276] "Real-time dynamic adjustment" refers to the ability to instantly update energy use plans when energy demand forecasts or optimization plans change, and to continuously implement optimized energy use.

[0277] The energy management system in this invention aims to improve the efficiency of power consumption by integrating information gathering, prediction, optimization, and display. The specific operation of each component is described below.

[0278] Information gathering

[0279] The server acquires real-time power consumption information from smart meters and IoT sensors installed in homes and enterprises. This information is securely transmitted to the server via a data collection protocol and stored in a database. Furthermore, the server obtains weather information and market price information from external data sources to comprehensively capture various factors that affect energy consumption.

[0280] Prediction

[0281] The server preprocesses the information in the database using Python's Pandas and NumPy and applies it to machine learning algorithms. Models using Scikit-learn and TensorFlow learn the consumption patterns and predict future energy demand based on past data. This prediction model makes it possible to identify the occurrence of peak demand in advance.

[0282] Optimization

[0283] Based on the predicted energy demand, the server generates an efficient energy usage plan using Pyomo and SciPy. For example, it proposes a schedule to limit peak consumption or suppress unnecessary consumption by adjusting the use of specific devices. This creates a custom plan suitable for the user's energy-saving goals in real time.

[0284] Display

[0285] The terminal provides the user with real-time power consumption data, predicted patterns, and optimized plans via a web-based dashboard. Applications using Flask and Django visualize this information, and the user can adjust energy usage based on this to reduce costs and achieve efficient energy management.

[0286] Specific Example

[0287] For example, consider the energy usage of a home during the daytime in summer. The server predicts the time of day when the air conditioner will be used most and creates a suggestion to raise the temperature to avoid that peak. The terminal immediately notifies the user of this suggestion, and by adjusting the air conditioner settings, the user can use electricity efficiently.

[0288] Example of a prompt

[0289] "Predict the peak electricity demand for a specific household tomorrow and generate specific methods to reduce the peak load."

[0290] In this way, the system of the present invention uses machine learning and data analysis to provide users with optimal energy use and support sustainable consumption.

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

[0292] Step 1:

[0293] The server acquires power consumption information in real time from smart meters and IoT sensors. It receives power data transmitted from these sensors as input and stores it in a database. External factors such as weather data and electricity market price information are also acquired and integrated into the database. Throughout this process, secure communication is maintained based on data collection protocols.

[0294] Step 2:

[0295] The server preprocesses the information stored in the database using data processing libraries such as Pandas and NumPy. The input for preprocessing is the stored raw data, and the output is clean data with missing values ​​and outliers removed. This clean data is then prepared for machine learning in the next step.

[0296] Step 3:

[0297] The server inputs clean data into a machine learning model using Scikit-learn or TensorFlow to learn consumption patterns. Here, the input is preprocessed energy consumption data, and the output is a prediction of future energy demand. This makes it possible to identify peak demand. Training the model involves regression analysis and predictive calculations based on historical data.

[0298] Step 4:

[0299] The server uses optimization algorithms built with Pyomo and SciPy based on the forecast results to generate an efficient energy use plan. It takes forecasted demand data as input and includes an adjusted energy schedule as output. Specifically, it adjusts the operating times of specific devices and develops plans that reduce peak power usage.

[0300] Step 5:

[0301] The terminal receives display data sent from the server. It receives energy consumption information, forecast data, and optimized plans as input, and visualizes this information for the user on a dashboard. The output is a visual information display, based on a web application using Flask or Django. Users can adjust their energy usage based on this information.

[0302] Step 6:

[0303] Users adjust their energy usage behavior based on information presented on the device. Input is the information provided by the device, and output is the user's manual or automated actions. This allows users to reduce electricity costs and improve energy efficiency.

[0304] (Application Example 1)

[0305] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0306] Improving the efficiency of energy consumption in physical stores directly contributes to reducing the environmental impact and operating costs. However, analyzing the energy usage that occurs within the store in real time and providing appropriate plans has not been sufficiently achieved by conventional technologies. Therefore, there is a need for new technologies that can improve the operational efficiency of stores through the automation and optimization of energy management.

[0307] 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.

[0308] In this invention, the server includes information acquisition means for acquiring information related to power usage, prediction means for performing time-series analysis based on the acquired information and implementing consumption prediction, and plan generation means for generating an effective energy usage plan based on the predicted consumption. As a result, it becomes possible to monitor the energy consumption status of physical stores in real time and automatically generate effective plans.

[0309] "Information related to power usage" refers to data indicating the amount and usage pattern of power consumed in homes, businesses, stores, etc.

[0310] "Information acquisition means" refers to a device or system that collects information related to power usage using smart meters and various sensors.

[0311] "Time-series analysis" is a method of analyzing the acquired data along the time order to clarify the tendency and pattern of consumption.

[0312] "Consumption prediction" is a process of estimating the future energy usage amount based on past data.

[0313] "Predicted consumption" refers to the expected future power usage amount calculated through time-series analysis.

[0314] An "effective energy use plan" is a feasible plan designed to maximize energy efficiency and reduce costs.

[0315] A "plan generation means" is a device or system that creates and proposes an optimized energy use plan based on predicted consumption.

[0316] A "physical store" refers to a physical location where commercial activities take place and where customers can visit in person.

[0317] A "prompt statement" is an instruction statement that is input into a generative AI model to generate information.

[0318] "Support measures" refer to devices or systems equipped with visual information provision or simulation functions to assist users in making decisions.

[0319] The system that realizes this invention supports the operation of physical stores by efficiently collecting and analyzing information related to electricity usage and generating an optimized energy usage plan.

[0320] The server collects real-time electricity usage data from within the store through an information acquisition system using smart meters and various IoT sensors. This data is then analyzed via a time-series analysis module to reveal consumption patterns and trends, and to predict future electricity usage.

[0321] Based on predicted data, the server's planning generation module utilizes machine learning algorithms with TensorFlow to create an efficient energy usage plan. This includes scheduling adjustments to avoid peak power consumption times within the store and measures to reduce unnecessary energy use.

[0322] The terminal visually presents energy consumption information and optimized plans to the user through a Plotly-based user interface. Based on this information, the user can make decisions regarding energy use in physical stores.

[0323] Furthermore, by utilizing a generative AI model that uses prompts, users can easily ask questions about energy management and receive appropriate advice. An example of a prompt is, "Create a Python script that predicts peak usage times based on energy consumption data from physical stores and proposes an optimal energy usage plan."

[0324] As a concrete example, a cafe in a bustling commercial district has achieved cost reductions by effectively managing electricity consumption during peak hours. In this way, the system can be customized to meet the needs of individual stores, supporting sustainable business operations.

[0325] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0326] Step 1:

[0327] The server collects electricity usage data in real time from smart meters and various IoT sensors. Electricity usage data is provided as input, and the server stores this data in a database. The output here is the electricity usage information stored in the database.

[0328] Step 2:

[0329] The server applies a time-series analysis algorithm to the collected power usage data. The input is the power usage information obtained in step 1, and based on this, it analyzes consumption patterns and predicts future power demand. The output is a graph of the predicted consumption pattern.

[0330] Step 3:

[0331] The server uses predicted consumption data to generate an efficient energy use plan using a machine learning algorithm powered by TensorFlow. The input is the predicted consumption data from step 2, and the program proposes an optimal energy use schedule. The output is the optimized energy use plan.

[0332] Step 4:

[0333] The terminal uses Plotly to visually present energy consumption information and the generated energy usage plan to the user. The input is the data obtained in steps 1, 2, and 3, which the terminal graphically displays on the dashboard. The output is visualized energy usage information that the user can review.

[0334] Step 5:

[0335] The user inputs prompts and questions to the system via a generative AI model. Based on this input, the server provides the user with appropriate energy optimization advice. The generated output consists of specific action plans and additional information that the user can implement.

[0336] 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.

[0337] This invention provides an energy usage plan that reflects the user's emotional state by integrating an emotion engine in addition to a power consumption management system. This system consists of data acquisition means, prediction means, optimization means, display means, and an emotion engine.

[0338] Emotional Engine

[0339] 1. Emotion recognition

[0340] The server extracts emotional data from the user's facial expressions and voice through camera and microphone sensors. This data is analyzed using a machine learning model to determine the user's current emotional state (e.g., stress, relaxation, joy).

[0341] 2. Use of emotional data

[0342] The server adjusts the energy usage plan generated by optimization means based on emotional data obtained from the emotion engine. For example, if the user is stressed, the system will suggest an energy usage plan that promotes relaxation.

[0343] Data acquisition and prediction

[0344] The server collects power consumption information from smart meters and weather data as an external factor. Using this database, machine learning algorithms are used to predict future power demand.

[0345] Optimization methods

[0346] The server generates an energy usage plan tailored to individual needs based on predictive and emotional data. For example, if a user is fatigued, it suggests a temperature setting that maintains comfort while avoiding peak hours.

[0347] Display means

[0348] The device presents the user with a dashboard showing their power consumption status, forecast results, and optimized plans. In addition, the user's emotional state is visually represented, allowing them to understand how the plan has been adjusted.

[0349] Specific example

[0350] For example, at night when a user feels tired, the server generates a plan to adjust the lighting brightness appropriately. The terminal notifies the user of this plan, and the user changes their environment accordingly. In this way, energy consumption is optimized while also caring for the user's emotional state.

[0351] The system of this invention utilizes emotional data for energy management, thereby realizing a more precise and personalized method of energy use.

[0352] The following describes the processing flow.

[0353] Step 1:

[0354] The server acquires real-time power consumption data from smart meters and sensors. Simultaneously, it collects weather data and market electricity prices using external APIs and stores this information in a database.

[0355] Step 2:

[0356] The server uses collected power consumption data and external factor data to perform time-series data analysis using machine learning algorithms. This allows it to predict future power demand and identify peak demand times.

[0357] Step 3:

[0358] The server acquires the user's facial expressions and voice data through the camera and microphone. This data is analyzed by an emotion engine to evaluate the user's emotional state in real time. This evaluation information is managed within the system along with other data.

[0359] Step 4:

[0360] The server generates an energy usage plan using optimization methods, taking into account predicted power demand and the user's emotional state. Based on the emotional data, it suggests adjusting the temperature and lighting to provide a relaxing environment for the user.

[0361] Step 5:

[0362] The device presents the generated energy usage plan to the user via a dashboard interface. The dashboard visually displays details of the current power consumption, demand forecast, and an optimized plan tailored to the user's emotional state.

[0363] Step 6:

[0364] Users can review the energy usage plan presented through their device and manually adjust settings as needed. They can also utilize the simulation function to compare different scenarios and adopt the optimal energy management approach.

[0365] Step 7:

[0366] The server collects and records feedback derived from user behavior and emotions. This data is used to retrain predictive models and the emotion engine, helping to continuously improve the overall system accuracy and user experience.

[0367] (Example 2)

[0368] 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".

[0369] The challenge lies in achieving efficient energy management while simultaneously proposing individualized energy usage plans that are sensitive to the emotional state of users. Conventional energy management systems provide plans based on electricity consumption and demand forecasts, but they do not take into account the emotional needs of users, thus improving the user experience is essential.

[0370] 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.

[0371] In this invention, the server includes information acquisition means, demand forecasting means, emotion recognition means, optimization means, and display means. This makes it possible to propose an effective and emotionally sensitive energy usage plan based on the user's power consumption patterns and emotional state.

[0372] "Information acquisition means" refers to a device or function that collects power consumption information and external environmental information.

[0373] A "demand forecasting tool" is a device or function that performs time-series data analysis on acquired information to predict future electricity demand.

[0374] "Emotion recognition means" refers to a device or function that analyzes a user's voice and video to determine their emotional state.

[0375] "Optimization means" refers to a device or function that generates an efficient and personalized energy usage plan based on predicted demand and the user's emotional state.

[0376] "Display means" refers to a device or function for visually presenting to the user information such as power consumption, optimization plans, and emotional state.

[0377] This invention is a system that integrates an emotion engine into power consumption management and provides an energy usage plan tailored to the user's emotional state. This system consists of multiple components such as a server, terminals, and sensors.

[0378] The server first uses information acquisition tools to collect power consumption information from smart meters and environmental data from external data sources. The server also monitors the user's facial expressions and voice through sensors such as cameras and microphones, and analyzes emotional data using emotion recognition tools. Machine learning models are used to determine the user's emotional state.

[0379] The server uses demand forecasting tools to predict future electricity demand from acquired power consumption information and environmental data. This process employs algorithms such as time series analysis. Subsequently, optimization tools are used to generate an energy usage plan tailored to the individual user, based on the predicted demand and emotional state. This plan takes into account factors such as reducing peak load and improving comfort.

[0380] The device displays an optimized energy usage plan to the user in a dashboard format. The display visually presents power consumption, emotional state, and plan adjustments. Based on this information, the user can adjust their environment to improve both energy efficiency and comfort simultaneously.

[0381] For example, if a user is fatigued, the server generates an energy usage plan that adjusts the room lighting to a warmer color and the music volume, and notifies the user of this suggestion via their device. In this way, an integrated plan for energy and emotional management is provided. This system is expected to improve the efficiency of energy management and enhance the user experience.

[0382] A concrete example of a prompt for a generative AI model might be, "Please suggest an energy usage plan that takes into account the user's current emotional state." This prompt allows the system to generate a plan that considers the user's emotional state.

[0383] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0384] Step 1:

[0385] The server collects power consumption information from smart meters and environmental data from external data sources using information acquisition methods. The inputs are power consumption information and environmental data, which are used directly in the next processing step. Specifically, it periodically acquires data from sensors and stores it in a database.

[0386] Step 2:

[0387] The server collects the user's facial expressions and voice through the camera and microphone, and analyzes the emotional data using emotion recognition technology. The input is the user's facial expressions and voice information, and the output is the emotional state (e.g., stress, relaxation, joy). Specifically, the acquired data is input into a machine learning model to determine the emotional state.

[0388] Step 3:

[0389] The server uses demand forecasting tools to perform time-series analysis based on the power consumption information and environmental data collected in Step 1 to predict future power demand. The input is power consumption information and environmental data, and the output is the predicted power demand. Specifically, it uses machine learning algorithms to analyze demand trends.

[0390] Step 4:

[0391] The server uses optimization techniques to generate an energy usage plan based on the emotional state in step 2 and the power demand forecast in step 3. The inputs are the emotional state and the power demand forecast, and the output is the optimized energy usage plan. Specifically, it creates a plan to avoid predicted peak times and adds settings that take the emotional state into consideration.

[0392] Step 5:

[0393] The terminal displays a dashboard-style presentation of an energy usage plan, power consumption status, and emotional state optimized for the user. Inputs are the generated energy usage plan and analyzed data, while output is a visual display on the dashboard. Specifically, the latest plan information is placed on the GUI for easy user access.

[0394] (Application Example 2)

[0395] 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."

[0396] Modern autonomous vehicles, a vital mode of transportation, require improved passenger comfort and safety. However, conventional systems struggle to instantly adjust the in-car environment to reflect the passenger's emotional state, resulting in an inability to effectively reduce passenger stress and discomfort. To address this, it is necessary to rapidly optimize the in-car environment in response to changes in the passenger's emotions and efficiently manage energy consumption.

[0397] 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.

[0398] In this invention, the server includes data acquisition means for obtaining power consumption information, emotion recognition means for recognizing the emotional state of the occupants, and environment adjustment means for adjusting the in-vehicle environment based on the recognized emotional state. This makes it possible to optimize energy use while responding quickly to the emotional state of the occupants.

[0399] "Power consumption information" refers to data that shows how electricity is being used, including consumption per hour and power consumption for specific devices.

[0400] "Data acquisition means" refers to a device or method for collecting necessary data such as power consumption information and external factor information.

[0401] "Demand forecasting" is the process of predicting future electricity consumption based on acquired data, and includes time-series data analysis.

[0402] "Optimization means" refers to techniques or methods for designing efficient energy use plans in response to predicted demand.

[0403] "Display means" refers to an interface or display device for visually presenting information or plans to a user.

[0404] "Emotion recognition means" refers to technology or devices that analyze the facial expressions and voices of crew members to determine their emotional state at a given time.

[0405] "Environmental adjustment means" refers to a device or method for appropriately changing the in-vehicle environment based on recognized emotional states.

[0406] This invention provides a system primarily for autonomous vehicles. The system aims to improve occupant safety and comfort, as well as to optimize energy consumption. Its main components, such as servers, terminals, and users, function in a collaborative manner.

[0407] The server collects power consumption information using data acquisition methods. Based on this, it predicts future power demand based on time-series data analysis and generates an efficient energy use plan through optimization methods. It also uses emotion recognition methods such as cameras and microphones to recognize the emotional state of the occupants. This emotion data is used as an environmental adjustment method to adjust the vehicle's air conditioning, lighting, and other settings.

[0408] The device uses a display to present users with power consumption information and optimized plans tailored to their emotional state in a dashboard format. This allows users to visually understand how their plans have been adjusted.

[0409] For example, if passengers experience stress during a long drive, the server can adjust the lighting in real time and play relaxing music to improve passenger comfort. The software used includes OpenCV and an Emotion Recognition model, which enables rapid recognition and analysis of emotions, allowing for appropriate environmental adjustments.

[0410] An example of a prompt message would be: "In autonomous vehicles, we propose a method to understand how occupants are feeling in real time and optimize the in-vehicle environment according to their emotions."

[0411] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0412] Step 1:

[0413] The server collects vehicle power consumption information through data acquisition methods. Inputs include power usage data from devices within the vehicle and external weather information. This data is converted into a format suitable for time-series data analysis, preparing it for analysis.

[0414] Step 2:

[0415] The server performs time-series data analysis based on the acquired power consumption information and conducts demand forecasting. The input is the data collected in step 1, and the output is forecast data showing future power demand. This forecast result is obtained using a machine learning algorithm.

[0416] Step 3:

[0417] The server uses emotion recognition technology to determine the emotional state of the occupants. Real-time video and audio data from in-vehicle cameras and microphones are used as input and analyzed by an Emotion Recognition model. The output is a classification result based on the occupants' emotional state (e.g., relaxed, stressed).

[0418] Step 4:

[0419] The server generates an efficient energy use plan using optimization methods based on predicted demand data and emotional state. Inputs include demand forecast data and emotional state data, and the output is the adjusted energy use plan. Specific actions such as adjusting air conditioning temperature and lighting are proposed.

[0420] Step 5:

[0421] The terminal presents the user with power consumption information and the generated energy usage plan via a display device. This information is visualized on a dashboard. The input is the plan generated in step 4, and the output is the information visually presented to the user.

[0422] Step 6:

[0423] The user can approve or modify the adjustments suggested by the system based on the information provided. If adjustments are made, the information is sent back to the server, and the plan is updated as necessary.

[0424] 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.

[0425] 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.

[0426] 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.

[0427] [Third Embodiment]

[0428] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0429] 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.

[0430] 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).

[0431] 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.

[0432] 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.

[0433] 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).

[0434] 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.

[0435] 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.

[0436] 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.

[0437] 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.

[0438] 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.

[0439] 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".

[0440] The energy management system of the present invention aims to improve the efficiency of power consumption by integrating data acquisition means, prediction means, optimization means, and display means. The specific operation of each component is described below.

[0441] Data acquisition means

[0442] The server collects real-time power consumption information from smart meters and IoT sensors installed in homes and businesses. In addition, it acquires external factor information such as weather data and electricity market price information and stores it in a database. This allows various factors that affect energy use to be taken into consideration.

[0443] Prediction methods

[0444] The server learns consumption patterns using machine learning algorithms based on the acquired data. Based on past data, it predicts future energy demand and identifies peak times and consumption levels. This enables accurate prediction of energy consumption.

[0445] Optimization methods

[0446] The server uses the forecast results to automatically generate an efficient energy usage plan. For example, it suggests scheduling adjustments to reduce peak power consumption and proposes ways to reduce unnecessary energy use. This plan is customized to the user's energy-saving goals.

[0447] Display means

[0448] The device provides users with a dashboard that visualizes real-time energy consumption, predicted consumption patterns, and optimization plans. Based on this information, users can adjust their energy usage as needed to reduce costs.

[0449] Specific example

[0450] As a concrete example of its application, consider household energy management during the daytime in summer. The server monitors power consumption in real time and predicts when air conditioner usage will reach its peak. To avoid the peak, the server suggests raising the air conditioner's temperature setting or using other low-power modes. The terminal immediately notifies the user of this suggestion, and the user can adjust accordingly, improving the efficiency of power usage.

[0451] In this way, the system of the present invention effectively manages users' energy use through real-time data analysis and AI prediction, thereby supporting sustainable energy use.

[0452] The following describes the processing flow.

[0453] Step 1:

[0454] The server collects real-time power consumption data from smart meters and IoT sensors. It also obtains weather information and market electricity price data via external APIs. This data is stored in a database and used for subsequent analysis.

[0455] Step 2:

[0456] The server preprocesses the collected data. This includes filtering outliers and imputing missing data. Then, it prepares the data for learning past consumption patterns using time series analysis.

[0457] Step 3:

[0458] The server uses the organized data to run machine learning algorithms and predict future electricity demand. This predictive model is used to identify peak demand and promote efficient energy use.

[0459] Step 4:

[0460] The server uses optimization techniques based on predicted data to generate an efficient energy usage plan. This includes suggestions such as peak shifting and power usage limits.

[0461] Step 5:

[0462] The device displays real-time energy consumption, forecasts, and optimized plans to the user on a dashboard. This allows the user to see the current situation and recommended actions at a glance.

[0463] Step 6:

[0464] Users can compare different energy usage scenarios using the simulation function provided by the device. This allows users to select the most effective energy management method and make decisions about its implementation.

[0465] Step 7:

[0466] The server collects the results of user actions as data again and uses this feedback to retrain the model. This improves the accuracy of future predictions and enables further energy efficiency.

[0467] (Example 1)

[0468] 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."

[0469] In modern society, the efficient use of energy resources is a crucial issue. Accurately predicting actual energy demand and optimizing energy use accordingly contributes to the realization of a sustainable society. However, traditional energy management systems struggle to respond to real-time demand fluctuations and lack concrete means to predict and mitigate peak demand. Against this backdrop, a system is needed to achieve more accurate and dynamic energy management.

[0470] 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.

[0471] In this invention, the server includes information gathering means for acquiring power consumption information, forecasting means for performing numerical analysis based on the acquired power consumption information and conducting demand forecasting, and optimization means for generating an efficient energy use plan based on the forecasted demand. This makes it possible to learn consumption patterns, identify peak demand, and dynamically adjust the energy use plan in real time based on that demand.

[0472] An "information gathering tool" is a mechanism that acquires data on energy consumption in households and businesses in real time and stores it in a database.

[0473] "Numerical analysis" is a technology that uses machine learning algorithms to analyze consumption patterns based on collected electricity consumption data and predict future energy demand.

[0474] A "predictive method" is a technique for accurately predicting future energy demand based on past consumption data.

[0475] "Optimization methods" refer to the process of formulating an energy use optimization plan based on predicted energy demand and proposing a specific schedule to reduce peak consumption.

[0476] A "display means" is an interface that visually displays to the user real-time power consumption status, predicted demand, and an optimized energy plan.

[0477] "Learning consumption patterns" is the process of using machine learning to analyze past energy usage history and identify future usage trends.

[0478] "Real-time dynamic adjustment" refers to the ability to instantly update energy use plans when energy demand forecasts or optimization plans change, and to continuously implement optimized energy use.

[0479] The energy management system in this invention aims to improve the efficiency of power consumption by integrating information gathering, prediction, optimization, and display. The specific operation of each component is described below.

[0480] Information gathering

[0481] The server acquires real-time power consumption information from smart meters and IoT sensors installed in homes and businesses. This information is securely transmitted to the server via a data collection protocol and stored in a database. Furthermore, the server acquires weather information and market price information from external data sources, comprehensively capturing various factors that affect energy consumption.

[0482] prediction

[0483] The server preprocesses the information in the database using Python's Pandas and NumPy libraries and applies it to machine learning algorithms. Models using Scikit-learn and TensorFlow learn consumption patterns and predict future energy demand based on historical data. This predictive model makes it possible to identify peak demand in advance.

[0484] optimization

[0485] The server generates efficient energy usage plans using Pyomo and SciPy based on predicted energy demand. For example, it suggests schedules to limit peak consumption or adjusts the use of specific devices to reduce unnecessary consumption. This creates a customized plan in real time that is tailored to the user's energy-saving goals.

[0486] display

[0487] The device provides users with real-time power consumption data, predicted patterns, and optimized plans via a web-based dashboard. Applications using Flask or Django visualize this information, allowing users to adjust their energy usage based on this information to reduce costs and achieve efficient energy management.

[0488] Specific example

[0489] For example, consider the energy usage of a home during the daytime in summer. The server predicts the time of day when the air conditioner will be used most and creates a suggestion to raise the temperature to avoid that peak. The terminal immediately notifies the user of this suggestion, and by adjusting the air conditioner settings, the user can use electricity efficiently.

[0490] Example of a prompt

[0491] "Predict the peak electricity demand for a specific household tomorrow and generate specific methods to reduce the peak load."

[0492] In this way, the system of the present invention uses machine learning and data analysis to provide users with optimal energy use and support sustainable consumption.

[0493] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0494] Step 1:

[0495] The server acquires power consumption information in real time from smart meters and IoT sensors. It receives power data transmitted from these sensors as input and stores it in a database. External factors such as weather data and electricity market price information are also acquired and integrated into the database. Throughout this process, secure communication is maintained based on data collection protocols.

[0496] Step 2:

[0497] The server preprocesses the information stored in the database using data processing libraries such as Pandas and NumPy. The input for preprocessing is the stored raw data, and the output is clean data with missing values ​​and outliers removed. This clean data is then prepared for machine learning in the next step.

[0498] Step 3:

[0499] The server inputs clean data into a machine learning model using Scikit-learn or TensorFlow to learn consumption patterns. Here, the input is preprocessed energy consumption data, and the output is a prediction of future energy demand. This makes it possible to identify peak demand. Training the model involves regression analysis and predictive calculations based on historical data.

[0500] Step 4:

[0501] The server uses optimization algorithms built with Pyomo and SciPy based on the forecast results to generate an efficient energy use plan. It takes forecasted demand data as input and includes an adjusted energy schedule as output. Specifically, it adjusts the operating times of specific devices and develops plans that reduce peak power usage.

[0502] Step 5:

[0503] The terminal receives display data sent from the server. It receives energy consumption information, forecast data, and optimized plans as input, and visualizes this information for the user on a dashboard. The output is a visual information display, based on a web application using Flask or Django. Users can adjust their energy usage based on this information.

[0504] Step 6:

[0505] Users adjust their energy usage behavior based on information presented on the device. Input is the information provided by the device, and output is the user's manual or automated actions. This allows users to reduce electricity costs and improve energy efficiency.

[0506] (Application Example 1)

[0507] 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."

[0508] Improving energy efficiency in physical stores directly leads to reduced environmental impact and lower operating costs. However, conventional technologies have not adequately analyzed energy usage within stores in real time and provided appropriate plans. Therefore, there is a need for new technologies that can improve store operational efficiency through the automation and optimization of energy management.

[0509] 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.

[0510] In this invention, the server includes information acquisition means for obtaining information related to power usage, prediction means for performing time-series analysis based on the acquired information and conducting consumption forecasts, and plan generation means for generating an effective energy usage plan based on the predicted consumption. This makes it possible to monitor the energy consumption status of physical stores in real time and automatically generate an effective plan.

[0511] "Information related to electricity usage" refers to data that shows the amount and patterns of electricity consumed in homes, businesses, stores, etc.

[0512] "Information acquisition means" refers to a device or system that collects information related to electricity usage using smart meters or various sensors.

[0513] Time series analysis is a method that analyzes acquired data in chronological order to reveal consumption trends and patterns.

[0514] "Consumption forecasting" is the process of estimating future energy consumption based on past data.

[0515] "Predicted consumption" refers to the projected future electricity usage calculated through time-series analysis.

[0516] An "effective energy use plan" is a feasible plan designed to maximize energy efficiency and reduce costs.

[0517] A "plan generation means" is a device or system that creates and proposes an optimized energy use plan based on predicted consumption.

[0518] A "physical store" refers to a physical location where commercial activities take place and where customers can visit in person.

[0519] A "prompt statement" is an instruction statement that is input into a generative AI model to generate information.

[0520] "Support measures" refer to devices or systems equipped with visual information provision or simulation functions to assist users in making decisions.

[0521] The system that realizes this invention supports the operation of physical stores by efficiently collecting and analyzing information related to electricity usage and generating an optimized energy usage plan.

[0522] The server collects real-time electricity usage data from within the store through an information acquisition system using smart meters and various IoT sensors. This data is then analyzed via a time-series analysis module to reveal consumption patterns and trends, and to predict future electricity usage.

[0523] Based on predicted data, the server's planning generation module utilizes machine learning algorithms with TensorFlow to create an efficient energy usage plan. This includes scheduling adjustments to avoid peak power consumption times within the store and measures to reduce unnecessary energy use.

[0524] The terminal visually presents energy consumption information and optimized plans to the user through a Plotly-based user interface. Based on this information, the user can make decisions regarding energy use in physical stores.

[0525] Furthermore, by utilizing a generative AI model that uses prompts, users can easily ask questions about energy management and receive appropriate advice. An example of a prompt is, "Create a Python script that predicts peak usage times based on energy consumption data from physical stores and proposes an optimal energy usage plan."

[0526] As a concrete example, a cafe in a bustling commercial district has achieved cost reductions by effectively managing electricity consumption during peak hours. In this way, the system can be customized to meet the needs of individual stores, supporting sustainable business operations.

[0527] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0528] Step 1:

[0529] The server collects electricity usage data in real time from smart meters and various IoT sensors. Electricity usage data is provided as input, and the server stores this data in a database. The output here is the electricity usage information stored in the database.

[0530] Step 2:

[0531] The server applies a time-series analysis algorithm to the collected power usage data. The input is the power usage information obtained in step 1, and based on this, it analyzes consumption patterns and predicts future power demand. The output is a graph of the predicted consumption pattern.

[0532] Step 3:

[0533] The server uses predicted consumption data to generate an efficient energy use plan using a machine learning algorithm powered by TensorFlow. The input is the predicted consumption data from step 2, and the program proposes an optimal energy use schedule. The output is the optimized energy use plan.

[0534] Step 4:

[0535] The terminal uses Plotly to visually present energy consumption information and the generated energy usage plan to the user. The input is the data obtained in steps 1, 2, and 3, which the terminal graphically displays on the dashboard. The output is visualized energy usage information that the user can review.

[0536] Step 5:

[0537] The user inputs prompts and questions to the system via a generative AI model. Based on this input, the server provides the user with appropriate energy optimization advice. The generated output consists of specific action plans and additional information that the user can implement.

[0538] 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.

[0539] This invention provides an energy usage plan that reflects the user's emotional state by integrating an emotion engine in addition to a power consumption management system. This system consists of data acquisition means, prediction means, optimization means, display means, and an emotion engine.

[0540] Emotional Engine

[0541] 1. Emotion recognition

[0542] The server extracts emotional data from the user's facial expressions and voice through camera and microphone sensors. This data is analyzed using a machine learning model to determine the user's current emotional state (e.g., stress, relaxation, joy).

[0543] 2. Use of emotional data

[0544] The server adjusts the energy usage plan generated by optimization means based on emotional data obtained from the emotion engine. For example, if the user is stressed, the system will suggest an energy usage plan that promotes relaxation.

[0545] Data acquisition and prediction

[0546] The server collects power consumption information from smart meters and weather data as an external factor. Using this database, machine learning algorithms are used to predict future power demand.

[0547] Optimization methods

[0548] The server generates an energy usage plan tailored to individual needs based on predictive and emotional data. For example, if a user is fatigued, it suggests a temperature setting that maintains comfort while avoiding peak hours.

[0549] Display means

[0550] The device presents the user with a dashboard showing their power consumption status, forecast results, and optimized plans. In addition, the user's emotional state is visually represented, allowing them to understand how the plan has been adjusted.

[0551] Specific example

[0552] For example, at night when a user feels tired, the server generates a plan to adjust the lighting brightness appropriately. The terminal notifies the user of this plan, and the user changes their environment accordingly. In this way, energy consumption is optimized while also caring for the user's emotional state.

[0553] The system of this invention utilizes emotional data for energy management, thereby realizing a more precise and personalized method of energy use.

[0554] The following describes the processing flow.

[0555] Step 1:

[0556] The server acquires real-time power consumption data from smart meters and sensors. Simultaneously, it collects weather data and market electricity prices using external APIs and stores this information in a database.

[0557] Step 2:

[0558] The server uses collected power consumption data and external factor data to perform time-series data analysis using machine learning algorithms. This allows it to predict future power demand and identify peak demand times.

[0559] Step 3:

[0560] The server acquires the user's facial expressions and voice data through the camera and microphone. This data is analyzed by an emotion engine to evaluate the user's emotional state in real time. This evaluation information is managed within the system along with other data.

[0561] Step 4:

[0562] The server generates an energy usage plan using optimization methods, taking into account predicted power demand and the user's emotional state. Based on the emotional data, it suggests adjusting the temperature and lighting to provide a relaxing environment for the user.

[0563] Step 5:

[0564] The device presents the generated energy usage plan to the user via a dashboard interface. The dashboard visually displays details of the current power consumption, demand forecast, and an optimized plan tailored to the user's emotional state.

[0565] Step 6:

[0566] Users can review the energy usage plan presented through their device and manually adjust settings as needed. They can also utilize the simulation function to compare different scenarios and adopt the optimal energy management approach.

[0567] Step 7:

[0568] The server collects and records feedback derived from user behavior and emotions. This data is used to retrain predictive models and the emotion engine, helping to continuously improve the overall system accuracy and user experience.

[0569] (Example 2)

[0570] 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."

[0571] The challenge lies in achieving efficient energy management while simultaneously proposing individualized energy usage plans that are sensitive to the emotional state of users. Conventional energy management systems provide plans based on electricity consumption and demand forecasts, but they do not take into account the emotional needs of users, thus improving the user experience is essential.

[0572] 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.

[0573] In this invention, the server includes information acquisition means, demand forecasting means, emotion recognition means, optimization means, and display means. This makes it possible to propose an effective and emotionally sensitive energy usage plan based on the user's power consumption patterns and emotional state.

[0574] "Information acquisition means" refers to a device or function that collects power consumption information and external environmental information.

[0575] A "demand forecasting tool" is a device or function that performs time-series data analysis on acquired information to predict future electricity demand.

[0576] "Emotion recognition means" refers to a device or function that analyzes a user's voice and video to determine their emotional state.

[0577] "Optimization means" refers to a device or function that generates an efficient and personalized energy usage plan based on predicted demand and the user's emotional state.

[0578] "Display means" refers to a device or function for visually presenting to the user information such as power consumption, optimization plans, and emotional state.

[0579] This invention is a system that integrates an emotion engine into power consumption management and provides an energy usage plan tailored to the user's emotional state. This system consists of multiple components such as a server, terminals, and sensors.

[0580] The server first uses information acquisition tools to collect power consumption information from smart meters and environmental data from external data sources. The server also monitors the user's facial expressions and voice through sensors such as cameras and microphones, and analyzes emotional data using emotion recognition tools. Machine learning models are used to determine the user's emotional state.

[0581] The server uses demand forecasting tools to predict future electricity demand from acquired power consumption information and environmental data. This process employs algorithms such as time series analysis. Subsequently, optimization tools are used to generate an energy usage plan tailored to the individual user, based on the predicted demand and emotional state. This plan takes into account factors such as reducing peak load and improving comfort.

[0582] The device displays an optimized energy usage plan to the user in a dashboard format. The display visually presents power consumption, emotional state, and plan adjustments. Based on this information, the user can adjust their environment to improve both energy efficiency and comfort simultaneously.

[0583] For example, if a user is fatigued, the server generates an energy usage plan that adjusts the room lighting to a warmer color and the music volume, and notifies the user of this suggestion via their device. In this way, an integrated plan for energy and emotional management is provided. This system is expected to improve the efficiency of energy management and enhance the user experience.

[0584] A concrete example of a prompt for a generative AI model might be, "Please suggest an energy usage plan that takes into account the user's current emotional state." This prompt allows the system to generate a plan that considers the user's emotional state.

[0585] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0586] Step 1:

[0587] The server collects power consumption information from smart meters and environmental data from external data sources using information acquisition methods. The inputs are power consumption information and environmental data, which are used directly in the next processing step. Specifically, it periodically acquires data from sensors and stores it in a database.

[0588] Step 2:

[0589] The server collects the user's facial expressions and voice through the camera and microphone, and analyzes the emotional data using emotion recognition technology. The input is the user's facial expressions and voice information, and the output is the emotional state (e.g., stress, relaxation, joy). Specifically, the acquired data is input into a machine learning model to determine the emotional state.

[0590] Step 3:

[0591] The server uses demand forecasting tools to perform time-series analysis based on the power consumption information and environmental data collected in Step 1 to predict future power demand. The input is power consumption information and environmental data, and the output is the predicted power demand. Specifically, it uses machine learning algorithms to analyze demand trends.

[0592] Step 4:

[0593] The server uses optimization techniques to generate an energy usage plan based on the emotional state in step 2 and the power demand forecast in step 3. The inputs are the emotional state and the power demand forecast, and the output is the optimized energy usage plan. Specifically, it creates a plan to avoid predicted peak times and adds settings that take the emotional state into consideration.

[0594] Step 5:

[0595] The terminal displays a dashboard-style presentation of an energy usage plan, power consumption status, and emotional state optimized for the user. Inputs are the generated energy usage plan and analyzed data, while output is a visual display on the dashboard. Specifically, the latest plan information is placed on the GUI for easy user access.

[0596] (Application Example 2)

[0597] 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."

[0598] Modern autonomous vehicles, a vital mode of transportation, require improved passenger comfort and safety. However, conventional systems struggle to instantly adjust the in-car environment to reflect the passenger's emotional state, resulting in an inability to effectively reduce passenger stress and discomfort. To address this, it is necessary to rapidly optimize the in-car environment in response to changes in the passenger's emotions and efficiently manage energy consumption.

[0599] 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.

[0600] In this invention, the server includes data acquisition means for obtaining power consumption information, emotion recognition means for recognizing the emotional state of the occupants, and environment adjustment means for adjusting the in-vehicle environment based on the recognized emotional state. This makes it possible to optimize energy use while responding quickly to the emotional state of the occupants.

[0601] "Power consumption information" refers to data that shows how electricity is being used, including consumption per hour and power consumption for specific devices.

[0602] "Data acquisition means" refers to a device or method for collecting necessary data such as power consumption information and external factor information.

[0603] "Demand forecasting" is the process of predicting future electricity consumption based on acquired data, and includes time-series data analysis.

[0604] "Optimization means" refers to techniques or methods for designing efficient energy use plans in response to predicted demand.

[0605] "Display means" refers to an interface or display device for visually presenting information or plans to a user.

[0606] "Emotion recognition means" refers to technology or devices that analyze the facial expressions and voices of crew members to determine their emotional state at a given time.

[0607] "Environmental adjustment means" refers to a device or method for appropriately changing the in-vehicle environment based on recognized emotional states.

[0608] This invention provides a system primarily for autonomous vehicles. The system aims to improve occupant safety and comfort, as well as to optimize energy consumption. Its main components, such as servers, terminals, and users, function in a collaborative manner.

[0609] The server collects power consumption information using data acquisition methods. Based on this, it predicts future power demand based on time-series data analysis and generates an efficient energy use plan through optimization methods. It also uses emotion recognition methods such as cameras and microphones to recognize the emotional state of the occupants. This emotion data is used as an environmental adjustment method to adjust the vehicle's air conditioning, lighting, and other settings.

[0610] The device uses a display to present users with power consumption information and optimized plans tailored to their emotional state in a dashboard format. This allows users to visually understand how their plans have been adjusted.

[0611] For example, if passengers experience stress during a long drive, the server can adjust the lighting in real time and play relaxing music to improve passenger comfort. The software used includes OpenCV and an Emotion Recognition model, which enables rapid recognition and analysis of emotions, allowing for appropriate environmental adjustments.

[0612] An example of a prompt message would be: "In autonomous vehicles, we propose a method to understand how occupants are feeling in real time and optimize the in-vehicle environment according to their emotions."

[0613] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0614] Step 1:

[0615] The server collects vehicle power consumption information through data acquisition methods. Inputs include power usage data from devices within the vehicle and external weather information. This data is converted into a format suitable for time-series data analysis, preparing it for analysis.

[0616] Step 2:

[0617] The server performs time-series data analysis based on the acquired power consumption information and conducts demand forecasting. The input is the data collected in step 1, and the output is forecast data showing future power demand. This forecast result is obtained using a machine learning algorithm.

[0618] Step 3:

[0619] The server uses emotion recognition technology to determine the emotional state of the occupants. Real-time video and audio data from in-vehicle cameras and microphones are used as input and analyzed by an Emotion Recognition model. The output is a classification result based on the occupants' emotional state (e.g., relaxed, stressed).

[0620] Step 4:

[0621] The server generates an efficient energy use plan using optimization methods based on predicted demand data and emotional state. Inputs include demand forecast data and emotional state data, and the output is the adjusted energy use plan. Specific actions such as adjusting air conditioning temperature and lighting are proposed.

[0622] Step 5:

[0623] The terminal presents the user with power consumption information and the generated energy usage plan via a display device. This information is visualized on a dashboard. The input is the plan generated in step 4, and the output is the information visually presented to the user.

[0624] Step 6:

[0625] The user can approve or modify the adjustments suggested by the system based on the information provided. If adjustments are made, the information is sent back to the server, and the plan is updated as necessary.

[0626] 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.

[0627] 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.

[0628] 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.

[0629] [Fourth Embodiment]

[0630] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0631] 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.

[0632] 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).

[0633] 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.

[0634] 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.

[0635] 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).

[0636] 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.

[0637] 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.

[0638] 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.

[0639] 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.

[0640] 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.

[0641] 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.

[0642] 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".

[0643] The energy management system of the present invention aims to improve the efficiency of power consumption by integrating data acquisition means, prediction means, optimization means, and display means. The specific operation of each component is described below.

[0644] Data acquisition means

[0645] The server collects real-time power consumption information from smart meters and IoT sensors installed in homes and businesses. In addition, it acquires external factor information such as weather data and electricity market price information and stores it in a database. This allows various factors that affect energy use to be taken into consideration.

[0646] Prediction methods

[0647] The server learns consumption patterns using machine learning algorithms based on the acquired data. Based on past data, it predicts future energy demand and identifies peak times and consumption levels. This enables accurate prediction of energy consumption.

[0648] Optimization methods

[0649] The server uses the forecast results to automatically generate an efficient energy usage plan. For example, it suggests scheduling adjustments to reduce peak power consumption and proposes ways to reduce unnecessary energy use. This plan is customized to the user's energy-saving goals.

[0650] Display means

[0651] The device provides users with a dashboard that visualizes real-time energy consumption, predicted consumption patterns, and optimization plans. Based on this information, users can adjust their energy usage as needed to reduce costs.

[0652] Specific example

[0653] As a concrete example of its application, consider household energy management during the daytime in summer. The server monitors power consumption in real time and predicts when air conditioner usage will reach its peak. To avoid the peak, the server suggests raising the air conditioner's temperature setting or using other low-power modes. The terminal immediately notifies the user of this suggestion, and the user can adjust accordingly, improving the efficiency of power usage.

[0654] In this way, the system of the present invention effectively manages users' energy use through real-time data analysis and AI prediction, thereby supporting sustainable energy use.

[0655] The following describes the processing flow.

[0656] Step 1:

[0657] The server collects real-time power consumption data from smart meters and IoT sensors. It also obtains weather information and market electricity price data via external APIs. This data is stored in a database and used for subsequent analysis.

[0658] Step 2:

[0659] The server preprocesses the collected data. This includes filtering outliers and imputing missing data. Then, it prepares the data for learning past consumption patterns using time series analysis.

[0660] Step 3:

[0661] The server uses the organized data to run machine learning algorithms and predict future electricity demand. This predictive model is used to identify peak demand and promote efficient energy use.

[0662] Step 4:

[0663] The server uses optimization techniques based on predicted data to generate an efficient energy usage plan. This includes suggestions such as peak shifting and power usage limits.

[0664] Step 5:

[0665] The device displays real-time energy consumption, forecasts, and optimized plans to the user on a dashboard. This allows the user to see the current situation and recommended actions at a glance.

[0666] Step 6:

[0667] Users can compare different energy usage scenarios using the simulation function provided by the device. This allows users to select the most effective energy management method and make decisions about its implementation.

[0668] Step 7:

[0669] The server collects the results of user actions as data again and uses this feedback to retrain the model. This improves the accuracy of future predictions and enables further energy efficiency.

[0670] (Example 1)

[0671] 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".

[0672] In modern society, the efficient use of energy resources is a crucial issue. Accurately predicting actual energy demand and optimizing energy use accordingly contributes to the realization of a sustainable society. However, traditional energy management systems struggle to respond to real-time demand fluctuations and lack concrete means to predict and mitigate peak demand. Against this backdrop, a system is needed to achieve more accurate and dynamic energy management.

[0673] 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.

[0674] In this invention, the server includes information gathering means for acquiring power consumption information, forecasting means for performing numerical analysis based on the acquired power consumption information and conducting demand forecasting, and optimization means for generating an efficient energy use plan based on the forecasted demand. This makes it possible to learn consumption patterns, identify peak demand, and dynamically adjust the energy use plan in real time based on that demand.

[0675] An "information gathering tool" is a mechanism that acquires data on energy consumption in households and businesses in real time and stores it in a database.

[0676] "Numerical analysis" is a technology that uses machine learning algorithms to analyze consumption patterns based on collected electricity consumption data and predict future energy demand.

[0677] A "predictive method" is a technique for accurately predicting future energy demand based on past consumption data.

[0678] "Optimization methods" refer to the process of formulating an energy use optimization plan based on predicted energy demand and proposing a specific schedule to reduce peak consumption.

[0679] A "display means" is an interface that visually displays to the user real-time power consumption status, predicted demand, and an optimized energy plan.

[0680] "Learning consumption patterns" is the process of using machine learning to analyze past energy usage history and identify future usage trends.

[0681] "Real-time dynamic adjustment" refers to the ability to instantly update energy use plans when energy demand forecasts or optimization plans change, and to continuously implement optimized energy use.

[0682] The energy management system in this invention aims to improve the efficiency of power consumption by integrating information gathering, prediction, optimization, and display. The specific operation of each component is described below.

[0683] Information gathering

[0684] The server acquires real-time power consumption information from smart meters and IoT sensors installed in homes and businesses. This information is securely transmitted to the server via a data collection protocol and stored in a database. Furthermore, the server acquires weather information and market price information from external data sources, comprehensively capturing various factors that affect energy consumption.

[0685] prediction

[0686] The server preprocesses the information in the database using Python's Pandas and NumPy libraries and applies it to machine learning algorithms. Models using Scikit-learn and TensorFlow learn consumption patterns and predict future energy demand based on historical data. This predictive model makes it possible to identify peak demand in advance.

[0687] optimization

[0688] The server generates efficient energy usage plans using Pyomo and SciPy based on predicted energy demand. For example, it suggests schedules to limit peak consumption or adjusts the use of specific devices to reduce unnecessary consumption. This creates a customized plan in real time that is tailored to the user's energy-saving goals.

[0689] display

[0690] The device provides users with real-time power consumption data, predicted patterns, and optimized plans via a web-based dashboard. Applications using Flask or Django visualize this information, allowing users to adjust their energy usage based on this information to reduce costs and achieve efficient energy management.

[0691] Specific example

[0692] For example, consider the energy usage of a home during the daytime in summer. The server predicts the time of day when the air conditioner will be used most and creates a suggestion to raise the temperature to avoid that peak. The terminal immediately notifies the user of this suggestion, and by adjusting the air conditioner settings, the user can use electricity efficiently.

[0693] Example of a prompt

[0694] "Predict the peak electricity demand for a specific household tomorrow and generate specific methods to reduce the peak load."

[0695] In this way, the system of the present invention uses machine learning and data analysis to provide users with optimal energy use and support sustainable consumption.

[0696] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0697] Step 1:

[0698] The server acquires power consumption information in real time from smart meters and IoT sensors. It receives power data transmitted from these sensors as input and stores it in a database. External factors such as weather data and electricity market price information are also acquired and integrated into the database. Throughout this process, secure communication is maintained based on data collection protocols.

[0699] Step 2:

[0700] The server preprocesses the information stored in the database using data processing libraries such as Pandas and NumPy. The input for preprocessing is the stored raw data, and the output is clean data with missing values ​​and outliers removed. This clean data is then prepared for machine learning in the next step.

[0701] Step 3:

[0702] The server inputs clean data into a machine learning model using Scikit-learn or TensorFlow to learn consumption patterns. Here, the input is preprocessed energy consumption data, and the output is a prediction of future energy demand. This makes it possible to identify peak demand. Training the model involves regression analysis and predictive calculations based on historical data.

[0703] Step 4:

[0704] The server uses optimization algorithms built with Pyomo and SciPy based on the forecast results to generate an efficient energy use plan. It takes forecasted demand data as input and includes an adjusted energy schedule as output. Specifically, it adjusts the operating times of specific devices and develops plans that reduce peak power usage.

[0705] Step 5:

[0706] The terminal receives display data sent from the server. It receives energy consumption information, forecast data, and optimized plans as input, and visualizes this information for the user on a dashboard. The output is a visual information display, based on a web application using Flask or Django. Users can adjust their energy usage based on this information.

[0707] Step 6:

[0708] Users adjust their energy usage behavior based on information presented on the device. Input is the information provided by the device, and output is the user's manual or automated actions. This allows users to reduce electricity costs and improve energy efficiency.

[0709] (Application Example 1)

[0710] 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".

[0711] Improving energy efficiency in physical stores directly leads to reduced environmental impact and lower operating costs. However, conventional technologies have not adequately analyzed energy usage within stores in real time and provided appropriate plans. Therefore, there is a need for new technologies that can improve store operational efficiency through the automation and optimization of energy management.

[0712] 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.

[0713] In this invention, the server includes information acquisition means for obtaining information related to power usage, prediction means for performing time-series analysis based on the acquired information and conducting consumption forecasts, and plan generation means for generating an effective energy usage plan based on the predicted consumption. This makes it possible to monitor the energy consumption status of physical stores in real time and automatically generate an effective plan.

[0714] "Information related to electricity usage" refers to data that shows the amount and patterns of electricity consumed in homes, businesses, stores, etc.

[0715] "Information acquisition means" refers to a device or system that collects information related to electricity usage using smart meters or various sensors.

[0716] Time series analysis is a method that analyzes acquired data in chronological order to reveal consumption trends and patterns.

[0717] "Consumption forecasting" is the process of estimating future energy consumption based on past data.

[0718] "Predicted consumption" refers to the projected future electricity usage calculated through time-series analysis.

[0719] An "effective energy use plan" is a feasible plan designed to maximize energy efficiency and reduce costs.

[0720] A "plan generation means" is a device or system that creates and proposes an optimized energy use plan based on predicted consumption.

[0721] A "physical store" refers to a physical location where commercial activities take place and where customers can visit in person.

[0722] A "prompt statement" is an instruction statement that is input into a generative AI model to generate information.

[0723] "Support measures" refer to devices or systems equipped with visual information provision or simulation functions to assist users in making decisions.

[0724] The system that realizes this invention supports the operation of physical stores by efficiently collecting and analyzing information related to electricity usage and generating an optimized energy usage plan.

[0725] The server collects real-time electricity usage data from within the store through an information acquisition system using smart meters and various IoT sensors. This data is then analyzed via a time-series analysis module to reveal consumption patterns and trends, and to predict future electricity usage.

[0726] Based on predicted data, the server's planning generation module utilizes machine learning algorithms with TensorFlow to create an efficient energy usage plan. This includes scheduling adjustments to avoid peak power consumption times within the store and measures to reduce unnecessary energy use.

[0727] The terminal visually presents energy consumption information and optimized plans to the user through a Plotly-based user interface. Based on this information, the user can make decisions regarding energy use in physical stores.

[0728] Furthermore, by utilizing a generative AI model that uses prompts, users can easily ask questions about energy management and receive appropriate advice. An example of a prompt is, "Create a Python script that predicts peak usage times based on energy consumption data from physical stores and proposes an optimal energy usage plan."

[0729] As a concrete example, a cafe in a bustling commercial district has achieved cost reductions by effectively managing electricity consumption during peak hours. In this way, the system can be customized to meet the needs of individual stores, supporting sustainable business operations.

[0730] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0731] Step 1:

[0732] The server collects electricity usage data in real time from smart meters and various IoT sensors. Electricity usage data is provided as input, and the server stores this data in a database. The output here is the electricity usage information stored in the database.

[0733] Step 2:

[0734] The server applies a time-series analysis algorithm to the collected power usage data. The input is the power usage information obtained in step 1, and based on this, it analyzes consumption patterns and predicts future power demand. The output is a graph of the predicted consumption pattern.

[0735] Step 3:

[0736] The server uses predicted consumption data to generate an efficient energy use plan using a machine learning algorithm powered by TensorFlow. The input is the predicted consumption data from step 2, and the program proposes an optimal energy use schedule. The output is the optimized energy use plan.

[0737] Step 4:

[0738] The terminal uses Plotly to visually present energy consumption information and the generated energy usage plan to the user. The input is the data obtained in steps 1, 2, and 3, which the terminal graphically displays on the dashboard. The output is visualized energy usage information that the user can review.

[0739] Step 5:

[0740] The user inputs prompts and questions to the system via a generative AI model. Based on this input, the server provides the user with appropriate energy optimization advice. The generated output consists of specific action plans and additional information that the user can implement.

[0741] 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.

[0742] This invention provides an energy usage plan that reflects the user's emotional state by integrating an emotion engine in addition to a power consumption management system. This system consists of data acquisition means, prediction means, optimization means, display means, and an emotion engine.

[0743] Emotional Engine

[0744] 1. Emotion recognition

[0745] The server extracts emotional data from the user's facial expressions and voice through camera and microphone sensors. This data is analyzed using a machine learning model to determine the user's current emotional state (e.g., stress, relaxation, joy).

[0746] 2. Use of emotional data

[0747] The server adjusts the energy usage plan generated by optimization means based on emotional data obtained from the emotion engine. For example, if the user is stressed, the system will suggest an energy usage plan that promotes relaxation.

[0748] Data acquisition and prediction

[0749] The server collects power consumption information from smart meters and weather data as an external factor. Using this database, machine learning algorithms are used to predict future power demand.

[0750] Optimization methods

[0751] The server generates an energy usage plan tailored to individual needs based on predictive and emotional data. For example, if a user is fatigued, it suggests a temperature setting that maintains comfort while avoiding peak hours.

[0752] Display means

[0753] The device presents the user with a dashboard showing their power consumption status, forecast results, and optimized plans. In addition, the user's emotional state is visually represented, allowing them to understand how the plan has been adjusted.

[0754] Specific example

[0755] For example, at night when a user feels tired, the server generates a plan to adjust the lighting brightness appropriately. The terminal notifies the user of this plan, and the user changes their environment accordingly. In this way, energy consumption is optimized while also caring for the user's emotional state.

[0756] The system of this invention utilizes emotional data for energy management, thereby realizing a more precise and personalized method of energy use.

[0757] The following describes the processing flow.

[0758] Step 1:

[0759] The server acquires real-time power consumption data from smart meters and sensors. Simultaneously, it collects weather data and market electricity prices using external APIs and stores this information in a database.

[0760] Step 2:

[0761] The server uses collected power consumption data and external factor data to perform time-series data analysis using machine learning algorithms. This allows it to predict future power demand and identify peak demand times.

[0762] Step 3:

[0763] The server acquires the user's facial expressions and voice data through the camera and microphone. This data is analyzed by an emotion engine to evaluate the user's emotional state in real time. This evaluation information is managed within the system along with other data.

[0764] Step 4:

[0765] The server generates an energy usage plan using optimization methods, taking into account predicted power demand and the user's emotional state. Based on the emotional data, it suggests adjusting the temperature and lighting to provide a relaxing environment for the user.

[0766] Step 5:

[0767] The device presents the generated energy usage plan to the user via a dashboard interface. The dashboard visually displays details of the current power consumption, demand forecast, and an optimized plan tailored to the user's emotional state.

[0768] Step 6:

[0769] Users can review the energy usage plan presented through their device and manually adjust settings as needed. They can also utilize the simulation function to compare different scenarios and adopt the optimal energy management approach.

[0770] Step 7:

[0771] The server collects and records feedback derived from user behavior and emotions. This data is used to retrain predictive models and the emotion engine, helping to continuously improve the overall system accuracy and user experience.

[0772] (Example 2)

[0773] 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".

[0774] The challenge lies in achieving efficient energy management while simultaneously proposing individualized energy usage plans that are sensitive to the emotional state of users. Conventional energy management systems provide plans based on electricity consumption and demand forecasts, but they do not take into account the emotional needs of users, thus improving the user experience is essential.

[0775] 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.

[0776] In this invention, the server includes information acquisition means, demand forecasting means, emotion recognition means, optimization means, and display means. This makes it possible to propose an effective and emotionally sensitive energy usage plan based on the user's power consumption patterns and emotional state.

[0777] "Information acquisition means" refers to a device or function that collects power consumption information and external environmental information.

[0778] A "demand forecasting tool" is a device or function that performs time-series data analysis on acquired information to predict future electricity demand.

[0779] "Emotion recognition means" refers to a device or function that analyzes a user's voice and video to determine their emotional state.

[0780] "Optimization means" refers to a device or function that generates an efficient and personalized energy usage plan based on predicted demand and the user's emotional state.

[0781] "Display means" refers to a device or function for visually presenting to the user information such as power consumption, optimization plans, and emotional state.

[0782] This invention is a system that integrates an emotion engine into power consumption management and provides an energy usage plan tailored to the user's emotional state. This system consists of multiple components such as a server, terminals, and sensors.

[0783] The server first uses information acquisition tools to collect power consumption information from smart meters and environmental data from external data sources. The server also monitors the user's facial expressions and voice through sensors such as cameras and microphones, and analyzes emotional data using emotion recognition tools. Machine learning models are used to determine the user's emotional state.

[0784] The server uses demand forecasting tools to predict future electricity demand from acquired power consumption information and environmental data. This process employs algorithms such as time series analysis. Subsequently, optimization tools are used to generate an energy usage plan tailored to the individual user, based on the predicted demand and emotional state. This plan takes into account factors such as reducing peak load and improving comfort.

[0785] The device displays an optimized energy usage plan to the user in a dashboard format. The display visually presents power consumption, emotional state, and plan adjustments. Based on this information, the user can adjust their environment to improve both energy efficiency and comfort simultaneously.

[0786] For example, if a user is fatigued, the server generates an energy usage plan that adjusts the room lighting to a warmer color and the music volume, and notifies the user of this suggestion via their device. In this way, an integrated plan for energy and emotional management is provided. This system is expected to improve the efficiency of energy management and enhance the user experience.

[0787] A concrete example of a prompt for a generative AI model might be, "Please suggest an energy usage plan that takes into account the user's current emotional state." This prompt allows the system to generate a plan that considers the user's emotional state.

[0788] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0789] Step 1:

[0790] The server collects power consumption information from smart meters and environmental data from external data sources using information acquisition methods. The inputs are power consumption information and environmental data, which are used directly in the next processing step. Specifically, it periodically acquires data from sensors and stores it in a database.

[0791] Step 2:

[0792] The server collects the user's facial expressions and voice through the camera and microphone, and analyzes the emotional data using emotion recognition technology. The input is the user's facial expressions and voice information, and the output is the emotional state (e.g., stress, relaxation, joy). Specifically, the acquired data is input into a machine learning model to determine the emotional state.

[0793] Step 3:

[0794] The server uses demand forecasting tools to perform time-series analysis based on the power consumption information and environmental data collected in Step 1 to predict future power demand. The input is power consumption information and environmental data, and the output is the predicted power demand. Specifically, it uses machine learning algorithms to analyze demand trends.

[0795] Step 4:

[0796] The server uses optimization techniques to generate an energy usage plan based on the emotional state in step 2 and the power demand forecast in step 3. The inputs are the emotional state and the power demand forecast, and the output is the optimized energy usage plan. Specifically, it creates a plan to avoid predicted peak times and adds settings that take the emotional state into consideration.

[0797] Step 5:

[0798] The terminal displays a dashboard-style presentation of an energy usage plan, power consumption status, and emotional state optimized for the user. Inputs are the generated energy usage plan and analyzed data, while output is a visual display on the dashboard. Specifically, the latest plan information is placed on the GUI for easy user access.

[0799] (Application Example 2)

[0800] 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".

[0801] Modern autonomous vehicles, a vital mode of transportation, require improved passenger comfort and safety. However, conventional systems struggle to instantly adjust the in-car environment to reflect the passenger's emotional state, resulting in an inability to effectively reduce passenger stress and discomfort. To address this, it is necessary to rapidly optimize the in-car environment in response to changes in the passenger's emotions and efficiently manage energy consumption.

[0802] 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.

[0803] In this invention, the server includes data acquisition means for obtaining power consumption information, emotion recognition means for recognizing the emotional state of the occupants, and environment adjustment means for adjusting the in-vehicle environment based on the recognized emotional state. This makes it possible to optimize energy use while responding quickly to the emotional state of the occupants.

[0804] "Power consumption information" refers to data that shows how electricity is being used, including consumption per hour and power consumption for specific devices.

[0805] "Data acquisition means" refers to a device or method for collecting necessary data such as power consumption information and external factor information.

[0806] "Demand forecasting" is the process of predicting future electricity consumption based on acquired data, and includes time-series data analysis.

[0807] "Optimization means" refers to techniques or methods for designing efficient energy use plans in response to predicted demand.

[0808] "Display means" refers to an interface or display device for visually presenting information or plans to a user.

[0809] "Emotion recognition means" refers to technology or devices that analyze the facial expressions and voices of crew members to determine their emotional state at a given time.

[0810] "Environmental adjustment means" refers to a device or method for appropriately changing the in-vehicle environment based on recognized emotional states.

[0811] This invention provides a system primarily for autonomous vehicles. The system aims to improve occupant safety and comfort, as well as to optimize energy consumption. Its main components, such as servers, terminals, and users, function in a collaborative manner.

[0812] The server collects power consumption information using data acquisition methods. Based on this, it predicts future power demand based on time-series data analysis and generates an efficient energy use plan through optimization methods. It also uses emotion recognition methods such as cameras and microphones to recognize the emotional state of the occupants. This emotion data is used as an environmental adjustment method to adjust the vehicle's air conditioning, lighting, and other settings.

[0813] The device uses a display to present users with power consumption information and optimized plans tailored to their emotional state in a dashboard format. This allows users to visually understand how their plans have been adjusted.

[0814] For example, if passengers experience stress during a long drive, the server can adjust the lighting in real time and play relaxing music to improve passenger comfort. The software used includes OpenCV and an Emotion Recognition model, which enables rapid recognition and analysis of emotions, allowing for appropriate environmental adjustments.

[0815] An example of a prompt message would be: "In autonomous vehicles, we propose a method to understand how occupants are feeling in real time and optimize the in-vehicle environment according to their emotions."

[0816] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0817] Step 1:

[0818] The server collects vehicle power consumption information through data acquisition methods. Inputs include power usage data from devices within the vehicle and external weather information. This data is converted into a format suitable for time-series data analysis, preparing it for analysis.

[0819] Step 2:

[0820] The server performs time-series data analysis based on the acquired power consumption information and conducts demand forecasting. The input is the data collected in step 1, and the output is forecast data showing future power demand. This forecast result is obtained using a machine learning algorithm.

[0821] Step 3:

[0822] The server uses emotion recognition technology to determine the emotional state of the occupants. Real-time video and audio data from in-vehicle cameras and microphones are used as input and analyzed by an Emotion Recognition model. The output is a classification result based on the occupants' emotional state (e.g., relaxed, stressed).

[0823] Step 4:

[0824] The server generates an efficient energy use plan using optimization methods based on predicted demand data and emotional state. Inputs include demand forecast data and emotional state data, and the output is the adjusted energy use plan. Specific actions such as adjusting air conditioning temperature and lighting are proposed.

[0825] Step 5:

[0826] The terminal presents the user with power consumption information and the generated energy usage plan via a display device. This information is visualized on a dashboard. The input is the plan generated in step 4, and the output is the information visually presented to the user.

[0827] Step 6:

[0828] The user can approve or modify the adjustments suggested by the system based on the information provided. If adjustments are made, the information is sent back to the server, and the plan is updated as necessary.

[0829] 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.

[0830] 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.

[0831] 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 robot 414.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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."

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] The following is further disclosed regarding the embodiments described above.

[0851] (Claim 1)

[0852] A data acquisition method for obtaining power consumption information,

[0853] A forecasting method that performs time-series data analysis based on acquired power consumption information to forecast demand,

[0854] An optimization means for generating an efficient energy use plan based on predicted demand,

[0855] A display means for visually presenting power consumption information and optimized plans to the user,

[0856] A system that includes this.

[0857] (Claim 2)

[0858] The system according to claim 1, which acquires environmental data including external factor information through data acquisition means, and performs demand forecasting by taking this data into consideration in the forecasting means.

[0859] (Claim 3)

[0860] The system according to claim 1, wherein the display means provides the user with a function to simulate multiple selectable energy usage scenarios and supports the selection.

[0861] "Example 1"

[0862] (Claim 1)

[0863] Information gathering means for obtaining electricity consumption information,

[0864] A forecasting method that performs numerical analysis based on acquired electricity consumption information to forecast demand,

[0865] An optimization means for generating an efficient energy use plan based on predicted demand,

[0866] A display means that visually presents power consumption information and optimized plans to the user,

[0867] A method that includes a process of preprocessing data, learning consumption patterns using machine learning algorithms, and estimating future energy demand,

[0868] An optimization method that dynamically adjusts energy use plans based on real-time data and predictive information,

[0869] A system that includes this.

[0870] (Claim 2)

[0871] The system according to claim 1, which acquires environmental information including external element information through information gathering means and performs demand forecasting taking this information into consideration in the forecasting means.

[0872] (Claim 3)

[0873] The system according to claim 1, wherein the display means provides the user with a function to simulate multiple selectable energy usage scenarios and supports the selection.

[0874] "Application Example 1"

[0875] (Claim 1)

[0876] Information acquisition means for obtaining information related to electricity usage,

[0877] A forecasting method that performs time-series analysis based on acquired information and conducts consumption forecasts,

[0878] A planning generation means that generates an effective energy use plan based on predicted consumption,

[0879] A presentation means for visually presenting power usage information and optimized plans to users,

[0880] An implementation method that monitors energy consumption within the store in real time, analyzes energy consumption patterns, and provides an optimal plan.

[0881] A support system that generates information using prompt statements and assists users in making decisions,

[0882] A system that includes this.

[0883] (Claim 2)

[0884] The system according to claim 1, which acquires environmental data including external factor data, makes consumption predictions considering this data in a prediction means, and optimizes the energy use of the store.

[0885] (Claim 3)

[0886] The system according to claim 1, wherein the presentation means provides the user with a function to simulate multiple selectable energy usage patterns and assists in making a selection.

[0887] "Example 2 of combining an emotion engine"

[0888] (Claim 1)

[0889] Information acquisition means for obtaining power consumption information,

[0890] A demand forecasting method that performs time-series data analysis based on acquired power consumption information and conducts demand forecasting,

[0891] To recognize the user's emotional state, an emotion recognition means analyzes audio and video,

[0892] An optimization means for generating an efficient and user-friendly energy usage plan based on predicted demand and perceived emotional states,

[0893] A display means that visually presents the user with power consumption information, an optimized plan, and their emotional state,

[0894] A system that includes this.

[0895] (Claim 2)

[0896] The system according to claim 1, which acquires environmental data including external factor information through an information acquisition means and performs demand forecasting by taking this data into consideration in a demand forecasting means.

[0897] (Claim 3)

[0898] The system according to claim 1, wherein the display means provides the user with a function to simulate multiple selectable energy usage scenarios and assists in making a selection.

[0899] "Application example 2 when combining with an emotional engine"

[0900] (Claim 1)

[0901] A data acquisition method for obtaining power consumption information,

[0902] A forecasting method that performs time-series data analysis based on acquired power consumption information to forecast demand,

[0903] An optimization means for generating an efficient energy use plan based on predicted demand,

[0904] A display means for visually presenting power consumption information and optimized plans to the user,

[0905] An emotion recognition means for recognizing the emotional state of the crew,

[0906] Environmental adjustment means for adjusting the in-car environment based on recognized emotional states,

[0907] A system that includes this.

[0908] (Claim 2)

[0909] The system according to claim 1, which acquires environmental data including external factor information through data acquisition means, and performs demand forecasting by taking this data into consideration in the forecasting means.

[0910] (Claim 3)

[0911] The system according to claim 1, wherein the display means provides the user with a function to simulate multiple selectable energy usage scenarios and supports the selection. [Explanation of symbols]

[0912] 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 data acquisition method for obtaining power consumption information, A forecasting method that performs time-series data analysis based on acquired power consumption information to forecast demand, An optimization means for generating an efficient energy use plan based on predicted demand, A display means for visually presenting power consumption information and optimized plans to the user, A system that includes this.

2. The system according to claim 1, which acquires environmental data including external factor information through data acquisition means, and performs demand forecasting taking this data into consideration in the forecasting means.

3. The system according to claim 1, wherein the display means provides the user with a function to simulate multiple selectable energy usage scenarios and supports the selection.

Images