System

Abstract

To provide a system capable of improving efficiency of energy consumption and reducing an environmental load.SOLUTION: The system includes means for collecting household or business power consumption data in real time, means for acquiring weather data, means for acquiring renewable energy supply prediction data, means for analyzing a power consumption pattern based on the collected data, means for analyzing fluctuations in renewable energy supply due to weather fluctuations, means for generating a consumption prediction model, means for integrating consumption prediction and supply prediction and simulating a supply-demand balance, means for creating an optimal supply plan, means for notifying a household or business of the supply plan, and means for adjusting a supply amount.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In today's world, improving the efficiency of energy consumption and expanding the use of renewable energy are important issues for combating global warming and realizing a sustainable society. However, it is difficult to respond flexibly and efficiently to sudden fluctuations in electricity consumption in homes and businesses and fluctuations in the supply of renewable energy due to weather conditions, making it difficult to achieve optimal energy management. For this reason, existing energy management systems have limitations in improving energy efficiency and reducing environmental impact. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems with a system including means for collecting electricity consumption data from homes and businesses in real time, means for acquiring weather data, means for acquiring renewable energy supply forecast data, means for analyzing electricity consumption patterns based on the collected data, means for analyzing fluctuations in renewable energy supply due to weather changes, means for generating a consumption forecast model, means for integrating consumption forecasts and supply forecasts to simulate the supply and demand balance, means for creating an optimal supply plan, means for notifying homes and businesses of the supply plan, and means for adjusting the supply amount.Furthermore, by providing means for detecting and immediately responding to sudden fluctuations in electricity consumption, means for monitoring the data collected in real time, means for notifying users in real time, and means for creating a supply plan that prioritizes the use of renewable energy and supplements the supply from the electric power company as necessary, the system achieves more efficient energy consumption and reduces environmental impact.

[0006] "Electricity consumption data" is information that indicates the amount of electricity a home or business uses.

[0007] "Weather data" is information about weather conditions, specifically including temperature, wind speed, sunlight illuminance, and the like.

[0008] "Renewable energy" is energy that is produced from naturally renewable resources, including solar, wind, and hydroelectric power.

[0009] "Supply forecast data" is information that predicts the supply of renewable energy, and is generated based on weather data and the like.

[0010] An "energy management system" is a system for efficiently managing power consumption and power supply.

[0011] The "server" is a central computer system that collects and analyzes various data and creates and executes supply plans.

[0012] A "terminal" is equipment such as a smart meter or IoT device installed in a home or business that collects electricity consumption data and sends it to a server.

[0013] "Users" are consumers, businesses, and other users of this system.

[0014] "Supply Plan" means a plan formulated to optimize the supply of electricity, including the use of renewable energy and the supply of external electricity as needed.

[0015] "Supply and demand balance" refers to the balance between the demand and supply of electricity, and refers to a state in which the amount of electricity consumed and the amount of electricity supplied are appropriately matched.

[0016] A "consumption prediction model" is a model used to predict future power consumption based on past consumption data.

[0017] "Real-time monitoring" means instantly monitoring an ongoing situation, allowing for instantaneous data collection and response. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0022] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0023] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0036] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] This invention relates to an AI energy management system for efficiently managing energy consumption and supply, and specific embodiments thereof will be described below.

[0040] First, let's look at the part that collects electricity consumption data from homes and businesses in real time. The server periodically collects electricity consumption data from terminals (e.g., smart meters and IoT devices). The terminals are installed in homes and businesses, measure electricity consumption in real time, and send that data to the server. This allows the server to accurately grasp each user's consumption status.

[0041] Next, regarding weather data, the server obtains various weather information from the weather data provider's API and stores it in an analytical database. This weather data includes temperature, wind speed, solar irradiance, etc., and is used to forecast the supply of renewable energy.

[0042] Regarding renewable energy supply forecasts, the server makes supply forecasts for renewable energy (e.g., solar power generation, wind power generation) based on past supply data and weather data. This supply forecast data is combined with current supply status data provided by the power company to make a comprehensive supply forecast.

[0043] In the consumption pattern analysis, the server analyzes each user's electricity consumption pattern based on past consumption data. For example, household consumption patterns reveal peak times and average consumption. The results of this analysis are used to create a future consumption prediction model.

[0044] By integrating consumption and supply forecasts and simulating the supply and demand balance, the server creates an optimal energy supply plan based on this forecast data. This plan prioritizes the use of renewable energy and makes up for any shortfall with supplies from the power company.

[0045] As a specific example, suppose the server records that household A's power consumption is 3kW at 8am, and obtains from local weather data that the sunlight illuminance is 800W / m².Then, it predicts that the amount of solar power generation will increase at 10am, and a policy is made to cover household A's energy supply with renewable energy.

[0046] For real-time adjustments, the server monitors consumption and supply data in real time and takes immediate action if there is a discrepancy between the forecast and reality. For example, if it suddenly becomes cloudy at 1 p.m. and the amount of solar power generation decreases, the server will adjust the amount of supply from the power company to increase, preventing a power shortage. Terminals can also dynamically change their consumption patterns according to instructions from the server.

[0047] Users can check real-time power consumption and supply status through a dedicated app, and can also turn power saving mode on or off, and set alerts to avoid peak power usage during specific times.

[0048] This system will optimize energy consumption and reduce environmental impact. The present invention will play an important role in effectively utilizing renewable energy and improving energy efficiency.

[0049] The processing flow will be explained below.

[0050] Step 1:

[0051] The server collects power consumption data from devices installed in homes and businesses. The devices measure real-time power consumption at predetermined intervals (e.g., every minute) and send the data to the server.

[0052] Step 2:

[0053] The server retrieves weather data from weather data providers via API, including temperature, wind speed, and sunlight intensity, and stores this data in a database for analysis.

[0054] Step 3:

[0055] The server predicts renewable energy supply based on past supply data and acquired weather data. The supply forecast data is integrated with current supply status data provided by the power company.

[0056] Step 4:

[0057] The server analyzes historical consumption data to extract power consumption patterns for each home or business. This analysis identifies peak hours and average consumption, and generates a consumption forecasting model.

[0058] Step 5:

[0059] The server integrates consumption and supply forecasts to simulate the future balance of supply and demand, and then uses this forecast data to create an optimal energy supply plan.

[0060] Step 6:

[0061] The server notifies the terminal of the supply plan and instructs it to make necessary adjustments. The terminal then adjusts its power usage in real time according to the instructions from the server.

[0062] Step 7:

[0063] A supply plan is created so that renewable energy is used first and any shortfall is made up by supply from the power company. The server sends instructions to the power company to implement this plan.

[0064] Step 8:

[0065] The server monitors consumption and supply data collected in real time and responds immediately if it detects an abnormality, such as a sudden change in weather or a sudden increase in power usage by users.

[0066] Step 9:

[0067] The server notifies the user in real time, and the user can check the current power consumption and supply status through a dedicated app and change the settings as needed.

[0068] Step 10:

[0069] The server regularly updates consumption forecasts and supply plans to continuously optimize energy efficiency, resulting in more efficient energy use and a reduced environmental impact.

[0070] Example 1

[0071] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0072] As renewable energy becomes more widespread, there is a need to efficiently manage the balance between energy supply and consumption. However, due to weather fluctuations and sudden changes in consumption, it is difficult to predict the supply of renewable energy, making it difficult to optimize energy supply plans. In addition, there is a lack of means for users to grasp consumption and supply conditions in real time. It is necessary to solve these issues and achieve improved energy efficiency and reduced environmental impact.

[0073] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0074] In this invention, the server includes means for collecting power consumption data in real time, means for acquiring weather information, means for collecting renewable energy supply forecast data, means for analyzing power consumption patterns based on the collected data, means for analyzing fluctuations in renewable energy supply according to weather changes, means for generating a consumption forecast model, means for simulating the supply and demand balance by integrating the consumption forecast and the supply forecast, means for creating an optimal energy supply plan, means for providing the energy supply plan to users, means for adjusting the supply amount, and means for monitoring the energy consumption and supply status in real time, thereby enabling efficient management of the balance between energy supply and consumption.

[0075] "Power consumption data" is data that indicates the amount of power consumed in a home or business over a certain period of time.

[0076] "Weather information" is data relating to weather conditions, including temperature, wind speed, sunlight illuminance, and the like.

[0077] "Renewable energy supply forecast data" is data used to predict the supply of renewable energy, such as solar power generation and wind power generation.

[0078] The term "electricity consumption pattern" refers to the tendency or characteristics of electricity consumption during a specific period, analyzed based on past electricity consumption data.

[0079] "Variations in renewable energy supply" means changes in the supply of renewable energy caused by weather or other external factors.

[0080] A "consumption forecasting model" is a mathematical or computational model for predicting future electricity consumption based on past consumption data, etc.

[0081] "Supply and demand balance simulation" is a method of virtually verifying the balance between energy supply and consumption based on electricity consumption forecast data and supply forecast data.

[0082] The "energy supply plan" is a plan designed to optimize the energy supply required by each user, prioritizing the use of renewable energy and supplementing any shortfall with external power supplies.

[0083] The "means for providing an energy supply plan to a user" refers to a method or system for notifying or displaying an optimized energy supply plan to a user.

[0084] "Means for adjusting supply amount" refers to a system or method for increasing or decreasing the external power supply when the supply of renewable energy is insufficient.

[0085] "Means for monitoring energy consumption and supply status in real time" refers to a method or system that instantly tracks the current status of power consumption and supply and takes appropriate action in response to fluctuations.

[0086] This invention relates to an AI energy management system for efficiently managing energy consumption and supply. Specific embodiments are described below.

[0087] 1. System Overview

[0088] This system consists of a server, terminals, and users. The server collects and analyzes energy consumption data and weather data, and predicts renewable energy supply. The terminals measure power consumption data and send it to the server. Users can check real-time power consumption and supply status through a dedicated app.

[0089] 2. Hardware and software used

[0090] Server: Performs data collection, analysis, prediction, simulation, and adjustment. In particular, it utilizes cloud platforms such as AWS (Amazon Web Services) and Microsoft Azure.

[0091] Devices: Smart meters, IoT devices (e.g., SmartThings, Nest Thermostat).

[0092] Weather data APIs: OpenWeatherMap, Weatherstack.

[0093] Analytics databases: Amazon RDS, MySQL, PostgreSQL.

[0094] 3. System Operation

[0095] Server Operation

[0096] The server collects real-time electricity consumption data from homes and businesses. For this purpose, the server periodically collects electricity consumption data from terminals (e.g., smart meters and IoT devices). This allows the server to accurately understand the consumption status of each user.

[0097] Next, the server periodically retrieves weather data from weather data providers' APIs (e.g., OpenWeatherMap, Weatherstack) and stores it in a database for analysis. The retrieved weather data includes temperature, wind speed, and sunlight irradiance.

[0098] The server also generates forecasts for renewable energy sources (e.g., solar and wind power) based on historical supply and weather data. This forecast data is combined with current supply data provided by the power company to produce a comprehensive forecast.

[0099] Device behavior

[0100] The device is installed at the site (home or business) and measures power consumption in real time. The measured data is then sent to the server. For example, the device detects that home A's power consumption is 3kW at 8:00 AM and sends that data to the server.

[0101] User operations

[0102] Users can check real-time power consumption and supply status through the dedicated app. They can also turn power saving mode on or off and set alerts to avoid peak power usage during specific times. For example, if a user opens the app at 9 a.m., the current power consumption, supply, and weather information will be displayed.

[0103] 4. Example of a system

[0104] As a specific example, suppose the server records that the power consumption of household A is 3kW at 8am and obtains that the sunlight illuminance in the area is 800W / m².Then, it predicts that the amount of solar power generation will increase at 10am, and a policy is made to cover household A's energy supply with renewable energy.

[0105] If the weather suddenly becomes cloudy at 1 p.m. and the amount of solar power generation decreases, the server will adjust the amount of power supplied by the power company to increase, preventing a power shortage. The device can also dynamically change its consumption pattern according to instructions from the server.

[0106] Prompt Sentence Examples

[0107] "Record the electricity consumption of household A from 8:00 AM onwards, obtain weather data (sunlight illuminance) for the area, and predict the solar power generation supply for 10:00 AM. Also, explain how users can check the real-time situation through a dedicated app."

[0108] This system will optimize energy consumption and reduce environmental impact. The present invention will play an important role in effectively utilizing renewable energy and improving energy efficiency.

[0109] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0110] Step 1:

[0111] Subject: Device

[0112] Terminals (smart meters and IoT devices) measure the electricity consumption data of homes and businesses in real time. The measured data is sent to a server every few minutes. For example, at 8:00 AM, the electricity consumption of home A is measured to be 3 kW, and this data is sent to the server.

[0113] Input: Current electricity consumption for your home or business

[0114] Output: Power consumption data sent to the server

[0115] Step 2:

[0116] Subject: Server

[0117] The server periodically obtains weather information from weather data provider APIs (e.g., OpenWeatherMap, Weatherstack) and stores it in a database for analysis. The obtained weather data includes temperature, wind speed, and sunlight irradiance. For example, it obtains that the sunlight irradiance in the area is 800W / m² at 8:00 AM and stores this data in the database.

[0118] Input: Weather data from a weather data provider

[0119] Output: Weather data stored in a database for analysis

[0120] Step 3:

[0121] Subject: Server

[0122] The server analyzes past consumption data to identify each user's consumption pattern. The analysis results, including peak hours and average consumption, are used to create a consumption prediction model. For example, the evening peak hours can be identified from the consumption pattern of household A.

[0123] Input: Historical electricity consumption data

[0124] Output: Identified consumption pattern data

[0125] Step 4:

[0126] Subject: Server

[0127] The server predicts the supply of renewable energy (e.g., solar power, wind power) based on past supply data and acquired weather data. For example, it predicts that the supply of solar power will increase at 10:00 a.m.

[0128] Input: Historical renewable energy supply data and weather data

[0129] Output: Forecasted renewable energy supply data

[0130] Step 5:

[0131] Subject: Server

[0132] The server integrates the consumption forecast data and supply forecast data to simulate the supply and demand balance. This simulation creates an optimal energy supply plan. For example, it simulates both the case where all of the energy consumed by household A is covered by solar power generation, and the case where the shortfall is supplemented by the power company.

[0133] Input: Consumption forecast data and supply forecast data

[0134] Output: Simulated supply and demand balance data

[0135] Step 6:

[0136] Subject: Server

[0137] The server creates an optimal energy supply plan and notifies households and businesses of that plan. For example, at 10:00 AM, household A will be informed of its plan to run solely on renewable energy sources.

[0138] Input: Simulated supply and demand balance data

[0139] Output: Energy supply plan provided to user

[0140] Step 7:

[0141] Subject: Server

[0142] The server monitors consumption and supply data in real time and responds immediately if there is a discrepancy between the forecast and the actual situation. For example, if it suddenly becomes cloudy at 1 p.m. and the amount of solar power generation decreases, the server will adjust the amount of power supplied by the power company to increase it.

[0143] Input: Real-time consumption and supply data

[0144] Output: Adjusted supply data

[0145] Step 8:

[0146] Subject: User

[0147] Users can check real-time electricity consumption and supply status through the dedicated app. They can also turn power saving mode on and off and set alerts to avoid peak power usage during specific times. For example, when a user opens the app at 9:00 a.m., the app will display the consumption status, supply amount, and weather information up to that point.

[0148] Input: Real-time electricity consumption and supply data

[0149] Output: Consumption and supply information displayed to the user

[0150] (Application example 1)

[0151] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0152] Modern factories operate a wide variety of equipment, making energy consumption optimization a key issue. Balancing renewable energy supply and consumption is particularly difficult, making inefficient energy use more likely. There is also a need for immediate responses to sudden fluctuations in power consumption, as well as real-time data monitoring and notification. Solving these issues and maximizing energy efficiency while reducing environmental impact are essential.

[0153] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0154] In this invention, the server includes means for collecting electricity consumption data for homes and businesses in real time, means for acquiring weather data, means for acquiring renewable energy supply forecast data, means for analyzing electricity consumption patterns based on the collected data, means for analyzing fluctuations in renewable energy supply due to weather changes, means for generating a consumption forecast model, means for integrating consumption forecasts and supply forecasts to simulate the supply and demand balance, means for creating an optimal supply plan, means for notifying homes and businesses of the supply plan, means for adjusting the supply amount, and means for acquiring energy consumption data for each device in a factory in real time and optimizing the operating status of the device based on the renewable energy supply amount, thereby balancing the supply and demand of renewable energy, maximizing energy efficiency, and reducing environmental impact.

[0155] "Means for collecting real-time electricity consumption data from homes and businesses" refers to devices and methods that measure electricity consumption in real time from smart meters and IoT devices installed in homes and businesses and transmit the data to a server.

[0156] "Means for acquiring weather data" refers to devices or methods for acquiring weather information such as temperature, wind speed, and sunlight illuminance from the weather data provider's API.

[0157] "Means for acquiring renewable energy supply forecast data" refers to a device or method for predicting the supply of renewable energy such as solar power generation and wind power generation based on past supply data and weather data, and acquiring that data.

[0158] The "means for analyzing power consumption patterns based on collected data" refers to a device or method for analyzing collected power consumption data and clarifying the consumption patterns of each user.

[0159] The "means for analyzing fluctuations in renewable energy supply due to weather fluctuations" refers to a device or method for analyzing changes in the amount of renewable energy supply due to fluctuations in weather conditions.

[0160] The "means for generating a consumption prediction model" refers to a device or method for generating a model that predicts future consumption based on past power consumption data.

[0161] The "means for integrating consumption forecast and supply forecast to simulate the supply and demand balance" refers to a device or method for integrating consumption forecast data and supply forecast data to simulate the energy supply and demand balance.

[0162] The "means for creating an optimal supply plan" refers to a device or method for formulating an optimal energy supply plan based on the results of the simulation.

[0163] A "means for communicating a supply plan to a home or business" is a device or method that notifies a user of the energy supply plan that has been created.

[0164] The "means for adjusting the supply amount" refers to a device or method for appropriately adjusting the supply amount in accordance with the actual energy consumption situation.

[0165] "Means for obtaining energy consumption data for each piece of equipment in a factory in real time" refers to a device or method for measuring the energy consumption of each piece of equipment installed in a factory in real time and collecting the data.

[0166] "Means for optimizing the operating status of equipment based on the amount of renewable energy supply" refers to devices and methods that optimize the operating status of equipment within a factory in accordance with the predicted amount of renewable energy supply, and operate it efficiently.

[0167] This invention relates to an AI energy management system for efficiently managing energy consumption and supply, and specific embodiments thereof are described below.

[0168] First, the server collects real-time electricity consumption data from homes and businesses. This is achieved by installing smart meters and IoT devices in homes and businesses. These devices measure electricity consumption in real time and send that data to the server, allowing the server to accurately track each user's consumption.

[0169] The server then retrieves weather data from the weather data provider's API, including temperature, wind speed, and solar irradiance, and stores the weather data in an analytical database for use in forecasting renewable energy supply.

[0170] When forecasting renewable energy supplies, the server uses past supply data and weather data to predict the supply of solar and wind power. This supply forecast data is combined with current supply status data provided by the power company to produce a comprehensive supply forecast.

[0171] In analyzing consumption patterns, the server analyzes each user's electricity consumption patterns based on past consumption data. For example, peak times and average consumption amounts can be determined from household consumption patterns. The results of this analysis are used to create a model for predicting future consumption.

[0172] The server integrates consumption and supply forecasts to simulate the supply-demand balance and creates an optimal energy supply plan based on these forecast data. This plan prioritizes the use of renewable energy and supplements shortfalls with supplies from the power company.

[0173] As a specific example, suppose the server records that household A's power consumption is 3kW at 8am, and obtains from local weather data that the sunlight illuminance is 800W / m².Then, it predicts that the amount of solar power generation will increase at 10am, and a policy is made to cover household A's energy supply with renewable energy.

[0174] In addition, the server collects energy consumption data for each piece of equipment in the factory in real time and optimizes the equipment's operating status based on the amount of renewable energy supplied. For example, the server uses energy consumption data and supply forecast data to optimize the operating status of equipment operating in the factory in real time.

[0175] If there is a discrepancy between the forecast and reality, the server responds immediately. For example, if it suddenly becomes cloudy at 1 p.m. and the amount of solar power generation decreases, the server will adjust the amount of power supplied by the power company to increase, preventing a power shortage. At the same time, it will notify the user in real time as appropriate.

[0176] Users can check real-time power consumption and supply status through a dedicated app, and can also turn power saving mode on or off and set alerts to avoid peak power usage during specific times.

[0177] In this way, the system optimizes energy consumption and contributes to reducing environmental impact.The present invention plays an important role because it can effectively utilize renewable energy and improve energy efficiency.

[0178] An example of a specific prompt is, "Using the following dataset, build a predictive model for a factory's energy consumption and renewable energy supply. Then, design an optimization algorithm that operates equipment when supply exceeds consumption and shuts it down when supply does not."

[0179] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0180] Step 1:

[0181] The server collects real-time electricity consumption data from homes and businesses. This is done using smart meters and IoT devices, with each device measuring its electricity consumption and sending the data to the server. The input of this data is the real-time measurement value from each device, and the output is consumption data accumulated on the server.

[0182] Step 2:

[0183] The server retrieves weather data from the weather data provider's API. To do this, it makes an API request to obtain weather information such as temperature, wind speed, and sunlight illuminance as input data, which it then stores in a database for analysis. The output is the retrieved weather data.

[0184] Step 3:

[0185] The server predicts the supply of renewable energy based on past supply data and weather data. For example, it predicts future power generation based on past solar power generation data and current weather data. The input data are past supply data and current weather data, and the output is supply forecast data.

[0186] Step 4:

[0187] The server analyzes each user's consumption pattern based on the collected power consumption data. This process involves analyzing the consumption data of each household or business over time to determine peak hours, average consumption, etc. The input data is past consumption data, and the output is the analysis result of the consumption pattern.

[0188] Step 5:

[0189] The server integrates the analyzed consumption patterns with the supply forecast data and simulates the supply and demand balance. For example, it calculates the supply and consumption balance based on the predicted power generation amount and the consumption amount of each user. The input data are the analysis results of consumption patterns and the supply forecast data, and the output is the simulation result of the supply and demand balance.

[0190] Step 6:

[0191] The server creates an optimal energy supply plan based on the simulation results. This process maximizes the use of renewable energy and, as necessary, creates a plan that supplements supply from the power company. The input data are the simulation results of the supply and demand balance, and the output is the optimal supply plan.

[0192] Step 7:

[0193] The server then communicates the supply plan to homes and businesses, informing them of the optimal energy supply plan through a dedicated app or notification system. The input data is the optimal supply plan, and the output is the notification to the user.

[0194] Step 8:

[0195] Based on instructions from the server, the terminal obtains energy consumption data for each piece of equipment in the factory in real time and optimizes the equipment's operating status based on the amount of renewable energy supply. For example, it determines which equipment can operate based on supply forecasts. The input data are real-time consumption data and supply forecast data, and the output is the optimized operating status of the equipment.

[0196] Step 9:

[0197] The server responds immediately if there is a discrepancy between the prediction and reality. For example, if a sudden change in weather causes a decrease in solar power generation supply, the server adjusts to increase supply from the power company. The input data are current consumption data and supply data, and the output is the adjusted supply amount.

[0198] Step 10:

[0199] Users can check real-time power consumption and supply status through a dedicated app. The app provides users with various operations such as turning power saving mode on and off and setting alerts for power usage during specific time periods. The input data is real-time consumption and supply status, and the output is the result of user operations.

[0200] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0201] This invention is an AI energy management system for effectively managing energy consumption and supply, and further improves user satisfaction by incorporating an emotion engine that recognizes user emotions. Specific embodiments are described below.

[0202] First, we will explain the part that collects electricity consumption data from homes and businesses in real time. The server periodically collects electricity consumption data from devices (e.g., smart meters and IoT devices installed in homes and businesses). The devices measure this electricity consumption data and send it to the server in real time. This allows the server to understand the consumption status of each user.

[0203] Next, we will explain weather data. The server obtains weather data (e.g., temperature, wind speed, and sunlight irradiance) from weather data providers via API and stores this data in an analytical database. The weather data is used to forecast the supply of renewable energy.

[0204] Regarding renewable energy supply forecasts, the server makes predictions based on past supply data and acquired weather data. This supply forecast data is then integrated with current supply status data provided by the power company to produce a comprehensive supply forecast.

[0205] In the consumption pattern analysis, the server analyzes each user's electricity consumption pattern based on past consumption data. Peak times and average consumption are identified from the past consumption data, and a consumption prediction model is generated based on this.

[0206] By integrating consumption and supply forecasts and simulating the supply and demand balance, the server creates an optimal energy supply plan based on this forecast data.The supply plan prioritizes the use of renewable energy and makes up for any shortfall with supplies from the power company.

[0207] As a specific example, the server records that household A's power consumption is 3kW at 8am, and obtains from local weather data that the sunlight illuminance is 800W / m². Based on this, and based on a prediction that the amount of solar power generation will increase at 10am, a plan is made to cover household A's energy supply with renewable energy.

[0208] For real-time adjustments, the server monitors the collected consumption and supply data in real time and takes immediate action if there is a discrepancy between the forecast and reality. For example, if it suddenly becomes cloudy at 1 p.m. and the amount of solar power generation decreases, the server will adjust the amount of power supplied by the power company to increase. In addition, terminals can dynamically change the consumption patterns of home appliances and other devices according to instructions from the server.

[0209] The system also includes an emotion engine that recognizes the user's emotions. This emotion engine reads emotions from the user's facial expressions and voice and sends the data to a server. The server then uses the emotion data to create a power supply plan to avoid peak power consumption during times when the user is feeling stressed. It can also adjust the content of notifications, for example, recommending power-saving mode when the user is relaxing.

[0210] As a specific example, if the emotion engine recognizes that a user is stressed while getting ready for work at 8 a.m., the server will notify the user and adjust the operation of home appliances to avoid peak power consumption in order to reduce stress.

[0211] In this way, combining the emotion engine enables energy management according to the user's emotional state, reducing stress and achieving efficient energy use. This system optimizes energy consumption, reduces environmental impact, and improves user satisfaction.

[0212] The processing flow will be explained below.

[0213] Step 1:

[0214] The server collects power consumption data from devices installed in homes and businesses. The devices measure real-time power consumption at predetermined intervals (e.g., every minute) and send the data to the server.

[0215] Step 2:

[0216] The server retrieves weather data from weather data providers via API, including temperature, wind speed, and sunlight intensity, and stores this data in a database for analysis.

[0217] Step 3:

[0218] The server predicts renewable energy supply based on past supply data and acquired weather data. The supply forecast data is integrated with current supply status data provided by the power company.

[0219] Step 4:

[0220] The server analyzes historical consumption data to extract power consumption patterns for each home or business. This analysis identifies peak hours and average consumption, and generates a consumption forecasting model.

[0221] Step 5:

[0222] The server integrates consumption and supply forecasts to simulate the future balance of supply and demand, and then uses this forecast data to create an optimal energy supply plan.

[0223] Step 6:

[0224] The server notifies the terminal of the supply plan and instructs it to make necessary adjustments. The terminal then adjusts its power usage in real time according to the instructions from the server.

[0225] Step 7:

[0226] A supply plan is created so that renewable energy is used first and any shortfall is made up by supply from the power company. The server sends instructions to the power company to implement this plan.

[0227] Step 8:

[0228] The server monitors consumption and supply data collected in real time and responds immediately if it detects an abnormality, such as a sudden change in weather or a sudden increase in power usage by users.

[0229] Step 9:

[0230] The server receives the user's emotional data through the emotion engine, which analyzes the user's facial expressions and voice and transmits the user's emotional state to the server.

[0231] Step 10:

[0232] The server optimizes power consumption and adjusts notifications based on the user's emotional data. For example, if the user is feeling stressed, it will adjust the notification to avoid peak power consumption.

[0233] Step 11:

[0234] The server notifies the user in real time, and the user can check the current power consumption and supply status through a dedicated app and change the settings as needed.

[0235] Step 12:

[0236] The server regularly updates consumption forecasts and supply plans to continuously optimize energy efficiency, resulting in more efficient energy use and a reduced environmental impact.

[0237] Example 2

[0238] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0239] Conventional energy management systems have difficulty efficiently managing the balance between power consumption and supply, and have had particular issues with being unable to respond to sudden fluctuations in consumption or weather conditions. Furthermore, energy management does not take into account the emotional state of the user, making it difficult to contribute to improving user satisfaction or reducing stress.

[0240] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0241] In this invention, the server includes means for collecting energy consumption data from homes and business facilities in real time, means for acquiring weather information, means for acquiring supply forecast data for renewable resources, means for analyzing energy consumption patterns based on the collected data, means for analyzing fluctuations in renewable energy supply due to weather changes, means for generating a consumption forecast model, means for integrating consumption forecasts and supply forecasts to simulate the supply and demand balance, means for creating an optimal supply plan, means for notifying the home and business facilities of the supply plan, means for adjusting the supply amount, means for acquiring user emotion data, and means for creating an energy management plan based on the user emotion, thereby enabling efficient energy management and improving user satisfaction.

[0242] "Energy consumption data" refers to data that indicates the amount of energy consumed in a home or business facility.

[0243] "Weather information" is data indicating weather conditions such as temperature, wind speed, and sunlight illuminance.

[0244] "Renewable resources" refers to renewable natural resources such as solar, wind, and hydropower, and provide energy derived from these.

[0245] "Supply forecast data" is data that forecasts the amount of energy supply that can be obtained from renewable resources.

[0246] "Energy consumption pattern" indicates the user's energy consumption tendency based on past energy consumption data.

[0247] A "consumption prediction model" is a model for predicting future energy consumption based on energy consumption patterns.

[0248] "Supply and demand balance" refers to the balance between energy consumption and supply, and is necessary for efficient energy management.

[0249] A "supply plan" is an optimal energy supply plan formulated based on the balance of energy supply and demand.

[0250] "Emotion data" is data that indicates the emotional state of the user, obtained from their facial expressions and voice.

[0251] An "energy management plan" is a plan that takes into account the user's emotional state and optimizes energy consumption.

[0252] "Home and business premises" refers to buildings in which private residences and commercial activities are carried out.

[0253] This invention is an AI energy management system that effectively manages energy consumption and supply, and further incorporates an emotion engine that recognizes user emotions to improve user satisfaction. Specific embodiments for carrying out the invention will now be described.

[0254] First, the server collects energy consumption data in real time from terminals such as smart meters and IoT devices installed in homes and business facilities. The terminals measure energy consumption and send the data to the server. This allows the server to grasp the energy consumption status of each home or business facility.

[0255] The server then retrieves weather information such as temperature, wind speed, and solar irradiance through the weather data provider's API. This data is stored in an analytical database and used to forecast renewable energy supply.

[0256] The server predicts the supply of renewable energy based on past supply data and acquired weather information. The supply forecast data is integrated with current supply status data provided by energy suppliers to produce a comprehensive supply forecast.

[0257] The server analyzes each user's consumption patterns based on their past energy consumption data. Peak times and average consumption are identified from the past consumption data, and a consumption prediction model is generated based on this.

[0258] In the supply and demand balance simulation, the server integrates consumption and supply forecast data to create an optimal energy supply plan that prioritizes the use of renewable energy and makes up for any shortfall with supplies from energy suppliers.

[0259] As a specific example, the server records that household A's power consumption is 3kW at 8am, and obtains from local weather information that the sunlight illuminance is 800W / m². Based on this, and based on a prediction that the amount of solar power generation will increase at 10am, a plan is made to cover household A's energy supply with renewable energy.

[0260] In real-time adjustments, the server monitors the collected consumption and supply data in real time and takes immediate action if there is a discrepancy between the forecast and reality. For example, if it suddenly becomes cloudy at 1 p.m. and the amount of solar power generation decreases, the server will adjust the amount of power supplied by the energy supplier to increase it.

[0261] The system also includes an emotion engine that recognizes the user's emotions. The emotion engine reads emotions from the user's facial expressions and voice and sends the data to a server. The server uses the emotion data to create a power supply plan to avoid peak power consumption during times when the user is feeling stressed. It can also adjust notification content, for example, recommending power-saving mode when the user is relaxing.

[0262] As a specific example, if the emotion engine recognizes that a user is stressed while getting ready for work at 8 a.m., the server will notify the user and adjust the operation of home appliances to avoid peak power consumption in order to reduce stress.

[0263] This system will optimize energy consumption, reduce environmental impact, and increase user satisfaction.

[0264] Examples of prompts for generative AI models include:

[0265] "The household's electricity consumption is recorded as 3kW at 8am and the solar illuminance is recorded as 800W / m². Please create an energy supply plan based on this situation."

[0266] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0267] Step 1:

[0268] Data collection

[0269] The server collects energy consumption data in real time from smart meters and IoT devices installed in homes and businesses. The devices measure energy consumption and send the data to the server.

[0270] Input: Energy consumption data sent from the device

[0271] Output: Collected energy consumption data is stored in a database on the server.

[0272] Specifically, the device measures consumption data, for example, every minute, and sends the data to the server, which then stores the data in a database.

[0273] Step 2:

[0274] Obtaining weather data

[0275] The server retrieves weather information through the weather data provider's API, which includes temperature, wind speed, and sunlight intensity.

[0276] Input: Weather information provided by weather data provider's API

[0277] Output: The acquired weather information is saved in a database for analysis.

[0278] Specifically, the server calls the API every hour, for example, to obtain the latest weather data and store it in a database.

[0279] Step 3:

[0280] Renewable energy supply forecast

[0281] The server makes supply forecasts based on past renewable energy supply data and current weather information.

[0282] Input: Historical renewable energy supply data, current weather information

[0283] Output: Renewable energy supply forecast data

[0284] Specifically, the server uses machine learning algorithms to generate supply forecasts for the next 24 hours.

[0285] Step 4:

[0286] Analysis of electricity consumption patterns

[0287] The server analyzes each user's consumption patterns based on past energy consumption data.

[0288] Input: Historical energy consumption data for each user

[0289] Output: Consumption patterns per user

[0290] Specifically, the server analyzes data from the past year, for example, to identify peak consumption times and average consumption amounts.

[0291] Step 5:

[0292] Generate consumption forecast models

[0293] The server generates a consumption prediction model based on the consumption pattern.

[0294] Input: Consumption Pattern

[0295] Output: Consumption forecast model

[0296] Specifically, the server uses a machine learning algorithm to build a consumption prediction model.

[0297] Step 6:

[0298] Supply and demand balance simulation

[0299] The server integrates consumption forecasts and supply forecasts to simulate the supply and demand balance.

[0300] Input: Consumption forecast model, supply forecast data

[0301] Output: Optimal supply and demand balance plan

[0302] Specifically, the server applies a simulation algorithm to calculate the supply and demand balance for the next 24 hours.

[0303] Step 7:

[0304] Creating a Supply Plan

[0305] The server creates an optimal supply plan, prioritizing the use of renewable energy and supplementing any shortfalls with supplies from energy suppliers.

[0306] Input: Supply and demand balance plan

[0307] Output: Supply Plan

[0308] Specifically, the server creates an energy supply plan based on the simulation results.

[0309] Step 8:

[0310] Notification of supply plan

[0311] The server communicates the supply plan to homes and businesses.

[0312] Input: Supply Plan

[0313] Output: Notified supply plan

[0314] Specifically, the server sends notifications to the terminals, and the plan is reflected on devices in each home or business facility.

[0315] Step 9:

[0316] Real-time adjustments

[0317] The server monitors the data collected in real time and responds immediately if any sudden fluctuations or discrepancies occur.

[0318] Input: Energy consumption data and weather data collected in real time

[0319] Output: Regulated energy supply

[0320] Specifically, the server adjusts the supply based on the new data, increasing or decreasing the supply from the energy supplier as needed.

[0321] Step 10:

[0322] Acquiring and processing emotion data

[0323] The device acquires emotional data from the user's facial expressions and voice and sends it to the server.

[0324] Input: User's facial and voice data

[0325] Output: Emotion data

[0326] Specifically, the device's camera and microphone capture data in real time, analyze it using the emotion engine, and send the results to the server.

[0327] Step 11:

[0328] Creating an emotional energy plan

[0329] Based on the emotional data, the server creates an energy supply plan that suits the emotional state.

[0330] Input: Emotion data

[0331] Output: Coordinated energy supply plan

[0332] Specifically, the server schedules the user to avoid peak power consumption during times when they are feeling stressed, and sends notifications to switch to power-saving mode when they are relaxing.

[0333] Examples of Proomp statements:

[0334] "The household's electricity consumption is recorded as 3kW at 8am and the solar illuminance is recorded as 800W / m². Please create an energy supply plan based on this situation."

[0335] (Application example 2)

[0336] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0337] Conventional energy management systems are effective at simulating supply-demand balances and creating supply plans based on power consumption and weather data, but they are unable to optimize energy efficiency while taking into account the emotional state of workers. Furthermore, there is a need for systems that can further optimize power consumption in complex and rapidly changing environments such as factories. Therefore, there is a demand for more advanced energy management systems that can reduce worker stress and improve work efficiency.

[0338] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting power consumption data from homes and businesses in real time, means for acquiring weather data, means for acquiring renewable energy supply forecast data, means for analyzing power consumption patterns based on the collected data, means for analyzing fluctuations in renewable energy supply due to weather changes, means for generating a consumption forecast model, means for integrating consumption forecasts and supply forecasts to simulate the supply and demand balance, means for creating an optimal supply plan, means for notifying homes and businesses of the supply plan, means for adjusting the supply amount, means for collecting worker emotion data, and means for adjusting the energy supply based on the emotion data. This optimizes power consumption, reduces worker stress, and enables efficient energy management.

[0339] "Home and business electricity consumption data" refers to data collected by measuring the amount of electricity consumed by homes and businesses in real time.

[0340] "Weather data" is information related to weather, including weather conditions such as temperature, wind speed, and sunlight illuminance.

[0341] "Renewable energy supply forecast data" is data that indicates the forecast supply of electricity generated from renewable energy resources such as solar and wind power.

[0342] "Power consumption patterns" are data that show usage trends, such as peak hours and average consumption, based on past power consumption data for a particular household or business.

[0343] "Weather fluctuations" refers to changes in weather conditions such as temperature, wind speed, and sunlight intensity over time.

[0344] A "consumption prediction model" is an algorithm or model for predicting future electricity consumption based on past consumption data.

[0345] A "supply and demand balance simulation" is a simulation that integrates predicted electricity consumption and supply to create an optimal energy supply plan.

[0346] A "supply plan" is a plan for efficiently supplying energy based on predicted demand and supply.

[0347] "Supply adjustment" refers to dynamically changing the supply amount in order to maintain the balance between supply and demand of electricity.

[0348] "Worker emotion data" refers to data relating to emotions acquired from the worker's facial expressions and voice, and includes stress and relaxation states.

[0349] "Adjusting energy supply based on emotional data" means adjusting the amount and method of energy supply in consideration of the emotional state of the worker.

[0350] This invention is a system that provides efficient energy management by using an application installed on a factory robot to effectively manage energy consumption and supply and also take into account the emotional state of the worker. Hereinafter, an embodiment of the invention will be described in detail.

[0351] First, to monitor the power consumption status within the factory in real time, the robots are equipped with smart meters and IoT devices. These devices collect power consumption data from the entire factory and send it to a server. The server then aggregates and stores this data in real time using cloud services such as Amazon Web Services (AWS) and Google Cloud Platform (GCP).

[0352] Next, the server obtains weather data from the internet via API. At this time, weather conditions (temperature, wind speed, sunlight illuminance, etc.) are obtained using Weather API, etc., and this data is stored in an analytical database. The weather data is used to forecast the supply of renewable energy.

[0353] The server uses past electricity consumption data and acquired weather data to predict renewable energy supply. The prediction also combines historical data and current supply status data, allowing for more accurate prediction of future supply.

[0354] The server then analyzes each robot's power consumption patterns based on past consumption data. This allows it to identify usage trends, such as peak times and average consumption, and generate a consumption prediction model. The generated consumption prediction model is then integrated with supply prediction data to simulate the supply and demand balance.

[0355] For example, if a particular robot consumes 5kW of power at 8am, the server records this value and retrieves from local weather data that the sunlight intensity is 1000W / m². Using this information, it predicts that the renewable energy supply will increase at 10am and plans to power the robot with renewable energy.

[0356] Furthermore, the server makes real-time adjustments. For example, if a sudden weather change reduces the supply of renewable energy, it can immediately adjust to increase the supply from the power company. It can also dynamically change the robot's movement patterns to avoid peak consumption.

[0357] This invention also includes an emotion engine. The emotion engine uses a camera and microphone attached to the robot to analyze the worker's facial expressions and voice to acquire emotion data. The acquired emotion data is sent to a server, which identifies the worker's stress and relaxation state. Based on this information, the energy supply is adjusted. For example, during times when the worker is feeling stressed, the supply plan is adjusted to avoid peak power consumption.

[0358] For example, if a worker arrives at work at 8 a.m. and the emotion engine detects the worker's stress, the server will use that information to create an energy supply plan to reduce stress. This reduces the burden on the worker and enables efficient energy management.

[0359] Prompt Sentence Examples

[0360] "Please monitor the energy consumption and supply in the factory in real time and generate an optimization model to manage it efficiently. Also, please create a demand forecast and supply plan that takes into account the emotional state of workers, and provide specific suggestions to avoid peak power consumption."

[0361] In this way, by combining an AI energy management system with an emotion engine, it is possible to optimize power consumption and improve work efficiency, while also reducing worker stress.

[0362] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0363] Step 1:

[0364] The server collects power consumption data in real time from smart meters and IoT devices in the factory. Smart meters and IoT devices are attached to robots and other machines and measure their power consumption. The data sent from these devices is collected by the server. The input is power consumption data, and the output is power consumption status data updated in real time.

[0365] Step 2:

[0366] The server obtains weather data through the weather data provider's API. Specifically, it collects weather information such as temperature, wind speed, and sunlight illuminance. This data is stored in an analysis database. The input is the weather data obtained from the API, and the output is the weather data stored in the analysis database.

[0367] Step 3:

[0368] The server predicts renewable energy supply based on past power consumption data and acquired weather data. It analyzes past supply data and uses an algorithm to predict future renewable energy supply based on weather conditions. The input is past power consumption data and weather data, and the output is future renewable energy supply forecast data.

[0369] Step 4:

[0370] The server generates a consumption prediction model based on past power consumption data. It analyzes the past consumption data and identifies consumption patterns using machine learning algorithms. The input is past consumption data, and the output is a model that predicts future power consumption.

[0371] Step 5:

[0372] The server integrates the consumption forecast model and supply forecast data to simulate the supply and demand balance. This simulation creates an optimal supply plan to balance the supply and consumption of renewable energy. The inputs are the consumption forecast model and supply forecast data, and the outputs are the simulation results of the supply and demand balance and a supply plan.

[0373] Step 6:

[0374] The server issues energy supply instructions to robots and related equipment based on the supply plan. Based on the supply plan, it adjusts the power supply to maximize the use of renewable energy. Specifically, it increases the supply from the power company as needed and recommends the use of renewable energy. The input is the supply plan, and the output is the power supply instructions within the factory.

[0375] Step 7:

[0376] The system monitors data collected in real time and responds immediately if there is a discrepancy between the forecast and reality. For example, if a sudden change in weather causes a decrease in the supply of renewable energy, the server immediately increases the supply from the power company. The input is power consumption data and weather data collected in real time, and the output is the adjusted supply.

[0377] Step 8:

[0378] The server collects the worker's emotional data through the camera and microphone installed on the robot. The emotion engine analyzes facial expressions and voice to detect the worker's stress or relaxation state. The input is the worker's facial expression and voice data, and the output is emotional data.

[0379] Step 9:

[0380] The server adjusts energy consumption based on the acquired emotional data. For example, if a worker is feeling stressed, it readjusts the supply plan to avoid peak power consumption. This reduces worker stress and achieves efficient energy management. The input is emotional data, and the output is the adjusted energy supply plan.

[0381] Through the above steps, the present invention provides an optimal energy management system that effectively manages energy consumption and supply within a factory and also takes into account the emotional state of workers.

[0382] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0383] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0384] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0385] [Second embodiment]

[0386] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0387] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0388] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0389] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0390] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0391] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0392] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0393] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0394] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0395] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0396] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0397] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0398] This invention relates to an AI energy management system for efficiently managing energy consumption and supply, and specific embodiments thereof will be described below.

[0399] First, let's look at the part that collects electricity consumption data from homes and businesses in real time. The server periodically collects electricity consumption data from terminals (e.g., smart meters and IoT devices). The terminals are installed in homes and businesses, measure electricity consumption in real time, and send that data to the server. This allows the server to accurately grasp each user's consumption status.

[0400] Next, regarding weather data, the server obtains various weather information from the weather data provider's API and stores it in an analytical database. This weather data includes temperature, wind speed, solar irradiance, etc., and is used to forecast the supply of renewable energy.

[0401] Regarding renewable energy supply forecasts, the server makes supply forecasts for renewable energy (e.g., solar power, wind power) based on past supply data and weather data. This supply forecast data is combined with current supply status data provided by the power company to produce a comprehensive supply forecast.

[0402] In the consumption pattern analysis, the server analyzes each user's electricity consumption pattern based on past consumption data. For example, household consumption patterns reveal peak times and average consumption. The results of this analysis are used to create a future consumption prediction model.

[0403] By integrating consumption and supply forecasts and simulating the supply and demand balance, the server creates an optimal energy supply plan based on this forecast data. This plan prioritizes the use of renewable energy and makes up for any shortfall with supplies from the power company.

[0404] As a specific example, suppose the server records that household A's power consumption is 3kW at 8am, and obtains from local weather data that the sunlight illuminance is 800W / m².Then, it predicts that the amount of solar power generation will increase at 10am, and a policy is made to cover household A's energy supply with renewable energy.

[0405] For real-time adjustments, the server monitors consumption and supply data in real time and takes immediate action if there is a discrepancy between the forecast and reality. For example, if it suddenly becomes cloudy at 1 p.m. and the amount of solar power generation decreases, the server will adjust the amount of supply from the power company to increase, preventing a power shortage. Terminals can also dynamically change their consumption patterns according to instructions from the server.

[0406] Users can check real-time power consumption and supply status through a dedicated app, and can also turn power saving mode on or off, and set alerts to avoid peak power usage during specific times.

[0407] This system will optimize energy consumption and reduce environmental impact. The present invention will play an important role in effectively utilizing renewable energy and improving energy efficiency.

[0408] The processing flow will be explained below.

[0409] Step 1:

[0410] The server collects power consumption data from devices installed in homes and businesses. The devices measure real-time power consumption at predetermined intervals (e.g., every minute) and send the data to the server.

[0411] Step 2:

[0412] The server retrieves weather data from weather data providers via API, including temperature, wind speed, and sunlight intensity, and stores this data in a database for analysis.

[0413] Step 3:

[0414] The server predicts the supply of renewable energy based on past supply data and acquired weather data. The supply forecast data is integrated with current supply status data provided by the power company.

[0415] Step 4:

[0416] The server analyzes historical consumption data to extract power consumption patterns for each home or business. This analysis identifies peak hours and average consumption and generates a consumption forecasting model.

[0417] Step 5:

[0418] The server integrates consumption and supply forecasts to simulate the future balance of supply and demand, and then uses this forecast data to create an optimal energy supply plan.

[0419] Step 6:

[0420] The server notifies the terminal of the supply plan and instructs it to make necessary adjustments. The terminal then adjusts its power usage in real time according to the instructions from the server.

[0421] Step 7:

[0422] A supply plan is created so that renewable energy is used first and any shortfall is made up by supply from the power company. The server sends instructions to the power company to implement this plan.

[0423] Step 8:

[0424] The server monitors consumption and supply data collected in real time and responds immediately if it detects an abnormality, such as a sudden change in weather or a sudden increase in power usage by users.

[0425] Step 9:

[0426] The server notifies the user in real time, and the user can check the current power consumption and supply status through a dedicated app and change the settings as needed.

[0427] Step 10:

[0428] The server regularly updates consumption forecasts and supply plans to continuously optimize energy efficiency, resulting in more efficient energy use and a reduced environmental impact.

[0429] Example 1

[0430] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0431] As renewable energy becomes more widespread, there is a need to efficiently manage the balance between energy supply and consumption. However, due to weather fluctuations and sudden changes in consumption, it is difficult to predict the supply of renewable energy, making it difficult to optimize energy supply plans. In addition, there is a lack of means for users to grasp consumption and supply conditions in real time. It is necessary to solve these issues and achieve improved energy efficiency and reduced environmental impact.

[0432] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0433] In this invention, the server includes means for collecting power consumption data in real time, means for acquiring weather information, means for collecting renewable energy supply forecast data, means for analyzing power consumption patterns based on the collected data, means for analyzing fluctuations in renewable energy supply according to weather changes, means for generating a consumption forecast model, means for simulating the supply and demand balance by integrating the consumption forecast and the supply forecast, means for creating an optimal energy supply plan, means for providing the energy supply plan to users, means for adjusting the supply amount, and means for monitoring the energy consumption and supply status in real time, thereby enabling efficient management of the balance between energy supply and consumption.

[0434] "Power consumption data" is data that indicates the amount of power consumed in a home or business over a certain period of time.

[0435] "Weather information" is data relating to weather conditions, including temperature, wind speed, sunlight illuminance, and the like.

[0436] "Renewable energy supply forecast data" is data used to predict the supply of renewable energy, such as solar power generation and wind power generation.

[0437] The term "electricity consumption pattern" refers to the tendency or characteristics of electricity consumption during a specific period, analyzed based on past electricity consumption data.

[0438] "Variations in renewable energy supply" means changes in the supply of renewable energy caused by weather or other external factors.

[0439] A "consumption forecasting model" is a mathematical or computational model for predicting future electricity consumption based on past consumption data, etc.

[0440] "Supply and demand balance simulation" is a method for virtually verifying the balance between energy supply and consumption based on electricity consumption forecast data and supply forecast data.

[0441] The "energy supply plan" is a plan designed to optimize the energy supply required by each user, prioritizing the use of renewable energy and supplementing any shortfall with external power supplies.

[0442] The "means for providing an energy supply plan to a user" refers to a method or system for notifying or displaying an optimized energy supply plan to a user.

[0443] "Means for adjusting supply amount" refers to a system or method for increasing or decreasing the external power supply when the supply of renewable energy is insufficient.

[0444] "Means for monitoring energy consumption and supply status in real time" refers to a method or system that instantly tracks the current status of power consumption and supply and takes appropriate action in response to fluctuations.

[0445] This invention relates to an AI energy management system for efficiently managing energy consumption and supply. Specific embodiments are described below.

[0446] 1. System Overview

[0447] This system consists of a server, terminals, and users. The server collects and analyzes energy consumption data and weather data, and predicts renewable energy supply. The terminals measure power consumption data and send it to the server. Users can check real-time power consumption and supply status through a dedicated app.

[0448] 2. Hardware and software used

[0449] Server: Performs data collection, analysis, prediction, simulation, and adjustment. In particular, it utilizes cloud platforms such as AWS (Amazon Web Services) and Microsoft Azure.

[0450] Devices: Smart meters, IoT devices (e.g., SmartThings, Nest Thermostat).

[0451] Weather data APIs: OpenWeatherMap, Weatherstack.

[0452] Analytics databases: Amazon RDS, MySQL, PostgreSQL.

[0453] 3. System Operation

[0454] Server Operation

[0455] The server collects real-time electricity consumption data from homes and businesses. To this end, the server periodically collects electricity consumption data from terminals (e.g., smart meters and IoT devices). This allows the server to accurately understand the consumption status of each user.

[0456] Next, the server periodically retrieves weather data from weather data providers' APIs (e.g., OpenWeatherMap, Weatherstack) and stores it in a database for analysis. The retrieved weather data includes temperature, wind speed, and sunlight irradiance.

[0457] The server also generates forecasts for renewable energy sources (e.g., solar and wind power) based on historical supply and weather data. This forecast data is combined with current supply data provided by the power company to produce a comprehensive forecast.

[0458] Device behavior

[0459] The device is installed at the site (home or business) and measures power consumption in real time. The measured data is then sent to the server. For example, the device detects that home A's power consumption is 3kW at 8:00 AM and sends that data to the server.

[0460] User operations

[0461] Users can check real-time power consumption and supply status through the dedicated app. They can also turn power saving mode on or off and set alerts to avoid peak power usage during specific times. For example, if a user opens the app at 9 a.m., the current power consumption, supply, and weather information will be displayed.

[0462] 4. Example of a system

[0463] As a specific example, suppose the server records that the power consumption of household A is 3kW at 8am and obtains that the sunlight illuminance in the area is 800W / m².Then, it predicts that the amount of solar power generation will increase at 10am, and a policy is made to cover household A's energy supply with renewable energy.

[0464] If the weather suddenly becomes cloudy at 1 p.m. and the amount of solar power generation decreases, the server will adjust the amount of power supplied by the power company to increase, preventing a power shortage. The device can also dynamically change its consumption pattern according to instructions from the server.

[0465] Prompt Sentence Examples

[0466] "Record the electricity consumption of household A from 8:00 AM onwards, obtain weather data (sunlight illuminance) for the area, and predict the solar power generation supply for 10:00 AM. Also, explain how users can check the real-time situation through a dedicated app."

[0467] This system will optimize energy consumption and reduce environmental impact. The present invention will play an important role in effectively utilizing renewable energy and improving energy efficiency.

[0468] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0469] Step 1:

[0470] Subject: Device

[0471] Terminals (smart meters and IoT devices) measure the electricity consumption data of homes and businesses in real time. The measured data is sent to a server every few minutes. For example, at 8:00 AM, the electricity consumption of home A is measured to be 3 kW, and this data is sent to the server.

[0472] Input: Current electricity consumption for your home or business

[0473] Output: Power consumption data sent to the server

[0474] Step 2:

[0475] Subject: Server

[0476] The server periodically obtains weather information from weather data providers' APIs (e.g., OpenWeatherMap, Weatherstack) and stores it in a database for analysis. The obtained weather data includes temperature, wind speed, and sunlight irradiance. For example, it obtains that the sunlight irradiance in the area is 800W / m² at 8:00 AM and stores this data in the database.

[0477] Input: Weather data from a weather data provider

[0478] Output: Weather data stored in a database for analysis

[0479] Step 3:

[0480] Subject: Server

[0481] The server analyzes past consumption data to identify each user's consumption pattern. The analysis results, including peak hours and average consumption, are used to create a consumption prediction model. For example, the evening peak hours can be identified from the consumption pattern of household A.

[0482] Input: Historical electricity consumption data

[0483] Output: Identified consumption pattern data

[0484] Step 4:

[0485] Subject: Server

[0486] The server predicts the supply of renewable energy (e.g., solar power, wind power) based on past supply data and acquired weather data. For example, it predicts that the supply of solar power will increase at 10:00 a.m.

[0487] Input: Historical renewable energy supply data and weather data

[0488] Output: Forecasted renewable energy supply data

[0489] Step 5:

[0490] Subject: Server

[0491] The server integrates the consumption forecast data and supply forecast data to simulate the supply and demand balance. This simulation creates an optimal energy supply plan. For example, it simulates both the case where all of the energy consumed by household A is covered by solar power generation, and the case where the shortfall is supplemented by the power company.

[0492] Input: Consumption forecast data and supply forecast data

[0493] Output: Simulated supply and demand balance data

[0494] Step 6:

[0495] Subject: Server

[0496] The server creates an optimal energy supply plan and notifies households and businesses of that plan. For example, at 10:00 AM, household A will be informed of its plan to run solely on renewable energy sources.

[0497] Input: Simulated supply and demand balance data

[0498] Output: Energy supply plan provided to user

[0499] Step 7:

[0500] Subject: Server

[0501] The server monitors consumption and supply data in real time and responds immediately if there is a discrepancy between the forecast and the actual situation. For example, if it suddenly becomes cloudy at 1 p.m. and the amount of solar power generation decreases, the server will adjust the amount of power supplied by the power company to increase it.

[0502] Input: Real-time consumption and supply data

[0503] Output: Adjusted supply data

[0504] Step 8:

[0505] Subject: User

[0506] Users can check real-time electricity consumption and supply status through a dedicated app. They can also turn power-saving mode on or off and set alerts to avoid peak power usage during specific times. For example, if a user opens the app at 9 a.m., the app will display the consumption and supply status up to that point, as well as weather information.

[0507] Input: Real-time electricity consumption and supply data

[0508] Output: Consumption and supply information displayed to the user

[0509] (Application example 1)

[0510] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0511] Modern factories operate a wide variety of equipment, making energy consumption optimization a key issue. Balancing renewable energy supply and consumption is particularly difficult, making inefficient energy use more likely. There is also a need for immediate responses to sudden fluctuations in power consumption, as well as real-time data monitoring and notification. Solving these issues and maximizing energy efficiency while reducing environmental impact are essential.

[0512] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0513] In this invention, the server includes means for collecting electricity consumption data for homes and businesses in real time, means for acquiring weather data, means for acquiring renewable energy supply forecast data, means for analyzing electricity consumption patterns based on the collected data, means for analyzing fluctuations in renewable energy supply due to weather changes, means for generating a consumption forecast model, means for integrating consumption forecasts and supply forecasts to simulate the supply and demand balance, means for creating an optimal supply plan, means for notifying homes and businesses of the supply plan, means for adjusting the supply amount, and means for acquiring energy consumption data for each device in a factory in real time and optimizing the operating status of the device based on the renewable energy supply amount, thereby balancing the supply and demand of renewable energy, maximizing energy efficiency, and reducing environmental impact.

[0514] "Means for collecting real-time electricity consumption data from homes and businesses" refers to devices and methods that measure electricity consumption in real time from smart meters and IoT devices installed in homes and businesses and transmit the data to a server.

[0515] "Means for acquiring weather data" refers to devices or methods for acquiring weather information such as temperature, wind speed, and sunlight illuminance from the weather data provider's API.

[0516] "Means for acquiring renewable energy supply forecast data" refers to a device or method for predicting the supply of renewable energy such as solar power generation and wind power generation based on past supply data and weather data, and acquiring that data.

[0517] The "means for analyzing power consumption patterns based on collected data" refers to a device or method for analyzing collected power consumption data and clarifying the consumption patterns of each user.

[0518] The "means for analyzing fluctuations in renewable energy supply due to weather fluctuations" refers to a device or method for analyzing changes in the amount of renewable energy supply due to fluctuations in weather conditions.

[0519] The "means for generating a consumption prediction model" refers to a device or method for generating a model that predicts future consumption based on past power consumption data.

[0520] The "means for integrating consumption forecast and supply forecast to simulate the supply and demand balance" refers to a device or method for integrating consumption forecast data and supply forecast data to simulate the energy supply and demand balance.

[0521] The "means for creating an optimal supply plan" refers to a device or method for formulating an optimal energy supply plan based on the results of the simulation.

[0522] A "means for communicating a supply plan to a home or business" is a device or method that notifies a user of the energy supply plan that has been created.

[0523] The "means for adjusting the supply amount" refers to a device or method for appropriately adjusting the supply amount in accordance with the actual energy consumption situation.

[0524] "Means for obtaining energy consumption data for each piece of equipment in a factory in real time" refers to a device or method for measuring the energy consumption of each piece of equipment installed in a factory in real time and collecting the data.

[0525] "Means for optimizing the operating status of equipment based on the amount of renewable energy supply" refers to devices and methods that optimize the operating status of equipment within a factory in accordance with the predicted amount of renewable energy supply, and operate it efficiently.

[0526] This invention relates to an AI energy management system for efficiently managing energy consumption and supply, and specific embodiments thereof are described below.

[0527] First, the server collects real-time electricity consumption data from homes and businesses. This is achieved by installing smart meters and IoT devices in homes and businesses. These devices measure electricity consumption in real time and send that data to the server. The server can then accurately track each user's consumption.

[0528] The server then retrieves weather data from the weather data provider's API, including temperature, wind speed, and solar irradiance, and stores the weather data in an analytical database for use in forecasting renewable energy supply.

[0529] When forecasting renewable energy supplies, the server uses past supply data and weather data to predict the supply of solar and wind power. This supply forecast data is combined with current supply status data provided by the power company to produce a comprehensive supply forecast.

[0530] In analyzing consumption patterns, the server analyzes each user's electricity consumption patterns based on past consumption data. For example, peak times and average consumption amounts can be determined from household consumption patterns. The results of this analysis are used to create a model for predicting future consumption.

[0531] The server integrates consumption and supply forecasts to simulate the supply-demand balance and creates an optimal energy supply plan based on these forecast data. This plan prioritizes the use of renewable energy and supplements shortfalls with supplies from the power company.

[0532] As a specific example, suppose the server records that household A's power consumption is 3kW at 8am, and obtains from local weather data that the sunlight illuminance is 800W / m².Then, it predicts that the amount of solar power generation will increase at 10am, and a policy is made to cover household A's energy supply with renewable energy.

[0533] In addition, the server collects energy consumption data for each piece of equipment in the factory in real time and optimizes the equipment's operating status based on the amount of renewable energy supplied. For example, the server uses energy consumption data and supply forecast data to optimize the operating status of equipment operating in the factory in real time.

[0534] If there is a discrepancy between the forecast and reality, the server responds immediately. For example, if it suddenly becomes cloudy at 1 p.m. and the amount of solar power generation decreases, the server will adjust the amount of power supplied by the power company to increase, preventing a power shortage. At the same time, it will notify the user in real time as appropriate.

[0535] Users can check real-time power consumption and supply status through a dedicated app, and can also turn power saving mode on or off and set alerts to avoid peak power usage during specific times.

[0536] In this way, the system optimizes energy consumption and contributes to reducing environmental impact.The present invention plays an important role because it can effectively utilize renewable energy and improve energy efficiency.

[0537] An example of a specific prompt is, "Using the following dataset, build a predictive model for a factory's energy consumption and renewable energy supply. Then, design an optimization algorithm that operates equipment when supply exceeds consumption and shuts it down when supply does not."

[0538] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0539] Step 1:

[0540] The server collects real-time electricity consumption data from homes and businesses. This is done using smart meters and IoT devices, with each device measuring its electricity consumption and sending the data to the server. The input of this data is the real-time measurement value from each device, and the output is consumption data accumulated on the server.

[0541] Step 2:

[0542] The server retrieves weather data from the weather data provider's API. To do this, it makes an API request to obtain weather information such as temperature, wind speed, and sunlight illuminance as input data, which it then stores in a database for analysis. The output is the retrieved weather data.

[0543] Step 3:

[0544] The server predicts the supply of renewable energy based on past supply data and weather data. For example, it predicts future power generation based on past solar power generation data and current weather data. The input data are past supply data and current weather data, and the output is supply forecast data.

[0545] Step 4:

[0546] The server analyzes each user's consumption pattern based on the collected power consumption data. This process involves analyzing the consumption data of each household or business over time to determine peak hours, average consumption, etc. The input data is past consumption data, and the output is the analysis result of the consumption pattern.

[0547] Step 5:

[0548] The server integrates the analyzed consumption patterns with the supply forecast data and simulates the supply and demand balance. For example, it calculates the supply and consumption balance based on the predicted power generation amount and the consumption amount of each user. The input data are the analysis results of consumption patterns and the supply forecast data, and the output is the simulation result of the supply and demand balance.

[0549] Step 6:

[0550] The server creates an optimal energy supply plan based on the simulation results. This process maximizes the use of renewable energy and, as necessary, creates a plan that supplements supply from the power company. The input data are the simulation results of the supply and demand balance, and the output is the optimal supply plan.

[0551] Step 7:

[0552] The server then communicates the supply plan to homes and businesses, informing them of the optimal energy supply plan through a dedicated app or notification system. The input data is the optimal supply plan, and the output is the notification to the user.

[0553] Step 8:

[0554] Based on instructions from the server, the terminal obtains energy consumption data for each piece of equipment in the factory in real time and optimizes the equipment's operating status based on the amount of renewable energy supplied. For example, it determines which equipment can operate based on supply forecasts. The input data are real-time consumption data and supply forecast data, and the output is the optimized operating status of the equipment.

[0555] Step 9:

[0556] The server responds immediately if there is a discrepancy between the prediction and reality. For example, if a sudden change in weather causes a decrease in solar power generation supply, the server adjusts to increase supply from the power company. The input data are current consumption data and supply data, and the output is the adjusted supply amount.

[0557] Step 10:

[0558] Users can check real-time power consumption and supply status through a dedicated app. The app provides users with various operations such as turning power saving mode on and off and setting alerts for power usage during specific time periods. The input data is real-time consumption and supply status, and the output is the result of user operations.

[0559] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0560] This invention is an AI energy management system for effectively managing energy consumption and supply, and further improves user satisfaction by incorporating an emotion engine that recognizes user emotions. Specific embodiments are described below.

[0561] First, we will explain the part that collects electricity consumption data from homes and businesses in real time. The server periodically collects electricity consumption data from devices (e.g., smart meters and IoT devices installed in homes and businesses). The devices measure this electricity consumption data and send it to the server in real time. This allows the server to understand the consumption status of each user.

[0562] Next, we will explain weather data. The server obtains weather data (e.g., temperature, wind speed, and sunlight irradiance) from weather data providers via API and stores this data in an analytical database. The weather data is used to forecast the supply of renewable energy.

[0563] Regarding renewable energy supply forecasts, the server makes predictions based on past supply data and acquired weather data. This supply forecast data is then integrated with current supply status data provided by the power company to produce a comprehensive supply forecast.

[0564] In the consumption pattern analysis, the server analyzes each user's electricity consumption pattern based on past consumption data. Peak times and average consumption are identified from the past consumption data, and a consumption prediction model is generated based on this.

[0565] By integrating consumption and supply forecasts and simulating the supply and demand balance, the server creates an optimal energy supply plan based on this forecast data.The supply plan prioritizes the use of renewable energy and makes up for any shortfall with supplies from the power company.

[0566] As a specific example, the server records that household A's power consumption is 3kW at 8am, and obtains from local weather data that the sunlight illuminance is 800W / m². Based on this, and based on a prediction that the amount of solar power generation will increase at 10am, a plan is made to cover household A's energy supply with renewable energy.

[0567] For real-time adjustments, the server monitors the collected consumption and supply data in real time and takes immediate action if there is a discrepancy between the forecast and reality. For example, if it suddenly becomes cloudy at 1 p.m. and the amount of solar power generation decreases, the server will adjust the amount of power supplied by the power company to increase. In addition, terminals can dynamically change the consumption patterns of home appliances and other devices according to instructions from the server.

[0568] The system also includes an emotion engine that recognizes the user's emotions. This emotion engine reads emotions from the user's facial expressions and voice and sends the data to a server. The server then uses the emotion data to create a power supply plan to avoid peak power consumption during times when the user is feeling stressed. It can also adjust the content of notifications, for example, recommending power-saving mode when the user is relaxing.

[0569] As a specific example, if the emotion engine recognizes that a user is stressed while getting ready for work at 8 a.m., the server will notify the user and adjust the operation of home appliances to avoid peak power consumption in order to reduce stress.

[0570] In this way, combining the emotion engine enables energy management according to the user's emotional state, reducing stress and achieving efficient energy use. This system optimizes energy consumption, reduces environmental impact, and improves user satisfaction.

[0571] The processing flow will be explained below.

[0572] Step 1:

[0573] The server collects power consumption data from devices installed in homes and businesses. The devices measure real-time power consumption at predetermined intervals (e.g., every minute) and send the data to the server.

[0574] Step 2:

[0575] The server retrieves weather data from weather data providers via API, including temperature, wind speed, and sunlight intensity, and stores this data in a database for analysis.

[0576] Step 3:

[0577] The server predicts the supply of renewable energy based on past supply data and acquired weather data. The supply forecast data is integrated with current supply status data provided by the power company.

[0578] Step 4:

[0579] The server analyzes historical consumption data to extract power consumption patterns for each home or business. This analysis identifies peak hours and average consumption and generates a consumption forecasting model.

[0580] Step 5:

[0581] The server integrates consumption and supply forecasts to simulate the future balance of supply and demand, and then uses this forecast data to create an optimal energy supply plan.

[0582] Step 6:

[0583] The server notifies the terminal of the supply plan and instructs it to make necessary adjustments. The terminal then adjusts its power usage in real time according to the instructions from the server.

[0584] Step 7:

[0585] A supply plan is created so that renewable energy is used first and any shortfall is made up by supply from the power company. The server sends instructions to the power company to implement this plan.

[0586] Step 8:

[0587] The server monitors consumption and supply data collected in real time and responds immediately if it detects an abnormality, such as a sudden change in weather or a sudden increase in power usage by users.

[0588] Step 9:

[0589] The server receives the user's emotional data through the emotion engine, which analyzes the user's facial expressions and voice and transmits the user's emotional state to the server.

[0590] Step 10:

[0591] The server optimizes power consumption and adjusts notifications based on the user's emotional data. For example, if the user is feeling stressed, it will adjust the notification to avoid peak power consumption.

[0592] Step 11:

[0593] The server notifies the user in real time, and the user can check the current power consumption and supply status through a dedicated app and change the settings as needed.

[0594] Step 12:

[0595] The server regularly updates consumption forecasts and supply plans to continuously optimize energy efficiency, resulting in more efficient energy use and a reduced environmental impact.

[0596] Example 2

[0597] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0598] Conventional energy management systems have difficulty efficiently managing the balance between power consumption and supply, and have had particular issues with being unable to respond to sudden fluctuations in consumption or weather conditions. Furthermore, energy management does not take into account the emotional state of the user, making it difficult to contribute to improving user satisfaction or reducing stress.

[0599] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0600] In this invention, the server includes means for collecting energy consumption data from homes and business facilities in real time, means for acquiring weather information, means for acquiring supply forecast data for renewable resources, means for analyzing energy consumption patterns based on the collected data, means for analyzing fluctuations in renewable energy supply due to weather changes, means for generating a consumption forecast model, means for integrating consumption forecasts and supply forecasts to simulate the supply and demand balance, means for creating an optimal supply plan, means for notifying the home and business facilities of the supply plan, means for adjusting the supply amount, means for acquiring user emotion data, and means for creating an energy management plan based on the user emotion, thereby enabling efficient energy management and improving user satisfaction.

[0601] "Energy consumption data" refers to data that indicates the amount of energy consumed in a home or business facility.

[0602] "Weather information" is data indicating weather conditions such as temperature, wind speed, and sunlight illuminance.

[0603] "Renewable resources" refers to renewable natural resources such as solar, wind, and hydropower, and provide energy derived from these.

[0604] "Supply forecast data" is data that forecasts the amount of energy supply that can be obtained from renewable resources.

[0605] "Energy consumption pattern" indicates the user's energy consumption tendency based on past energy consumption data.

[0606] A "consumption prediction model" is a model for predicting future energy consumption based on energy consumption patterns.

[0607] "Supply and demand balance" refers to the balance between energy consumption and supply, and is necessary for efficient energy management.

[0608] A "supply plan" is an optimal energy supply plan formulated based on the balance of energy supply and demand.

[0609] "Emotion data" is data that indicates the emotional state of the user, obtained from their facial expressions and voice.

[0610] An "energy management plan" is a plan that takes into account the user's emotional state and optimizes energy consumption.

[0611] "Home and business premises" refers to buildings in which private residences and commercial activities are carried out.

[0612] This invention is an AI energy management system that effectively manages energy consumption and supply, and further incorporates an emotion engine that recognizes user emotions to improve user satisfaction. Specific embodiments for carrying out the invention will now be described.

[0613] First, the server collects energy consumption data in real time from terminals such as smart meters and IoT devices installed in homes and business facilities. The terminals measure energy consumption and send the data to the server. This allows the server to grasp the energy consumption status of each home or business facility.

[0614] The server then retrieves weather information such as temperature, wind speed, and solar irradiance through the weather data provider's API. This data is stored in an analytical database and used to forecast renewable energy supply.

[0615] The server predicts the supply of renewable energy based on past supply data and acquired weather information. The supply forecast data is integrated with current supply status data provided by energy suppliers to produce a comprehensive supply forecast.

[0616] The server analyzes each user's consumption patterns based on their past energy consumption data. Peak times and average consumption are identified from the past consumption data, and a consumption prediction model is generated based on this.

[0617] In the supply and demand balance simulation, the server integrates consumption and supply forecast data to create an optimal energy supply plan that prioritizes the use of renewable energy and makes up for any shortfall with supplies from energy suppliers.

[0618] As a specific example, the server records that household A's power consumption is 3kW at 8am, and obtains from local weather information that the sunlight illuminance is 800W / m². Based on this, and based on a prediction that the amount of solar power generation will increase at 10am, a plan is made to cover household A's energy supply with renewable energy.

[0619] In real-time adjustments, the server monitors the collected consumption and supply data in real time and takes immediate action if there is a discrepancy between the forecast and reality. For example, if it suddenly becomes cloudy at 1 p.m. and the amount of solar power generation decreases, the server will adjust the amount of power supplied by the energy supplier to increase it.

[0620] The system also includes an emotion engine that recognizes the user's emotions. The emotion engine reads emotions from the user's facial expressions and voice and sends the data to a server. The server uses the emotion data to create a power supply plan to avoid peak power consumption during times when the user is feeling stressed. It can also adjust the content of notifications, for example, recommending power-saving mode when the user is relaxing.

[0621] As a specific example, if the emotion engine recognizes that a user is stressed while getting ready for work at 8 a.m., the server will notify the user and adjust the operation of home appliances to avoid peak power consumption in order to reduce stress.

[0622] This system will optimize energy consumption, reduce environmental impact, and increase user satisfaction.

[0623] Examples of prompts for generative AI models include:

[0624] "The household's electricity consumption is recorded as 3kW at 8am and the solar illuminance is recorded as 800W / m². Please create an energy supply plan based on this situation."

[0625] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0626] Step 1:

[0627] Data collection

[0628] The server collects energy consumption data in real time from smart meters and IoT devices installed in homes and businesses. The devices measure energy consumption and send the data to the server.

[0629] Input: Energy consumption data sent from the device

[0630] Output: Collected energy consumption data is stored in a database on the server.

[0631] Specifically, the device measures consumption data, for example, every minute, and sends the data to the server, which then stores the data in a database.

[0632] Step 2:

[0633] Obtaining weather data

[0634] The server retrieves weather information through the weather data provider's API, which includes temperature, wind speed, and sunlight intensity.

[0635] Input: Weather information provided by weather data provider's API

[0636] Output: The acquired weather information is saved in a database for analysis.

[0637] Specifically, the server calls the API every hour, for example, to obtain the latest weather data and store it in a database.

[0638] Step 3:

[0639] Renewable energy supply forecast

[0640] The server makes supply forecasts based on past renewable energy supply data and current weather information.

[0641] Input: Historical renewable energy supply data, current weather information

[0642] Output: Renewable energy supply forecast data

[0643] Specifically, the server uses machine learning algorithms to generate supply forecasts for the next 24 hours.

[0644] Step 4:

[0645] Analysis of electricity consumption patterns

[0646] The server analyzes each user's consumption patterns based on past energy consumption data.

[0647] Input: Historical energy consumption data for each user

[0648] Output: Consumption patterns per user

[0649] Specifically, the server analyzes data from the past year, for example, to identify peak consumption times and average consumption amounts.

[0650] Step 5:

[0651] Generate consumption forecast models

[0652] The server generates a consumption prediction model based on the consumption pattern.

[0653] Input: Consumption Pattern

[0654] Output: Consumption forecast model

[0655] Specifically, the server uses a machine learning algorithm to build a consumption prediction model.

[0656] Step 6:

[0657] Supply and demand balance simulation

[0658] The server integrates consumption forecasts and supply forecasts to simulate the supply and demand balance.

[0659] Input: Consumption forecast model, supply forecast data

[0660] Output: Optimal supply and demand balance plan

[0661] Specifically, the server applies a simulation algorithm to calculate the supply and demand balance for the next 24 hours.

[0662] Step 7:

[0663] Creating a Supply Plan

[0664] The server creates an optimal supply plan, prioritizing the use of renewable energy and supplementing any shortfalls with supplies from energy suppliers.

[0665] Input: Supply and demand balance plan

[0666] Output: Supply Plan

[0667] Specifically, the server creates an energy supply plan based on the simulation results.

[0668] Step 8:

[0669] Notification of supply plan

[0670] The server communicates the supply plan to homes and businesses.

[0671] Input: Supply Plan

[0672] Output: Notified supply plan

[0673] Specifically, the server sends notifications to the terminals, and the plan is reflected on devices in each home or business facility.

[0674] Step 9:

[0675] Real-time adjustments

[0676] The server monitors the data collected in real time and responds immediately if any sudden fluctuations or discrepancies occur.

[0677] Input: Energy consumption data and weather data collected in real time

[0678] Output: Regulated energy supply

[0679] Specifically, the server adjusts the supply based on the new data, increasing or decreasing the supply from the energy supplier as needed.

[0680] Step 10:

[0681] Acquiring and processing emotion data

[0682] The device acquires emotional data from the user's facial expressions and voice and sends it to the server.

[0683] Input: User's facial and voice data

[0684] Output: Emotion data

[0685] Specifically, the device's camera and microphone capture data in real time, analyze it using the emotion engine, and send the results to the server.

[0686] Step 11:

[0687] Creating an emotional energy plan

[0688] Based on the emotional data, the server creates an energy supply plan that suits the emotional state.

[0689] Input: Emotion data

[0690] Output: Coordinated energy supply plan

[0691] Specifically, the server schedules the user to avoid peak power consumption during times when they are feeling stressed, and sends notifications to switch to power-saving mode when they are relaxing.

[0692] Examples of Proomp statements:

[0693] "The household's electricity consumption is recorded as 3kW at 8am and the solar illuminance is recorded as 800W / m². Please create an energy supply plan based on this situation."

[0694] (Application example 2)

[0695] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0696] Conventional energy management systems are effective at simulating supply-demand balances and creating supply plans based on power consumption and weather data, but they are unable to optimize energy efficiency while taking into account the emotional state of workers. Furthermore, there is a need for systems that can further optimize power consumption in complex and rapidly changing environments such as factories. Therefore, there is a demand for more advanced energy management systems that can reduce worker stress and improve work efficiency.

[0697] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting power consumption data from homes and businesses in real time, means for acquiring weather data, means for acquiring renewable energy supply forecast data, means for analyzing power consumption patterns based on the collected data, means for analyzing fluctuations in renewable energy supply due to weather changes, means for generating a consumption forecast model, means for integrating consumption forecasts and supply forecasts to simulate the supply and demand balance, means for creating an optimal supply plan, means for notifying homes and businesses of the supply plan, means for adjusting the supply amount, means for collecting worker emotion data, and means for adjusting the energy supply based on the emotion data. This optimizes power consumption, reduces worker stress, and enables efficient energy management.

[0698] "Home and business electricity consumption data" refers to data collected by measuring the amount of electricity consumed by homes and businesses in real time.

[0699] "Weather data" is information related to weather, including weather conditions such as temperature, wind speed, and sunlight illuminance.

[0700] "Renewable energy supply forecast data" is data that indicates the forecast supply of electricity generated from renewable energy resources such as solar and wind power.

[0701] "Power consumption patterns" are data that show usage trends, such as peak hours and average consumption, based on past power consumption data for a particular household or business.

[0702] "Weather fluctuations" refers to changes in weather conditions such as temperature, wind speed, and sunlight intensity over time.

[0703] A "consumption prediction model" is an algorithm or model for predicting future electricity consumption based on past consumption data.

[0704] A "supply and demand balance simulation" is a simulation that integrates predicted electricity consumption and supply to create an optimal energy supply plan.

[0705] A "supply plan" is a plan for efficiently supplying energy based on predicted demand and supply.

[0706] "Supply adjustment" refers to dynamically changing the supply amount in order to maintain the balance between supply and demand of electricity.

[0707] "Worker emotion data" refers to data relating to emotions acquired from the worker's facial expressions and voice, and includes stress and relaxation states.

[0708] "Adjusting energy supply based on emotional data" means adjusting the amount and method of energy supply in consideration of the emotional state of the worker.

[0709] This invention is a system that provides efficient energy management by using an application installed on a factory robot to effectively manage energy consumption and supply and also take into account the emotional state of the worker. Hereinafter, an embodiment of the invention will be described in detail.

[0710] First, to monitor the power consumption status within the factory in real time, the robots are equipped with smart meters and IoT devices. These devices collect power consumption data from the entire factory and send it to a server. The server then aggregates and stores this data in real time using cloud services such as Amazon Web Services (AWS) and Google Cloud Platform (GCP).

[0711] Next, the server obtains weather data from the internet via API. At this time, weather conditions (temperature, wind speed, sunlight illuminance, etc.) are obtained using Weather API, etc., and this data is stored in an analytical database. The weather data is used to forecast the supply of renewable energy.

[0712] The server uses past electricity consumption data and acquired weather data to predict renewable energy supply. The prediction also combines historical data and current supply status data, allowing for more accurate prediction of future supply.

[0713] The server then analyzes each robot's power consumption patterns based on past consumption data. This allows it to identify usage trends, such as peak times and average consumption, and generate a consumption prediction model. The generated consumption prediction model is then integrated with supply prediction data to simulate the supply and demand balance.

[0714] For example, if a particular robot consumes 5kW of power at 8am, the server records this value and retrieves from local weather data that the sunlight intensity is 1000W / m². Using this information, it predicts that the renewable energy supply will increase at 10am and plans to power the robot with renewable energy.

[0715] Furthermore, the server makes real-time adjustments. For example, if a sudden weather change reduces the supply of renewable energy, it can immediately adjust to increase the supply from the power company. It can also dynamically change the robot's movement patterns to avoid peak consumption.

[0716] This invention also includes an emotion engine. The emotion engine uses a camera and microphone attached to the robot to analyze the worker's facial expressions and voice to acquire emotion data. The acquired emotion data is sent to a server, which identifies the worker's stress and relaxation state. Based on this information, the energy supply is adjusted. For example, during times when the worker is feeling stressed, the supply plan is adjusted to avoid peak power consumption.

[0717] For example, if a worker arrives at work at 8 a.m. and the emotion engine detects the worker's stress, the server will use that information to create an energy supply plan to reduce stress. This reduces the burden on the worker and enables efficient energy management.

[0718] Prompt Sentence Examples

[0719] "Please monitor the energy consumption and supply in the factory in real time and generate an optimization model to manage it efficiently. Also, please create a demand forecast and supply plan that takes into account the emotional state of workers, and provide specific suggestions to avoid peak power consumption."

[0720] In this way, by combining an AI energy management system with an emotion engine, it is possible to optimize power consumption and improve work efficiency, while also reducing worker stress.

[0721] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0722] Step 1:

[0723] The server collects power consumption data in real time from smart meters and IoT devices in the factory. Smart meters and IoT devices are attached to robots and other machines and measure their power consumption. The data sent from these devices is collected by the server. The input is power consumption data, and the output is power consumption status data updated in real time.

[0724] Step 2:

[0725] The server obtains weather data through the weather data provider's API. Specifically, it collects weather information such as temperature, wind speed, and sunlight illuminance. This data is stored in an analysis database. The input is the weather data obtained from the API, and the output is the weather data stored in the analysis database.

[0726] Step 3:

[0727] The server predicts renewable energy supply based on past power consumption data and acquired weather data. It analyzes past supply data and uses an algorithm to predict future renewable energy supply based on weather conditions. The input is past power consumption data and weather data, and the output is future renewable energy supply forecast data.

[0728] Step 4:

[0729] The server generates a consumption prediction model based on past power consumption data. It analyzes the past consumption data and identifies consumption patterns using machine learning algorithms. The input is past consumption data, and the output is a model that predicts future power consumption.

[0730] Step 5:

[0731] The server integrates the consumption forecast model and supply forecast data to simulate the supply and demand balance. This simulation creates an optimal supply plan to balance the supply and consumption of renewable energy. The inputs are the consumption forecast model and supply forecast data, and the outputs are the simulation results of the supply and demand balance and a supply plan.

[0732] Step 6:

[0733] The server issues energy supply instructions to robots and related equipment based on the supply plan. Based on the supply plan, it adjusts the power supply to maximize the use of renewable energy. Specifically, it increases the supply from the power company as needed and recommends the use of renewable energy. The input is the supply plan, and the output is the power supply instructions within the factory.

[0734] Step 7:

[0735] The system monitors data collected in real time and responds immediately if there is a discrepancy between the forecast and reality. For example, if a sudden change in weather causes a decrease in the supply of renewable energy, the server immediately increases the supply from the power company. The input is power consumption data and weather data collected in real time, and the output is the adjusted supply.

[0736] Step 8:

[0737] The server collects the worker's emotional data through the camera and microphone installed on the robot. The emotion engine analyzes facial expressions and voice to detect the worker's stress or relaxation state. The input is the worker's facial expression and voice data, and the output is emotional data.

[0738] Step 9:

[0739] The server adjusts energy consumption based on the acquired emotional data. For example, if a worker is feeling stressed, it readjusts the supply plan to avoid peak power consumption. This reduces worker stress and achieves efficient energy management. The input is emotional data, and the output is the adjusted energy supply plan.

[0740] Through the above steps, the present invention provides an optimal energy management system that effectively manages energy consumption and supply within a factory and also takes into account the emotional state of workers.

[0741] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0742] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0743] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0744] [Third embodiment]

[0745] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0746] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0747] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0748] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0749] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0750] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0751] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0752] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0753] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0754] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0755] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0756] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0757] This invention relates to an AI energy management system for efficiently managing energy consumption and supply, and specific embodiments thereof will be described below.

[0758] First, let's look at the part that collects electricity consumption data from homes and businesses in real time. The server periodically collects electricity consumption data from terminals (e.g., smart meters and IoT devices). The terminals are installed in homes and businesses, measure electricity consumption in real time, and send that data to the server. This allows the server to accurately grasp each user's consumption status.

[0759] Next, regarding weather data, the server obtains various weather information from the weather data provider's API and stores it in an analytical database. This weather data includes temperature, wind speed, solar irradiance, etc., and is used to forecast the supply of renewable energy.

[0760] Regarding renewable energy supply forecasts, the server makes supply forecasts for renewable energy (e.g., solar power, wind power) based on past supply data and weather data. This supply forecast data is combined with current supply status data provided by the power company to produce a comprehensive supply forecast.

[0761] In the consumption pattern analysis, the server analyzes each user's electricity consumption pattern based on past consumption data. For example, household consumption patterns reveal peak times and average consumption. The results of this analysis are used to create a future consumption prediction model.

[0762] By integrating consumption and supply forecasts and simulating the supply and demand balance, the server creates an optimal energy supply plan based on this forecast data. This plan prioritizes the use of renewable energy and makes up for any shortfall with supplies from the power company.

[0763] As a specific example, suppose the server records that household A's power consumption is 3kW at 8am, and obtains from local weather data that the sunlight illuminance is 800W / m².Then, it predicts that the amount of solar power generation will increase at 10am, and a policy is made to cover household A's energy supply with renewable energy.

[0764] For real-time adjustments, the server monitors consumption and supply data in real time and takes immediate action if there is a discrepancy between the forecast and reality. For example, if it suddenly becomes cloudy at 1 p.m. and the amount of solar power generation decreases, the server will adjust the amount of supply from the power company to increase, preventing a power shortage. Terminals can also dynamically change their consumption patterns according to instructions from the server.

[0765] Users can check real-time power consumption and supply status through a dedicated app, and can also turn power saving mode on or off, and set alerts to avoid peak power usage during specific times.

[0766] This system will optimize energy consumption and reduce environmental impact. The present invention will play an important role in effectively utilizing renewable energy and improving energy efficiency.

[0767] The processing flow will be explained below.

[0768] Step 1:

[0769] The server collects power consumption data from devices installed in homes and businesses. The devices measure real-time power consumption at predetermined intervals (e.g., every minute) and send the data to the server.

[0770] Step 2:

[0771] The server retrieves weather data from weather data providers via API, including temperature, wind speed, and sunlight intensity, and stores this data in a database for analysis.

[0772] Step 3:

[0773] The server predicts the supply of renewable energy based on past supply data and acquired weather data. The supply forecast data is integrated with current supply status data provided by the power company.

[0774] Step 4:

[0775] The server analyzes historical consumption data to extract power consumption patterns for each home or business. This analysis identifies peak hours and average consumption and generates a consumption forecasting model.

[0776] Step 5:

[0777] The server integrates consumption and supply forecasts to simulate the future balance of supply and demand, and then uses this forecast data to create an optimal energy supply plan.

[0778] Step 6:

[0779] The server notifies the terminal of the supply plan and instructs it to make necessary adjustments. The terminal then adjusts its power usage in real time according to the instructions from the server.

[0780] Step 7:

[0781] A supply plan is created so that renewable energy is used first and any shortfall is made up by supply from the power company. The server sends instructions to the power company to implement this plan.

[0782] Step 8:

[0783] The server monitors consumption and supply data collected in real time and responds immediately if it detects an abnormality, such as a sudden change in weather or a sudden increase in power usage by users.

[0784] Step 9:

[0785] The server notifies the user in real time, and the user can check the current power consumption and supply status through a dedicated app and change the settings as needed.

[0786] Step 10:

[0787] The server regularly updates consumption forecasts and supply plans to continuously optimize energy efficiency, resulting in more efficient energy use and a reduced environmental impact.

[0788] Example 1

[0789] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0790] As renewable energy becomes more widespread, there is a need to efficiently manage the balance between energy supply and consumption. However, due to weather fluctuations and sudden changes in consumption, it is difficult to predict the supply of renewable energy, making it difficult to optimize energy supply plans. In addition, there is a lack of means for users to grasp consumption and supply conditions in real time. It is necessary to solve these issues and achieve improved energy efficiency and reduced environmental impact.

[0791] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0792] In this invention, the server includes means for collecting power consumption data in real time, means for acquiring weather information, means for collecting renewable energy supply forecast data, means for analyzing power consumption patterns based on the collected data, means for analyzing fluctuations in renewable energy supply according to weather changes, means for generating a consumption forecast model, means for simulating the supply and demand balance by integrating the consumption forecast and the supply forecast, means for creating an optimal energy supply plan, means for providing the energy supply plan to users, means for adjusting the supply amount, and means for monitoring the energy consumption and supply status in real time, thereby enabling efficient management of the balance between energy supply and consumption.

[0793] "Power consumption data" is data that indicates the amount of power consumed in a home or business over a certain period of time.

[0794] "Weather information" is data relating to weather conditions, including temperature, wind speed, sunlight illuminance, and the like.

[0795] "Renewable energy supply forecast data" is data used to predict the supply of renewable energy, such as solar power generation and wind power generation.

[0796] The term "electricity consumption pattern" refers to the tendency or characteristics of electricity consumption during a specific period, analyzed based on past electricity consumption data.

[0797] "Variations in renewable energy supply" means changes in the supply of renewable energy caused by weather or other external factors.

[0798] A "consumption forecasting model" is a mathematical or computational model for predicting future electricity consumption based on past consumption data, etc.

[0799] "Supply and demand balance simulation" is a method for virtually verifying the balance between energy supply and consumption based on electricity consumption forecast data and supply forecast data.

[0800] The "energy supply plan" is a plan designed to optimize the energy supply required by each user, prioritizing the use of renewable energy and supplementing any shortfall with external power supplies.

[0801] The "means for providing an energy supply plan to a user" refers to a method or system for notifying or displaying an optimized energy supply plan to a user.

[0802] "Means for adjusting supply amount" refers to a system or method for increasing or decreasing the external power supply when the supply of renewable energy is insufficient.

[0803] "Means for monitoring energy consumption and supply status in real time" refers to a method or system that instantly tracks the current status of power consumption and supply and takes appropriate action in response to fluctuations.

[0804] This invention relates to an AI energy management system for efficiently managing energy consumption and supply. Specific embodiments are described below.

[0805] 1. System Overview

[0806] This system consists of a server, terminals, and users. The server collects and analyzes energy consumption data and weather data, and predicts renewable energy supply. The terminals measure power consumption data and send it to the server. Users can check real-time power consumption and supply status through a dedicated app.

[0807] 2. Hardware and software used

[0808] Server: Performs data collection, analysis, prediction, simulation, and adjustment. In particular, it utilizes cloud platforms such as AWS (Amazon Web Services) and Microsoft Azure.

[0809] Devices: Smart meters, IoT devices (e.g., SmartThings, Nest Thermostat).

[0810] Weather data APIs: OpenWeatherMap, Weatherstack.

[0811] Analytics databases: Amazon RDS, MySQL, PostgreSQL.

[0812] 3. System Operation

[0813] Server Operation

[0814] The server collects real-time electricity consumption data from homes and businesses. To this end, the server periodically collects electricity consumption data from terminals (e.g., smart meters and IoT devices). This allows the server to accurately understand the consumption status of each user.

[0815] Next, the server periodically retrieves weather data from weather data providers' APIs (e.g., OpenWeatherMap, Weatherstack) and stores it in a database for analysis. The retrieved weather data includes temperature, wind speed, and sunlight irradiance.

[0816] The server also generates forecasts for renewable energy sources (e.g., solar and wind power) based on historical supply and weather data. This forecast data is combined with current supply data provided by the power company to produce a comprehensive forecast.

[0817] Device behavior

[0818] The device is installed at the site (home or business) and measures power consumption in real time. The measured data is then sent to the server. For example, the device detects that home A's power consumption is 3kW at 8:00 AM and sends that data to the server.

[0819] User operations

[0820] Users can check real-time power consumption and supply status through the dedicated app. They can also turn power saving mode on or off and set alerts to avoid peak power usage during specific times. For example, if a user opens the app at 9 a.m., the current power consumption, supply, and weather information will be displayed.

[0821] 4. Example of a system

[0822] As a specific example, suppose the server records that the power consumption of household A is 3kW at 8am and obtains that the sunlight illuminance in the area is 800W / m².Then, it predicts that the amount of solar power generation will increase at 10am, and a policy is made to cover household A's energy supply with renewable energy.

[0823] If the weather suddenly becomes cloudy at 1 p.m. and the amount of solar power generation decreases, the server will adjust the amount of power supplied by the power company to increase, preventing a power shortage. The device can also dynamically change its consumption pattern according to instructions from the server.

[0824] Prompt Sentence Examples

[0825] "Record the electricity consumption of household A from 8:00 AM onwards, obtain weather data (sunlight illuminance) for the area, and predict the solar power generation supply for 10:00 AM. Also, explain how users can check the real-time situation through a dedicated app."

[0826] This system will optimize energy consumption and reduce environmental impact. The present invention will play an important role in effectively utilizing renewable energy and improving energy efficiency.

[0827] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0828] Step 1:

[0829] Subject: Device

[0830] Terminals (smart meters and IoT devices) measure the electricity consumption data of homes and businesses in real time. The measured data is sent to a server every few minutes. For example, at 8:00 AM, the electricity consumption of home A is measured to be 3 kW, and this data is sent to the server.

[0831] Input: Current electricity consumption for your home or business

[0832] Output: Power consumption data sent to the server

[0833] Step 2:

[0834] Subject: Server

[0835] The server periodically obtains weather information from weather data providers' APIs (e.g., OpenWeatherMap, Weatherstack) and stores it in a database for analysis. The obtained weather data includes temperature, wind speed, and sunlight irradiance. For example, it obtains that the sunlight irradiance in the area is 800W / m² at 8:00 AM and stores this data in the database.

[0836] Input: Weather data from a weather data provider

[0837] Output: Weather data stored in a database for analysis

[0838] Step 3:

[0839] Subject: Server

[0840] The server analyzes past consumption data to identify each user's consumption pattern. The analysis results, including peak hours and average consumption, are used to create a consumption prediction model. For example, the evening peak hours can be identified from the consumption pattern of household A.

[0841] Input: Historical electricity consumption data

[0842] Output: Identified consumption pattern data

[0843] Step 4:

[0844] Subject: Server

[0845] The server predicts the supply of renewable energy (e.g., solar power, wind power) based on past supply data and acquired weather data. For example, it predicts that the supply of solar power will increase at 10:00 a.m.

[0846] Input: Historical renewable energy supply data and weather data

[0847] Output: Forecasted renewable energy supply data

[0848] Step 5:

[0849] Subject: Server

[0850] The server integrates the consumption forecast data and supply forecast data to simulate the supply and demand balance. This simulation creates an optimal energy supply plan. For example, it simulates both the case where all of the energy consumed by household A is covered by solar power generation, and the case where the shortfall is supplemented by the power company.

[0851] Input: Consumption forecast data and supply forecast data

[0852] Output: Simulated supply and demand balance data

[0853] Step 6:

[0854] Subject: Server

[0855] The server creates an optimal energy supply plan and notifies households and businesses of that plan. For example, at 10:00 AM, household A will be informed of its plan to run solely on renewable energy sources.

[0856] Input: Simulated supply and demand balance data

[0857] Output: Energy supply plan provided to user

[0858] Step 7:

[0859] Subject: Server

[0860] The server monitors consumption and supply data in real time and responds immediately if there is a discrepancy between the forecast and the actual situation. For example, if it suddenly becomes cloudy at 1 p.m. and the amount of solar power generation decreases, the server will adjust the amount of power supplied by the power company to increase it.

[0861] Input: Real-time consumption and supply data

[0862] Output: Adjusted supply data

[0863] Step 8:

[0864] Subject: User

[0865] Users can check real-time electricity consumption and supply status through a dedicated app. They can also turn power-saving mode on or off and set alerts to avoid peak power usage during specific times. For example, if a user opens the app at 9 a.m., the app will display the consumption and supply status up to that point, as well as weather information.

[0866] Input: Real-time electricity consumption and supply data

[0867] Output: Consumption and supply information displayed to the user

[0868] (Application example 1)

[0869] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0870] Modern factories operate a wide variety of equipment, making energy consumption optimization a key issue. Balancing renewable energy supply and consumption is particularly difficult, making inefficient energy use more likely. There is also a need for immediate responses to sudden fluctuations in power consumption, as well as real-time data monitoring and notification. Solving these issues and maximizing energy efficiency while reducing environmental impact are essential.

[0871] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0872] In this invention, the server includes means for collecting electricity consumption data for homes and businesses in real time, means for acquiring weather data, means for acquiring renewable energy supply forecast data, means for analyzing electricity consumption patterns based on the collected data, means for analyzing fluctuations in renewable energy supply due to weather changes, means for generating a consumption forecast model, means for integrating consumption forecasts and supply forecasts to simulate the supply and demand balance, means for creating an optimal supply plan, means for notifying homes and businesses of the supply plan, means for adjusting the supply amount, and means for acquiring energy consumption data for each device in a factory in real time and optimizing the operating status of the device based on the renewable energy supply amount, thereby balancing the supply and demand of renewable energy, maximizing energy efficiency, and reducing environmental impact.

[0873] "Means for collecting real-time electricity consumption data from homes and businesses" refers to devices and methods that measure electricity consumption in real time from smart meters and IoT devices installed in homes and businesses and transmit the data to a server.

[0874] "Means for acquiring weather data" refers to devices or methods for acquiring weather information such as temperature, wind speed, and sunlight illuminance from the weather data provider's API.

[0875] "Means for acquiring renewable energy supply forecast data" refers to a device or method for predicting the supply of renewable energy such as solar power generation and wind power generation based on past supply data and weather data, and acquiring that data.

[0876] The "means for analyzing power consumption patterns based on collected data" refers to a device or method for analyzing collected power consumption data and clarifying the consumption patterns of each user.

[0877] The "means for analyzing fluctuations in renewable energy supply due to weather fluctuations" refers to a device or method for analyzing changes in the amount of renewable energy supply due to fluctuations in weather conditions.

[0878] The "means for generating a consumption prediction model" refers to a device or method for generating a model that predicts future consumption based on past power consumption data.

[0879] The "means for integrating consumption forecast and supply forecast to simulate the supply and demand balance" refers to a device or method for integrating consumption forecast data and supply forecast data to simulate the energy supply and demand balance.

[0880] The "means for creating an optimal supply plan" refers to a device or method for formulating an optimal energy supply plan based on the results of the simulation.

[0881] A "means for communicating a supply plan to a home or business" is a device or method that notifies a user of the energy supply plan that has been created.

[0882] The "means for adjusting the supply amount" refers to a device or method for appropriately adjusting the supply amount in accordance with the actual energy consumption situation.

[0883] "Means for obtaining energy consumption data for each piece of equipment in a factory in real time" refers to a device or method for measuring the energy consumption of each piece of equipment installed in a factory in real time and collecting the data.

[0884] "Means for optimizing the operating status of equipment based on the amount of renewable energy supply" refers to devices and methods that optimize the operating status of equipment within a factory in accordance with the predicted amount of renewable energy supply, and operate it efficiently.

[0885] This invention relates to an AI energy management system for efficiently managing energy consumption and supply, and specific embodiments thereof are described below.

[0886] First, the server collects real-time electricity consumption data from homes and businesses. This is achieved by installing smart meters and IoT devices in homes and businesses. These devices measure electricity consumption in real time and send that data to the server. The server can then accurately track each user's consumption.

[0887] The server then retrieves weather data from the weather data provider's API, including temperature, wind speed, and solar irradiance, and stores the weather data in an analytical database for use in forecasting renewable energy supply.

[0888] When forecasting renewable energy supplies, the server uses past supply data and weather data to predict the supply of solar and wind power. This supply forecast data is combined with current supply status data provided by the power company to produce a comprehensive supply forecast.

[0889] In analyzing consumption patterns, the server analyzes each user's electricity consumption patterns based on past consumption data. For example, peak times and average consumption amounts can be determined from household consumption patterns. The results of this analysis are used to create a model for predicting future consumption.

[0890] The server integrates consumption and supply forecasts to simulate the supply-demand balance and creates an optimal energy supply plan based on these forecast data. This plan prioritizes the use of renewable energy and supplements shortfalls with supplies from the power company.

[0891] As a specific example, suppose the server records that household A's power consumption is 3kW at 8am, and obtains from local weather data that the sunlight illuminance is 800W / m².Then, it predicts that the amount of solar power generation will increase at 10am, and a policy is made to cover household A's energy supply with renewable energy.

[0892] In addition, the server collects energy consumption data for each piece of equipment in the factory in real time and optimizes the equipment's operating status based on the amount of renewable energy supplied. For example, the server uses energy consumption data and supply forecast data to optimize the operating status of equipment operating in the factory in real time.

[0893] If there is a discrepancy between the forecast and reality, the server responds immediately. For example, if it suddenly becomes cloudy at 1 p.m. and the amount of solar power generation decreases, the server will adjust the amount of power supplied by the power company to increase, preventing a power shortage. At the same time, it will notify the user in real time as appropriate.

[0894] Users can check real-time power consumption and supply status through a dedicated app, and can also turn power saving mode on or off and set alerts to avoid peak power usage during specific times.

[0895] In this way, the system optimizes energy consumption and contributes to reducing environmental impact.The present invention plays an important role because it can effectively utilize renewable energy and improve energy efficiency.

[0896] An example of a specific prompt is, "Using the following dataset, build a predictive model for a factory's energy consumption and renewable energy supply. Then, design an optimization algorithm that operates equipment when supply exceeds consumption and shuts it down when supply does not."

[0897] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0898] Step 1:

[0899] The server collects real-time electricity consumption data from homes and businesses. This is done using smart meters and IoT devices, with each device measuring its electricity consumption and sending the data to the server. The input of this data is the real-time measurement value from each device, and the output is consumption data accumulated on the server.

[0900] Step 2:

[0901] The server retrieves weather data from the weather data provider's API. To do this, it makes an API request to obtain weather information such as temperature, wind speed, and sunlight illuminance as input data, which it then stores in a database for analysis. The output is the retrieved weather data.

[0902] Step 3:

[0903] The server predicts the supply of renewable energy based on past supply data and weather data. For example, it predicts future power generation based on past solar power generation data and current weather data. The input data are past supply data and current weather data, and the output is supply forecast data.

[0904] Step 4:

[0905] The server analyzes each user's consumption pattern based on the collected power consumption data. This process involves analyzing the consumption data of each household or business over time to determine peak hours, average consumption, etc. The input data is past consumption data, and the output is the analysis result of the consumption pattern.

[0906] Step 5:

[0907] The server integrates the analyzed consumption patterns with the supply forecast data and simulates the supply and demand balance. For example, it calculates the supply and consumption balance based on the predicted power generation amount and the consumption amount of each user. The input data are the analysis results of consumption patterns and the supply forecast data, and the output is the simulation result of the supply and demand balance.

[0908] Step 6:

[0909] The server creates an optimal energy supply plan based on the simulation results. This process maximizes the use of renewable energy and, as necessary, creates a plan that supplements supply from the power company. The input data are the simulation results of the supply and demand balance, and the output is the optimal supply plan.

[0910] Step 7:

[0911] The server then communicates the supply plan to homes and businesses, informing them of the optimal energy supply plan through a dedicated app or notification system. The input data is the optimal supply plan, and the output is the notification to the user.

[0912] Step 8:

[0913] Based on instructions from the server, the terminal obtains energy consumption data for each piece of equipment in the factory in real time and optimizes the equipment's operating status based on the amount of renewable energy supplied. For example, it determines which equipment can operate based on supply forecasts. The input data are real-time consumption data and supply forecast data, and the output is the optimized operating status of the equipment.

[0914] Step 9:

[0915] The server responds immediately if there is a discrepancy between the prediction and reality. For example, if a sudden change in weather causes a decrease in solar power generation supply, the server adjusts to increase supply from the power company. The input data are current consumption data and supply data, and the output is the adjusted supply amount.

[0916] Step 10:

[0917] Users can check real-time power consumption and supply status through a dedicated app. The app provides users with various operations such as turning power saving mode on and off and setting alerts for power usage during specific time periods. The input data is real-time consumption and supply status, and the output is the result of user operations.

[0918] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0919] This invention is an AI energy management system for effectively managing energy consumption and supply, and further improves user satisfaction by incorporating an emotion engine that recognizes user emotions. Specific embodiments are described below.

[0920] First, we will explain the part that collects electricity consumption data from homes and businesses in real time. The server periodically collects electricity consumption data from devices (e.g., smart meters and IoT devices installed in homes and businesses). The devices measure this electricity consumption data and send it to the server in real time. This allows the server to understand the consumption status of each user.

[0921] Next, we will explain weather data. The server obtains weather data (e.g., temperature, wind speed, and sunlight irradiance) from weather data providers via API and stores this data in an analytical database. The weather data is used to forecast the supply of renewable energy.

[0922] Regarding renewable energy supply forecasts, the server makes predictions based on past supply data and acquired weather data. This supply forecast data is then integrated with current supply status data provided by the power company to produce a comprehensive supply forecast.

[0923] In the consumption pattern analysis, the server analyzes each user's electricity consumption pattern based on past consumption data. Peak times and average consumption are identified from the past consumption data, and a consumption prediction model is generated based on this.

[0924] By integrating consumption and supply forecasts and simulating the supply and demand balance, the server creates an optimal energy supply plan based on this forecast data.The supply plan prioritizes the use of renewable energy and makes up for any shortfall with supplies from the power company.

[0925] As a specific example, the server records that household A's power consumption is 3kW at 8am, and obtains from local weather data that the sunlight illuminance is 800W / m². Based on this, and based on a prediction that the amount of solar power generation will increase at 10am, a plan is made to cover household A's energy supply with renewable energy.

[0926] For real-time adjustments, the server monitors the collected consumption and supply data in real time and takes immediate action if there is a discrepancy between the forecast and reality. For example, if it suddenly becomes cloudy at 1 p.m. and the amount of solar power generation decreases, the server will adjust the amount of power supplied by the power company to increase. In addition, terminals can dynamically change the consumption patterns of home appliances and other devices according to instructions from the server.

[0927] The system also includes an emotion engine that recognizes the user's emotions. This emotion engine reads emotions from the user's facial expressions and voice and sends the data to a server. The server then uses the emotion data to create a power supply plan to avoid peak power consumption during times when the user is feeling stressed. It can also adjust the content of notifications, for example, recommending power-saving mode when the user is relaxing.

[0928] As a specific example, if the emotion engine recognizes that a user is stressed while getting ready for work at 8 a.m., the server will notify the user and adjust the operation of home appliances to avoid peak power consumption in order to reduce stress.

[0929] In this way, combining the emotion engine enables energy management according to the user's emotional state, reducing stress and achieving efficient energy use. This system optimizes energy consumption, reduces environmental impact, and improves user satisfaction.

[0930] The processing flow will be explained below.

[0931] Step 1:

[0932] The server collects power consumption data from devices installed in homes and businesses. The devices measure real-time power consumption at predetermined intervals (e.g., every minute) and send the data to the server.

[0933] Step 2:

[0934] The server retrieves weather data from weather data providers via API, including temperature, wind speed, and sunlight intensity, and stores this data in a database for analysis.

[0935] Step 3:

[0936] The server predicts the supply of renewable energy based on past supply data and acquired weather data. The supply forecast data is integrated with current supply status data provided by the power company.

[0937] Step 4:

[0938] The server analyzes historical consumption data to extract power consumption patterns for each home or business. This analysis identifies peak hours and average consumption and generates a consumption forecasting model.

[0939] Step 5:

[0940] The server integrates consumption and supply forecasts to simulate the future balance of supply and demand, and then uses this forecast data to create an optimal energy supply plan.

[0941] Step 6:

[0942] The server notifies the terminal of the supply plan and instructs it to make necessary adjustments. The terminal then adjusts its power usage in real time according to the instructions from the server.

[0943] Step 7:

[0944] A supply plan is created so that renewable energy is used first and any shortfall is made up by supply from the power company. The server sends instructions to the power company to implement this plan.

[0945] Step 8:

[0946] The server monitors consumption and supply data collected in real time and responds immediately if it detects an abnormality, such as a sudden change in weather or a sudden increase in power usage by users.

[0947] Step 9:

[0948] The server receives the user's emotional data through the emotion engine, which analyzes the user's facial expressions and voice and transmits the user's emotional state to the server.

[0949] Step 10:

[0950] The server optimizes power consumption and adjusts notifications based on the user's emotional data. For example, if the user is feeling stressed, it will adjust the notification to avoid peak power consumption.

[0951] Step 11:

[0952] The server notifies the user in real time, and the user can check the current power consumption and supply status through a dedicated app and change the settings as needed.

[0953] Step 12:

[0954] The server regularly updates consumption forecasts and supply plans to continuously optimize energy efficiency, resulting in more efficient energy use and a reduced environmental impact.

[0955] Example 2

[0956] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0957] Conventional energy management systems have difficulty efficiently managing the balance between power consumption and supply, and have had particular issues with being unable to respond to sudden fluctuations in consumption or weather conditions. Furthermore, energy management does not take into account the emotional state of the user, making it difficult to contribute to improving user satisfaction or reducing stress.

[0958] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0959] In this invention, the server includes means for collecting energy consumption data from homes and business facilities in real time, means for acquiring weather information, means for acquiring supply forecast data for renewable resources, means for analyzing energy consumption patterns based on the collected data, means for analyzing fluctuations in renewable energy supply due to weather changes, means for generating a consumption forecast model, means for integrating consumption forecasts and supply forecasts to simulate the supply and demand balance, means for creating an optimal supply plan, means for notifying the home and business facilities of the supply plan, means for adjusting the supply amount, means for acquiring user emotion data, and means for creating an energy management plan based on the user emotion, thereby enabling efficient energy management and improving user satisfaction.

[0960] "Energy consumption data" refers to data that indicates the amount of energy consumed in a home or business facility.

[0961] "Weather information" is data indicating weather conditions such as temperature, wind speed, and sunlight illuminance.

[0962] "Renewable resources" refers to renewable natural resources such as solar, wind, and hydropower, and provide energy derived from these.

[0963] "Supply forecast data" is data that forecasts the amount of energy supply that can be obtained from renewable resources.

[0964] "Energy consumption pattern" indicates the user's energy consumption tendency based on past energy consumption data.

[0965] A "consumption prediction model" is a model for predicting future energy consumption based on energy consumption patterns.

[0966] "Supply and demand balance" refers to the balance between energy consumption and supply, and is necessary for efficient energy management.

[0967] A "supply plan" is an optimal energy supply plan formulated based on the balance of energy supply and demand.

[0968] "Emotion data" is data that indicates the emotional state of the user, obtained from their facial expressions and voice.

[0969] An "energy management plan" is a plan that takes into account the user's emotional state and optimizes energy consumption.

[0970] "Home and business premises" refers to buildings in which private residences and commercial activities are carried out.

[0971] This invention is an AI energy management system that effectively manages energy consumption and supply, and further incorporates an emotion engine that recognizes user emotions to improve user satisfaction. Specific embodiments for carrying out the invention will now be described.

[0972] First, the server collects energy consumption data in real time from terminals such as smart meters and IoT devices installed in homes and business facilities. The terminals measure energy consumption and send the data to the server. This allows the server to grasp the energy consumption status of each home or business facility.

[0973] The server then retrieves weather information such as temperature, wind speed, and solar irradiance through the weather data provider's API. This data is stored in an analytical database and used to forecast renewable energy supply.

[0974] The server predicts the supply of renewable energy based on past supply data and acquired weather information. The supply forecast data is integrated with current supply status data provided by energy suppliers to produce a comprehensive supply forecast.

[0975] The server analyzes each user's consumption patterns based on their past energy consumption data. Peak times and average consumption are identified from the past consumption data, and a consumption prediction model is generated based on this.

[0976] In the supply and demand balance simulation, the server integrates consumption and supply forecast data to create an optimal energy supply plan that prioritizes the use of renewable energy and makes up for any shortfall with supplies from energy suppliers.

[0977] As a specific example, the server records that household A's power consumption is 3kW at 8am, and obtains from local weather information that the sunlight illuminance is 800W / m². Based on this, and based on a prediction that the amount of solar power generation will increase at 10am, a plan is made to cover household A's energy supply with renewable energy.

[0978] In real-time adjustments, the server monitors the collected consumption and supply data in real time and takes immediate action if there is a discrepancy between the forecast and reality. For example, if it suddenly becomes cloudy at 1 p.m. and the amount of solar power generation decreases, the server will adjust the amount of power supplied by the energy supplier to increase it.

[0979] The system also includes an emotion engine that recognizes the user's emotions. The emotion engine reads emotions from the user's facial expressions and voice and sends the data to a server. The server uses the emotion data to create a power supply plan to avoid peak power consumption during times when the user is feeling stressed. It can also adjust the content of notifications, for example, recommending power-saving mode when the user is relaxing.

[0980] As a specific example, if the emotion engine recognizes that a user is stressed while getting ready for work at 8 a.m., the server will notify the user and adjust the operation of home appliances to avoid peak power consumption in order to reduce stress.

[0981] This system will optimize energy consumption, reduce environmental impact, and increase user satisfaction.

[0982] Examples of prompts for generative AI models include:

[0983] "The household's electricity consumption is recorded as 3kW at 8am and the solar illuminance is recorded as 800W / m². Please create an energy supply plan based on this situation."

[0984] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0985] Step 1:

[0986] Data collection

[0987] The server collects energy consumption data in real time from smart meters and IoT devices installed in homes and businesses. The devices measure energy consumption and send the data to the server.

[0988] Input: Energy consumption data sent from the device

[0989] Output: Collected energy consumption data is stored in a database on the server.

[0990] Specifically, the device measures consumption data, for example, every minute, and sends the data to the server, which then stores the data in a database.

[0991] Step 2:

[0992] Obtaining weather data

[0993] The server retrieves weather information through the weather data provider's API, which includes temperature, wind speed, and sunlight intensity.

[0994] Input: Weather information provided by weather data provider's API

[0995] Output: The acquired weather information is saved in a database for analysis.

[0996] Specifically, the server calls the API every hour, for example, to obtain the latest weather data and store it in a database.

[0997] Step 3:

[0998] Renewable energy supply forecast

[0999] The server makes supply forecasts based on past renewable energy supply data and current weather information.

[1000] Input: Historical renewable energy supply data, current weather information

[1001] Output: Renewable energy supply forecast data

[1002] Specifically, the server uses machine learning algorithms to generate supply forecasts for the next 24 hours.

[1003] Step 4:

[1004] Analysis of electricity consumption patterns

[1005] The server analyzes each user's consumption patterns based on past energy consumption data.

[1006] Input: Historical energy consumption data for each user

[1007] Output: Consumption patterns per user

[1008] Specifically, the server analyzes data from the past year, for example, to identify peak consumption times and average consumption amounts.

[1009] Step 5:

[1010] Generate consumption forecast models

[1011] The server generates a consumption prediction model based on the consumption pattern.

[1012] Input: Consumption Pattern

[1013] Output: Consumption forecast model

[1014] Specifically, the server uses a machine learning algorithm to build a consumption prediction model.

[1015] Step 6:

[1016] Supply and demand balance simulation

[1017] The server integrates consumption forecasts and supply forecasts to simulate the supply and demand balance.

[1018] Input: Consumption forecast model, supply forecast data

[1019] Output: Optimal supply and demand balance plan

[1020] Specifically, the server applies a simulation algorithm to calculate the supply and demand balance for the next 24 hours.

[1021] Step 7:

[1022] Creating a Supply Plan

[1023] The server creates an optimal supply plan, prioritizing the use of renewable energy and supplementing any shortfalls with supplies from energy suppliers.

[1024] Input: Supply and demand balance plan

[1025] Output: Supply Plan

[1026] Specifically, the server creates an energy supply plan based on the simulation results.

[1027] Step 8:

[1028] Notification of supply plan

[1029] The server communicates the supply plan to homes and businesses.

[1030] Input: Supply Plan

[1031] Output: Notified supply plan

[1032] Specifically, the server sends notifications to the terminals, and the plan is reflected on devices in each home or business facility.

[1033] Step 9:

[1034] Real-time adjustments

[1035] The server monitors the data collected in real time and responds immediately if any sudden fluctuations or discrepancies occur.

[1036] Input: Energy consumption data and weather data collected in real time

[1037] Output: Regulated energy supply

[1038] Specifically, the server adjusts the supply based on the new data, increasing or decreasing the supply from the energy supplier as needed.

[1039] Step 10:

[1040] Acquiring and processing emotion data

[1041] The device acquires emotional data from the user's facial expressions and voice and sends it to the server.

[1042] Input: User's facial and voice data

[1043] Output: Emotion data

[1044] Specifically, the device's camera and microphone capture data in real time, analyze it using the emotion engine, and send the results to the server.

[1045] Step 11:

[1046] Creating an emotional energy plan

[1047] Based on the emotional data, the server creates an energy supply plan that suits the emotional state.

[1048] Input: Emotion data

[1049] Output: Coordinated energy supply plan

[1050] Specifically, the server schedules the user to avoid peak power consumption during times when they are feeling stressed, and sends notifications to switch to power-saving mode when they are relaxing.

[1051] Examples of Proomp statements:

[1052] "The household's electricity consumption is recorded as 3kW at 8am and the solar illuminance is recorded as 800W / m². Please create an energy supply plan based on this situation."

[1053] (Application example 2)

[1054] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1055] Conventional energy management systems are effective at simulating supply-demand balances and creating supply plans based on power consumption and weather data, but they are unable to optimize energy efficiency while taking into account the emotional state of workers. Furthermore, there is a need for systems that can further optimize power consumption in complex and rapidly changing environments such as factories. Therefore, there is a demand for more advanced energy management systems that can reduce worker stress and improve work efficiency.

[1056] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting power consumption data from homes and businesses in real time, means for acquiring weather data, means for acquiring renewable energy supply forecast data, means for analyzing power consumption patterns based on the collected data, means for analyzing fluctuations in renewable energy supply due to weather changes, means for generating a consumption forecast model, means for integrating consumption forecasts and supply forecasts to simulate the supply and demand balance, means for creating an optimal supply plan, means for notifying homes and businesses of the supply plan, means for adjusting the supply amount, means for collecting worker emotion data, and means for adjusting the energy supply based on the emotion data. This optimizes power consumption, reduces worker stress, and enables efficient energy management.

[1057] "Home and business electricity consumption data" refers to data collected by measuring the amount of electricity consumed by homes and businesses in real time.

[1058] "Weather data" is information related to weather, including weather conditions such as temperature, wind speed, and sunlight illuminance.

[1059] "Renewable energy supply forecast data" is data that indicates the forecast supply of electricity generated from renewable energy resources such as solar and wind power.

[1060] "Power consumption patterns" are data that show usage trends, such as peak hours and average consumption, based on past power consumption data for a particular household or business.

[1061] "Weather fluctuations" refers to changes in weather conditions such as temperature, wind speed, and sunlight intensity over time.

[1062] A "consumption prediction model" is an algorithm or model for predicting future electricity consumption based on past consumption data.

[1063] "Supply and demand balance simulation" is a simulation that integrates predicted electricity consumption and supply to create an optimal energy supply plan.

[1064] A "supply plan" is a plan for efficiently supplying energy based on predicted demand and supply.

[1065] "Supply adjustment" refers to dynamically changing the supply amount in order to maintain the balance between supply and demand of electricity.

[1066] "Worker emotion data" refers to data relating to emotions acquired from the worker's facial expressions and voice, and includes stress and relaxation states.

[1067] "Adjusting energy supply based on emotional data" means adjusting the amount and method of energy supply in consideration of the emotional state of the worker.

[1068] This invention is a system that provides efficient energy management by using an application installed on a factory robot to effectively manage energy consumption and supply and also take into account the emotional state of the worker. Hereinafter, an embodiment of the invention will be described in detail.

[1069] First, to monitor the power consumption status within the factory in real time, the robots are equipped with smart meters and IoT devices. These devices collect power consumption data from the entire factory and send it to a server. The server then aggregates and stores this data in real time using cloud services such as Amazon Web Services (AWS) and Google Cloud Platform (GCP).

[1070] Next, the server obtains weather data from the internet via API. At this time, weather conditions (temperature, wind speed, sunlight illuminance, etc.) are obtained using Weather API, etc., and this data is stored in an analytical database. The weather data is used to forecast the supply of renewable energy.

[1071] The server uses past electricity consumption data and acquired weather data to predict renewable energy supply. The prediction also combines historical data and current supply status data, allowing for more accurate prediction of future supply.

[1072] The server then analyzes each robot's power consumption patterns based on past consumption data. This allows it to identify usage trends, such as peak times and average consumption, and generate a consumption prediction model. The generated consumption prediction model is then integrated with supply prediction data to simulate the supply and demand balance.

[1073] For example, if a particular robot consumes 5kW of power at 8am, the server records this value and retrieves from local weather data that the sunlight intensity is 1000W / m². Using this information, it predicts that the renewable energy supply will increase at 10am and plans to power the robot with renewable energy.

[1074] Furthermore, the server makes real-time adjustments. For example, if a sudden weather change reduces the supply of renewable energy, it can immediately adjust to increase the supply from the power company. It can also dynamically change the robot's movement patterns to avoid peak consumption.

[1075] This invention also includes an emotion engine. The emotion engine uses a camera and microphone attached to the robot to analyze the worker's facial expressions and voice to acquire emotion data. The acquired emotion data is sent to a server, which identifies the worker's stress and relaxation state. Based on this information, the energy supply is adjusted. For example, during times when the worker is feeling stressed, the supply plan is adjusted to avoid peak power consumption.

[1076] For example, if a worker arrives at work at 8 a.m. and the emotion engine detects the worker's stress, the server will use that information to create an energy supply plan to reduce stress. This reduces the burden on the worker and enables efficient energy management.

[1077] Prompt Sentence Examples

[1078] "Please monitor the energy consumption and supply in the factory in real time and generate an optimization model to manage it efficiently. Also, please create a demand forecast and supply plan that takes into account the emotional state of workers, and provide specific suggestions to avoid peak power consumption."

[1079] In this way, by combining an AI energy management system with an emotion engine, it is possible to optimize power consumption and improve work efficiency, while also reducing worker stress.

[1080] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1081] Step 1:

[1082] The server collects power consumption data in real time from smart meters and IoT devices in the factory. Smart meters and IoT devices are attached to robots and other machines and measure their power consumption. The data sent from these devices is collected by the server. The input is power consumption data, and the output is power consumption status data updated in real time.

[1083] Step 2:

[1084] The server obtains weather data through the weather data provider's API. Specifically, it collects weather information such as temperature, wind speed, and sunlight illuminance. This data is stored in an analysis database. The input is the weather data obtained from the API, and the output is the weather data stored in the analysis database.

[1085] Step 3:

[1086] The server predicts renewable energy supply based on past power consumption data and acquired weather data. It analyzes past supply data and uses an algorithm to predict future renewable energy supply based on weather conditions. The input is past power consumption data and weather data, and the output is future renewable energy supply forecast data.

[1087] Step 4:

[1088] The server generates a consumption prediction model based on past power consumption data. It analyzes the past consumption data and uses machine learning algorithms to identify consumption patterns. The input is past consumption data, and the output is a model that predicts future power consumption.

[1089] Step 5:

[1090] The server integrates the consumption forecast model and supply forecast data to simulate the supply and demand balance. This simulation creates an optimal supply plan to balance the supply and consumption of renewable energy. The inputs are the consumption forecast model and supply forecast data, and the outputs are the simulation results of the supply and demand balance and a supply plan.

[1091] Step 6:

[1092] The server issues energy supply instructions to robots and related equipment based on the supply plan. Based on the supply plan, it adjusts the power supply to maximize the use of renewable energy. Specifically, it increases the supply from the power company as needed and recommends the use of renewable energy. The input is the supply plan, and the output is the power supply instructions within the factory.

[1093] Step 7:

[1094] The system monitors data collected in real time and responds immediately if there is a discrepancy between the forecast and reality. For example, if a sudden change in weather causes a decrease in the supply of renewable energy, the server immediately increases the supply from the power company. The input is power consumption data and weather data collected in real time, and the output is the adjusted supply.

[1095] Step 8:

[1096] The server collects the worker's emotional data through the camera and microphone installed on the robot. The emotion engine analyzes facial expressions and voice to detect the worker's stress or relaxation state. The input is the worker's facial expression and voice data, and the output is emotional data.

[1097] Step 9:

[1098] The server adjusts energy consumption based on the acquired emotional data. For example, if a worker is feeling stressed, it readjusts the supply plan to avoid peak power consumption. This reduces worker stress and achieves efficient energy management. The input is emotional data, and the output is the adjusted energy supply plan.

[1099] Through the above steps, the present invention provides an optimal energy management system that effectively manages energy consumption and supply within a factory and also takes into account the emotional state of workers.

[1100] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1101] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1102] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1103] [Fourth embodiment]

[1104] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1105] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1106] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1107] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1108] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1110] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1111] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1112] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1113] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1115] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1116] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1117] This invention relates to an AI energy management system for efficiently managing energy consumption and supply, and specific embodiments thereof will be described below.

[1118] First, let's look at the part that collects electricity consumption data from homes and businesses in real time. The server periodically collects electricity consumption data from terminals (e.g., smart meters and IoT devices). The terminals are installed in homes and businesses, measure electricity consumption in real time, and send that data to the server. This allows the server to accurately grasp each user's consumption status.

[1119] Next, regarding weather data, the server obtains various weather information from the weather data provider's API and stores it in an analytical database. This weather data includes temperature, wind speed, solar irradiance, etc., and is used to forecast the supply of renewable energy.

[1120] Regarding renewable energy supply forecasts, the server makes supply forecasts for renewable energy (e.g., solar power, wind power) based on past supply data and weather data. This supply forecast data is combined with current supply status data provided by the power company to produce a comprehensive supply forecast.

[1121] In the consumption pattern analysis, the server analyzes each user's electricity consumption pattern based on past consumption data. For example, household consumption patterns reveal peak times and average consumption. The results of this analysis are used to create a future consumption prediction model.

[1122] By integrating consumption and supply forecasts and simulating the supply and demand balance, the server creates an optimal energy supply plan based on this forecast data. This plan prioritizes the use of renewable energy and makes up for any shortfall with supplies from the power company.

[1123] As a specific example, suppose the server records that household A's power consumption is 3kW at 8am, and obtains from local weather data that the sunlight illuminance is 800W / m².Then, it predicts that the amount of solar power generation will increase at 10am, and a policy is made to cover household A's energy supply with renewable energy.

[1124] For real-time adjustments, the server monitors consumption and supply data in real time and takes immediate action if there is a discrepancy between the forecast and reality. For example, if it suddenly becomes cloudy at 1 p.m. and the amount of solar power generation decreases, the server will adjust the amount of supply from the power company to increase, preventing a power shortage. Terminals can also dynamically change their consumption patterns according to instructions from the server.

[1125] Users can check real-time power consumption and supply status through a dedicated app, and can also turn power saving mode on or off, and set alerts to avoid peak power usage during specific times.

[1126] This system will optimize energy consumption and reduce environmental impact. The present invention will play an important role in effectively utilizing renewable energy and improving energy efficiency.

[1127] The processing flow will be explained below.

[1128] Step 1:

[1129] The server collects power consumption data from devices installed in homes and businesses. The devices measure real-time power consumption at predetermined intervals (e.g., every minute) and send the data to the server.

[1130] Step 2:

[1131] The server retrieves weather data from weather data providers via API, including temperature, wind speed, and sunlight intensity, and stores this data in a database for analysis.

[1132] Step 3:

[1133] The server predicts the supply of renewable energy based on past supply data and acquired weather data. The supply forecast data is integrated with current supply status data provided by the power company.

[1134] Step 4:

[1135] The server analyzes historical consumption data to extract power consumption patterns for each home or business. This analysis identifies peak hours and average consumption and generates a consumption forecasting model.

[1136] Step 5:

[1137] The server integrates consumption and supply forecasts to simulate the future balance of supply and demand, and then uses this forecast data to create an optimal energy supply plan.

[1138] Step 6:

[1139] The server notifies the terminal of the supply plan and instructs it to make necessary adjustments. The terminal then adjusts its power usage in real time according to the instructions from the server.

[1140] Step 7:

[1141] A supply plan is created so that renewable energy is used first and any shortfall is made up by supply from the power company. The server sends instructions to the power company to implement this plan.

[1142] Step 8:

[1143] The server monitors consumption and supply data collected in real time and responds immediately if it detects an abnormality, such as a sudden change in weather or a sudden increase in power usage by users.

[1144] Step 9:

[1145] The server notifies the user in real time, and the user can check the current power consumption and supply status through a dedicated app and change the settings as needed.

[1146] Step 10:

[1147] The server regularly updates consumption forecasts and supply plans to continuously optimize energy efficiency, resulting in more efficient energy use and a reduced environmental impact.

[1148] Example 1

[1149] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1150] As renewable energy becomes more widespread, there is a need to efficiently manage the balance between energy supply and consumption. However, due to weather fluctuations and sudden changes in consumption, it is difficult to predict the supply of renewable energy, making it difficult to optimize energy supply plans. In addition, there is a lack of means for users to grasp consumption and supply conditions in real time. It is necessary to solve these issues and achieve improved energy efficiency and reduced environmental impact.

[1151] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1152] In this invention, the server includes means for collecting power consumption data in real time, means for acquiring weather information, means for collecting renewable energy supply forecast data, means for analyzing power consumption patterns based on the collected data, means for analyzing fluctuations in renewable energy supply according to weather changes, means for generating a consumption forecast model, means for simulating the supply and demand balance by integrating the consumption forecast and the supply forecast, means for creating an optimal energy supply plan, means for providing the energy supply plan to users, means for adjusting the supply amount, and means for monitoring the energy consumption and supply status in real time, thereby enabling efficient management of the balance between energy supply and consumption.

[1153] "Power consumption data" is data that indicates the amount of power consumed in a home or business over a certain period of time.

[1154] "Weather information" is data relating to weather conditions, including temperature, wind speed, sunlight illuminance, and the like.

[1155] "Renewable energy supply forecast data" is data used to predict the supply of renewable energy, such as solar power generation and wind power generation.

[1156] The term "electricity consumption pattern" refers to the tendency or characteristics of electricity consumption during a specific period, analyzed based on past electricity consumption data.

[1157] "Variations in renewable energy supply" means changes in the supply of renewable energy caused by weather or other external factors.

[1158] A "consumption forecasting model" is a mathematical or computational model for predicting future electricity consumption based on past consumption data, etc.

[1159] "Supply and demand balance simulation" is a method for virtually verifying the balance between energy supply and consumption based on electricity consumption forecast data and supply forecast data.

[1160] The "energy supply plan" is a plan designed to optimize the energy supply required by each user, prioritizing the use of renewable energy and supplementing any shortfall with external power supplies.

[1161] The "means for providing an energy supply plan to a user" refers to a method or system for notifying or displaying an optimized energy supply plan to a user.

[1162] "Means for adjusting supply amount" refers to a system or method for increasing or decreasing the external power supply when the supply of renewable energy is insufficient.

[1163] "Means for monitoring energy consumption and supply status in real time" refers to a method or system that instantly tracks the current status of power consumption and supply and takes appropriate action in response to fluctuations.

[1164] This invention relates to an AI energy management system for efficiently managing energy consumption and supply. Specific embodiments are described below.

[1165] 1. System Overview

[1166] This system consists of a server, terminals, and users. The server collects and analyzes energy consumption data and weather data, and predicts renewable energy supply. The terminals measure power consumption data and send it to the server. Users can check real-time power consumption and supply status through a dedicated app.

[1167] 2. Hardware and software used

[1168] Server: Performs data collection, analysis, prediction, simulation, and adjustment. In particular, it utilizes cloud platforms such as AWS (Amazon Web Services) and Microsoft Azure.

[1169] Devices: Smart meters, IoT devices (e.g., SmartThings, Nest Thermostat).

[1170] Weather data APIs: OpenWeatherMap, Weatherstack.

[1171] Analytics databases: Amazon RDS, MySQL, PostgreSQL.

[1172] 3. System Operation

[1173] Server Operation

[1174] The server collects real-time electricity consumption data from homes and businesses. To this end, the server periodically collects electricity consumption data from terminals (e.g., smart meters and IoT devices). This allows the server to accurately understand the consumption status of each user.

[1175] Next, the server periodically retrieves weather data from weather data providers' APIs (e.g., OpenWeatherMap, Weatherstack) and stores it in a database for analysis. The retrieved weather data includes temperature, wind speed, and sunlight irradiance.

[1176] The server also generates forecasts for renewable energy sources (e.g., solar and wind power) based on historical supply and weather data. This forecast data is combined with current supply data provided by the power company to produce a comprehensive forecast.

[1177] Device behavior

[1178] The device is installed at the site (home or business) and measures power consumption in real time. The measured data is then sent to the server. For example, the device detects that home A's power consumption is 3kW at 8:00 AM and sends that data to the server.

[1179] User operations

[1180] Users can check real-time power consumption and supply status through the dedicated app. They can also turn power saving mode on or off and set alerts to avoid peak power usage during specific times. For example, if a user opens the app at 9 a.m., the current power consumption, supply, and weather information will be displayed.

[1181] 4. Example of a system

[1182] As a specific example, suppose the server records that the power consumption of household A is 3kW at 8am and obtains that the sunlight illuminance in the area is 800W / m².Then, it predicts that the amount of solar power generation will increase at 10am, and a policy is made to cover household A's energy supply with renewable energy.

[1183] If the weather suddenly becomes cloudy at 1 p.m. and the amount of solar power generation decreases, the server will adjust the amount of power supplied by the power company to increase, preventing a power shortage. The device can also dynamically change its consumption pattern according to instructions from the server.

[1184] Prompt Sentence Examples

[1185] "Record the electricity consumption of household A from 8:00 AM onwards, obtain weather data (sunlight illuminance) for the area, and predict the solar power generation supply for 10:00 AM. Also, explain how users can check the real-time situation through a dedicated app."

[1186] This system will optimize energy consumption and reduce environmental impact. The present invention will play an important role in effectively utilizing renewable energy and improving energy efficiency.

[1187] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1188] Step 1:

[1189] Subject: Device

[1190] Terminals (smart meters and IoT devices) measure the electricity consumption data of homes and businesses in real time. The measured data is sent to a server every few minutes. For example, at 8:00 AM, the electricity consumption of home A is measured to be 3 kW, and this data is sent to the server.

[1191] Input: Current electricity consumption for your home or business

[1192] Output: Power consumption data sent to the server

[1193] Step 2:

[1194] Subject: Server

[1195] The server periodically obtains weather information from weather data providers' APIs (e.g., OpenWeatherMap, Weatherstack) and stores it in a database for analysis. The obtained weather data includes temperature, wind speed, and sunlight irradiance. For example, it obtains that the sunlight irradiance in the area is 800W / m² at 8:00 AM and stores this data in the database.

[1196] Input: Weather data from a weather data provider

[1197] Output: Weather data stored in a database for analysis

[1198] Step 3:

[1199] Subject: Server

[1200] The server analyzes past consumption data to identify each user's consumption pattern. The analysis results, including peak hours and average consumption, are used to create a consumption prediction model. For example, the evening peak hours can be identified from the consumption pattern of household A.

[1201] Input: Historical electricity consumption data

[1202] Output: Identified consumption pattern data

[1203] Step 4:

[1204] Subject: Server

[1205] The server predicts the supply of renewable energy (e.g., solar power, wind power) based on past supply data and acquired weather data. For example, it predicts that the supply of solar power will increase at 10:00 a.m.

[1206] Input: Historical renewable energy supply data and weather data

[1207] Output: Forecasted renewable energy supply data

[1208] Step 5:

[1209] Subject: Server

[1210] The server integrates the consumption forecast data and supply forecast data to simulate the supply and demand balance. This simulation creates an optimal energy supply plan. For example, it simulates both the case where all of the energy consumed by household A is covered by solar power generation, and the case where the shortfall is supplemented by the power company.

[1211] Input: Consumption forecast data and supply forecast data

[1212] Output: Simulated supply and demand balance data

[1213] Step 6:

[1214] Subject: Server

[1215] The server creates an optimal energy supply plan and notifies households and businesses of that plan. For example, at 10:00 AM, household A will be informed of its plan to run solely on renewable energy sources.

[1216] Input: Simulated supply and demand balance data

[1217] Output: Energy supply plan provided to user

[1218] Step 7:

[1219] Subject: Server

[1220] The server monitors consumption and supply data in real time and responds immediately if there is a discrepancy between the forecast and the actual situation. For example, if it suddenly becomes cloudy at 1 p.m. and the amount of solar power generation decreases, the server will adjust the amount of power supplied by the power company to increase it.

[1221] Input: Real-time consumption and supply data

[1222] Output: Adjusted supply data

[1223] Step 8:

[1224] Subject: User

[1225] Users can check real-time electricity consumption and supply status through a dedicated app. They can also turn power-saving mode on or off and set alerts to avoid peak power usage during specific times. For example, if a user opens the app at 9 a.m., the app will display the consumption and supply status up to that point, as well as weather information.

[1226] Input: Real-time electricity consumption and supply data

[1227] Output: Consumption and supply information displayed to the user

[1228] (Application example 1)

[1229] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1230] Modern factories operate a wide variety of equipment, making energy consumption optimization a key issue. Balancing renewable energy supply and consumption is particularly difficult, making inefficient energy use more likely. There is also a need for immediate responses to sudden fluctuations in power consumption, as well as real-time data monitoring and notification. Solving these issues and maximizing energy efficiency while reducing environmental impact are essential.

[1231] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1232] In this invention, the server includes means for collecting electricity consumption data for homes and businesses in real time, means for acquiring weather data, means for acquiring renewable energy supply forecast data, means for analyzing electricity consumption patterns based on the collected data, means for analyzing fluctuations in renewable energy supply due to weather changes, means for generating a consumption forecast model, means for integrating consumption forecasts and supply forecasts to simulate the supply and demand balance, means for creating an optimal supply plan, means for notifying homes and businesses of the supply plan, means for adjusting the supply amount, and means for acquiring energy consumption data for each device in a factory in real time and optimizing the operating status of the device based on the renewable energy supply amount, thereby balancing the supply and demand of renewable energy, maximizing energy efficiency, and reducing environmental impact.

[1233] "Means for collecting real-time electricity consumption data from homes and businesses" refers to devices and methods that measure electricity consumption in real time from smart meters and IoT devices installed in homes and businesses and transmit the data to a server.

[1234] "Means for acquiring weather data" refers to devices or methods for acquiring weather information such as temperature, wind speed, and sunlight illuminance from the weather data provider's API.

[1235] "Means for acquiring renewable energy supply forecast data" refers to a device or method for predicting the supply of renewable energy such as solar power generation and wind power generation based on past supply data and weather data, and acquiring that data.

[1236] The "means for analyzing power consumption patterns based on collected data" refers to a device or method for analyzing collected power consumption data and clarifying the consumption patterns of each user.

[1237] The "means for analyzing fluctuations in renewable energy supply due to weather fluctuations" refers to a device or method for analyzing changes in the amount of renewable energy supply due to fluctuations in weather conditions.

[1238] The "means for generating a consumption prediction model" refers to a device or method for generating a model that predicts future consumption based on past power consumption data.

[1239] The "means for integrating consumption forecast and supply forecast to simulate the supply and demand balance" refers to a device or method for integrating consumption forecast data and supply forecast data to simulate the energy supply and demand balance.

[1240] The "means for creating an optimal supply plan" refers to a device or method for formulating an optimal energy supply plan based on the results of the simulation.

[1241] A "means for communicating a supply plan to a home or business" is a device or method that notifies a user of the energy supply plan that has been created.

[1242] The "means for adjusting the supply amount" refers to a device or method for appropriately adjusting the supply amount in accordance with the actual energy consumption situation.

[1243] "Means for obtaining energy consumption data for each piece of equipment in a factory in real time" refers to a device or method for measuring the energy consumption of each piece of equipment installed in a factory in real time and collecting the data.

[1244] "Means for optimizing the operating status of equipment based on the amount of renewable energy supply" refers to devices and methods that optimize the operating status of equipment within a factory in accordance with the predicted amount of renewable energy supply, and operate it efficiently.

[1245] This invention relates to an AI energy management system for efficiently managing energy consumption and supply, and specific embodiments thereof are described below.

[1246] First, the server collects real-time electricity consumption data from homes and businesses. This is achieved by installing smart meters and IoT devices in homes and businesses. These devices measure electricity consumption in real time and send that data to the server. The server can then accurately track each user's consumption.

[1247] The server then retrieves weather data from the weather data provider's API, including temperature, wind speed, and solar irradiance, and stores the weather data in an analytical database for use in forecasting renewable energy supply.

[1248] When forecasting renewable energy supplies, the server uses past supply data and weather data to predict the supply of solar and wind power. This supply forecast data is combined with current supply status data provided by the power company to produce a comprehensive supply forecast.

[1249] In analyzing consumption patterns, the server analyzes each user's electricity consumption patterns based on past consumption data. For example, peak times and average consumption amounts can be determined from household consumption patterns. The results of this analysis are used to create a model for predicting future consumption.

[1250] The server integrates consumption and supply forecasts to simulate the supply-demand balance and creates an optimal energy supply plan based on these forecast data. This plan prioritizes the use of renewable energy and supplements shortfalls with supplies from the power company.

[1251] As a specific example, suppose the server records that household A's power consumption is 3kW at 8am, and obtains from local weather data that the sunlight illuminance is 800W / m².Then, it predicts that the amount of solar power generation will increase at 10am, and a policy is made to cover household A's energy supply with renewable energy.

[1252] In addition, the server collects energy consumption data for each piece of equipment in the factory in real time and optimizes the equipment's operating status based on the amount of renewable energy supplied. For example, the server uses energy consumption data and supply forecast data to optimize the operating status of equipment operating in the factory in real time.

[1253] If there is a discrepancy between the forecast and reality, the server responds immediately. For example, if it suddenly becomes cloudy at 1 p.m. and the amount of solar power generation decreases, the server will adjust the amount of power supplied by the power company to increase, preventing a power shortage. At the same time, it will notify the user in real time as appropriate.

[1254] Users can check real-time power consumption and supply status through a dedicated app, and can also turn power saving mode on or off and set alerts to avoid peak power usage during specific times.

[1255] In this way, the system optimizes energy consumption and contributes to reducing environmental impact.The present invention plays an important role because it can effectively utilize renewable energy and improve energy efficiency.

[1256] An example of a specific prompt is, "Using the following dataset, build a predictive model for a factory's energy consumption and renewable energy supply. Then, design an optimization algorithm that operates equipment when supply exceeds consumption and shuts it down when supply does not."

[1257] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1258] Step 1:

[1259] The server collects real-time electricity consumption data from homes and businesses. This is done using smart meters and IoT devices, with each device measuring its electricity consumption and sending the data to the server. The input of this data is the real-time measurement value from each device, and the output is consumption data accumulated on the server.

[1260] Step 2:

[1261] The server retrieves weather data from the weather data provider's API. To do this, it makes an API request to obtain weather information such as temperature, wind speed, and sunlight illuminance as input data, which it then stores in a database for analysis. The output is the retrieved weather data.

[1262] Step 3:

[1263] The server predicts the supply of renewable energy based on past supply data and weather data. For example, it predicts future power generation based on past solar power generation data and current weather data. The input data are past supply data and current weather data, and the output is supply forecast data.

[1264] Step 4:

[1265] The server analyzes each user's consumption pattern based on the collected power consumption data. This process involves analyzing the consumption data of each household or business over time to determine peak hours, average consumption, etc. The input data is past consumption data, and the output is the analysis result of the consumption pattern.

[1266] Step 5:

[1267] The server integrates the analyzed consumption patterns with the supply forecast data and simulates the supply and demand balance. For example, it calculates the supply and consumption balance based on the predicted power generation amount and the consumption amount of each user. The input data are the analysis results of consumption patterns and the supply forecast data, and the output is the simulation result of the supply and demand balance.

[1268] Step 6:

[1269] The server creates an optimal energy supply plan based on the simulation results. This process maximizes the use of renewable energy and, as necessary, creates a plan that supplements supply from the power company. The input data are the simulation results of the supply and demand balance, and the output is the optimal supply plan.

[1270] Step 7:

[1271] The server then communicates the supply plan to homes and businesses, informing them of the optimal energy supply plan through a dedicated app or notification system. The input data is the optimal supply plan, and the output is the notification to the user.

[1272] Step 8:

[1273] Based on instructions from the server, the terminal obtains energy consumption data for each piece of equipment in the factory in real time and optimizes the equipment's operating status based on the amount of renewable energy supplied. For example, it determines which equipment can operate based on supply forecasts. The input data are real-time consumption data and supply forecast data, and the output is the optimized operating status of the equipment.

[1274] Step 9:

[1275] The server responds immediately if there is a discrepancy between the prediction and reality. For example, if a sudden change in weather causes a decrease in solar power generation supply, the server adjusts to increase supply from the power company. The input data are current consumption data and supply data, and the output is the adjusted supply amount.

[1276] Step 10:

[1277] Users can check real-time power consumption and supply status through a dedicated app. The app provides users with various operations such as turning power saving mode on and off and setting alerts for power usage during specific time periods. The input data is real-time consumption and supply status, and the output is the result of user operations.

[1278] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1279] This invention is an AI energy management system for effectively managing energy consumption and supply, and further improves user satisfaction by incorporating an emotion engine that recognizes user emotions. Specific embodiments are described below.

[1280] First, we will explain the part that collects electricity consumption data from homes and businesses in real time. The server periodically collects electricity consumption data from devices (e.g., smart meters and IoT devices installed in homes and businesses). The devices measure this electricity consumption data and send it to the server in real time. This allows the server to understand the consumption status of each user.

[1281] Next, we will explain weather data. The server obtains weather data (e.g., temperature, wind speed, and sunlight irradiance) from weather data providers via API and stores this data in an analytical database. The weather data is used to forecast the supply of renewable energy.

[1282] Regarding renewable energy supply forecasts, the server makes predictions based on past supply data and acquired weather data. This supply forecast data is then integrated with current supply status data provided by the power company to produce a comprehensive supply forecast.

[1283] In the consumption pattern analysis, the server analyzes each user's electricity consumption pattern based on past consumption data. Peak times and average consumption are identified from the past consumption data, and a consumption prediction model is generated based on this.

[1284] By integrating consumption and supply forecasts and simulating the supply and demand balance, the server creates an optimal energy supply plan based on this forecast data.The supply plan prioritizes the use of renewable energy and makes up for any shortfall with supplies from the power company.

[1285] As a specific example, the server records that household A's power consumption is 3kW at 8am, and obtains from local weather data that the sunlight illuminance is 800W / m². Based on this, and based on a prediction that the amount of solar power generation will increase at 10am, a plan is made to cover household A's energy supply with renewable energy.

[1286] For real-time adjustments, the server monitors the collected consumption and supply data in real time and takes immediate action if there is a discrepancy between the forecast and reality. For example, if it suddenly becomes cloudy at 1 p.m. and the amount of solar power generation decreases, the server will adjust the amount of power supplied by the power company to increase. In addition, terminals can dynamically change the consumption patterns of home appliances and other devices according to instructions from the server.

[1287] The system also includes an emotion engine that recognizes the user's emotions. This emotion engine reads emotions from the user's facial expressions and voice and sends the data to a server. The server then uses the emotion data to create a power supply plan to avoid peak power consumption during times when the user is feeling stressed. It can also adjust the content of notifications, for example, recommending power-saving mode when the user is relaxing.

[1288] As a specific example, if the emotion engine recognizes that a user is stressed while getting ready for work at 8 a.m., the server will notify the user and adjust the operation of home appliances to avoid peak power consumption in order to reduce stress.

[1289] In this way, combining the emotion engine enables energy management according to the user's emotional state, reducing stress and achieving efficient energy use. This system optimizes energy consumption, reduces environmental impact, and improves user satisfaction.

[1290] The processing flow will be explained below.

[1291] Step 1:

[1292] The server collects power consumption data from devices installed in homes and businesses. The devices measure real-time power consumption at predetermined intervals (e.g., every minute) and send the data to the server.

[1293] Step 2:

[1294] The server retrieves weather data from weather data providers via API, including temperature, wind speed, and sunlight intensity, and stores this data in a database for analysis.

[1295] Step 3:

[1296] The server predicts the supply of renewable energy based on past supply data and acquired weather data. The supply forecast data is integrated with current supply status data provided by the power company.

[1297] Step 4:

[1298] The server analyzes historical consumption data to extract power consumption patterns for each home or business. This analysis identifies peak hours and average consumption and generates a consumption forecasting model.

[1299] Step 5:

[1300] The server integrates consumption and supply forecasts to simulate the future balance of supply and demand, and then uses this forecast data to create an optimal energy supply plan.

[1301] Step 6:

[1302] The server notifies the terminal of the supply plan and instructs it to make necessary adjustments. The terminal then adjusts its power usage in real time according to the instructions from the server.

[1303] Step 7:

[1304] A supply plan is created so that renewable energy is used first and any shortfall is made up by supply from the power company. The server sends instructions to the power company to implement this plan.

[1305] Step 8:

[1306] The server monitors consumption and supply data collected in real time and responds immediately if it detects an abnormality, such as a sudden change in weather or a sudden increase in power usage by users.

[1307] Step 9:

[1308] The server receives the user's emotional data through the emotion engine, which analyzes the user's facial expressions and voice and transmits the user's emotional state to the server.

[1309] Step 10:

[1310] The server optimizes power consumption and adjusts notifications based on the user's emotional data. For example, if the user is feeling stressed, it will adjust the notification to avoid peak power consumption.

[1311] Step 11:

[1312] The server notifies the user in real time, and the user can check the current power consumption and supply status through a dedicated app and change the settings as needed.

[1313] Step 12:

[1314] The server regularly updates consumption forecasts and supply plans to continuously optimize energy efficiency, resulting in more efficient energy use and a reduced environmental impact.

[1315] Example 2

[1316] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1317] Conventional energy management systems have difficulty efficiently managing the balance between power consumption and supply, and have had particular issues with being unable to respond to sudden fluctuations in consumption or weather conditions. Furthermore, energy management does not take into account the emotional state of the user, making it difficult to contribute to improving user satisfaction or reducing stress.

[1318] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1319] In this invention, the server includes means for collecting energy consumption data from homes and business facilities in real time, means for acquiring weather information, means for acquiring supply forecast data for renewable resources, means for analyzing energy consumption patterns based on the collected data, means for analyzing fluctuations in renewable energy supply due to weather changes, means for generating a consumption forecast model, means for integrating consumption forecasts and supply forecasts to simulate the supply and demand balance, means for creating an optimal supply plan, means for notifying the home and business facilities of the supply plan, means for adjusting the supply amount, means for acquiring user emotion data, and means for creating an energy management plan based on the user emotion, thereby enabling efficient energy management and improving user satisfaction.

[1320] "Energy consumption data" refers to data that indicates the amount of energy consumed in a home or business facility.

[1321] "Weather information" is data indicating weather conditions such as temperature, wind speed, and sunlight illuminance.

[1322] "Renewable resources" refers to renewable natural resources such as solar, wind, and hydropower, and provide energy derived from these.

[1323] "Supply forecast data" is data that forecasts the amount of energy supply that can be obtained from renewable resources.

[1324] "Energy consumption pattern" indicates the user's energy consumption tendency based on past energy consumption data.

[1325] A "consumption prediction model" is a model for predicting future energy consumption based on energy consumption patterns.

[1326] "Supply and demand balance" refers to the balance between energy consumption and supply, and is necessary for efficient energy management.

[1327] A "supply plan" is an optimal energy supply plan formulated based on the balance of energy supply and demand.

[1328] "Emotion data" is data that indicates the emotional state of the user, obtained from their facial expressions and voice.

[1329] An "energy management plan" is a plan that takes into account the user's emotional state and optimizes energy consumption.

[1330] "Home and business premises" refers to buildings in which private residences and commercial activities are carried out.

[1331] This invention is an AI energy management system that effectively manages energy consumption and supply, and further incorporates an emotion engine that recognizes user emotions to improve user satisfaction. Specific embodiments for carrying out the invention will now be described.

[1332] First, the server collects energy consumption data in real time from terminals such as smart meters and IoT devices installed in homes and business facilities. The terminals measure energy consumption and send the data to the server. This allows the server to grasp the energy consumption status of each home or business facility.

[1333] The server then retrieves weather information such as temperature, wind speed, and solar irradiance through the weather data provider's API. This data is stored in an analytical database and used to forecast renewable energy supply.

[1334] The server predicts the supply of renewable energy based on past supply data and acquired weather information. The supply forecast data is integrated with current supply status data provided by energy suppliers to produce a comprehensive supply forecast.

[1335] The server analyzes each user's consumption patterns based on their past energy consumption data. Peak times and average consumption are identified from the past consumption data, and a consumption prediction model is generated based on this.

[1336] In the supply and demand balance simulation, the server integrates consumption and supply forecast data to create an optimal energy supply plan that prioritizes the use of renewable energy and makes up for any shortfall with supplies from energy suppliers.

[1337] As a specific example, the server records that household A's power consumption is 3kW at 8am, and obtains from local weather information that the sunlight illuminance is 800W / m². Based on this, and based on a prediction that the amount of solar power generation will increase at 10am, a plan is made to cover household A's energy supply with renewable energy.

[1338] In real-time adjustments, the server monitors the collected consumption and supply data in real time and takes immediate action if there is a discrepancy between the forecast and reality. For example, if it suddenly becomes cloudy at 1 p.m. and the amount of solar power generation decreases, the server will adjust the amount of power supplied by the energy supplier to increase it.

[1339] The system also includes an emotion engine that recognizes the user's emotions. The emotion engine reads emotions from the user's facial expressions and voice and sends the data to a server. The server uses the emotion data to create a power supply plan to avoid peak power consumption during times when the user is feeling stressed. It can also adjust the content of notifications, for example, recommending power-saving mode when the user is relaxing.

[1340] As a specific example, if the emotion engine recognizes that a user is stressed while getting ready for work at 8 a.m., the server will notify the user and adjust the operation of home appliances to avoid peak power consumption in order to reduce stress.

[1341] This system will optimize energy consumption, reduce environmental impact, and increase user satisfaction.

[1342] Examples of prompts for generative AI models include:

[1343] "The household's electricity consumption is recorded as 3kW at 8am and the solar illuminance is recorded as 800W / m². Please create an energy supply plan based on this situation."

[1344] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1345] Step 1:

[1346] Data collection

[1347] The server collects energy consumption data in real time from smart meters and IoT devices installed in homes and businesses. The devices measure energy consumption and send the data to the server.

[1348] Input: Energy consumption data sent from the device

[1349] Output: Collected energy consumption data is stored in a database on the server.

[1350] Specifically, the device measures consumption data, for example, every minute, and sends the data to the server, which then stores the data in a database.

[1351] Step 2:

[1352] Obtaining weather data

[1353] The server retrieves weather information through the weather data provider's API, which includes temperature, wind speed, and sunlight intensity.

[1354] Input: Weather information provided by weather data provider's API

[1355] Output: The acquired weather information is saved in a database for analysis.

[1356] Specifically, the server calls the API every hour, for example, to obtain the latest weather data and store it in a database.

[1357] Step 3:

[1358] Renewable energy supply forecast

[1359] The server makes supply forecasts based on past renewable energy supply data and current weather information.

[1360] Input: Historical renewable energy supply data, current weather information

[1361] Output: Renewable energy supply forecast data

[1362] Specifically, the server uses machine learning algorithms to generate supply forecasts for the next 24 hours.

[1363] Step 4:

[1364] Analysis of electricity consumption patterns

[1365] The server analyzes each user's consumption patterns based on past energy consumption data.

[1366] Input: Historical energy consumption data for each user

[1367] Output: Consumption patterns per user

[1368] Specifically, the server analyzes data from the past year, for example, to identify peak consumption times and average consumption amounts.

[1369] Step 5:

[1370] Generate consumption forecast models

[1371] The server generates a consumption prediction model based on the consumption pattern.

[1372] Input: Consumption Pattern

[1373] Output: Consumption forecast model

[1374] Specifically, the server uses a machine learning algorithm to build a consumption prediction model.

[1375] Step 6:

[1376] Supply and demand balance simulation

[1377] The server integrates consumption forecasts and supply forecasts to simulate the supply and demand balance.

[1378] Input: Consumption forecast model, supply forecast data

[1379] Output: Optimal supply and demand balance plan

[1380] Specifically, the server applies a simulation algorithm to calculate the supply and demand balance for the next 24 hours.

[1381] Step 7:

[1382] Creating a Supply Plan

[1383] The server creates an optimal supply plan, prioritizing the use of renewable energy and supplementing any shortfalls with supplies from energy suppliers.

[1384] Input: Supply and demand balance plan

[1385] Output: Supply Plan

[1386] Specifically, the server creates an energy supply plan based on the simulation results.

[1387] Step 8:

[1388] Notification of supply plan

[1389] The server communicates the supply plan to homes and businesses.

[1390] Input: Supply Plan

[1391] Output: Notified supply plan

[1392] Specifically, the server sends notifications to the terminals, and the plan is reflected on devices in each home or business facility.

[1393] Step 9:

[1394] Real-time adjustments

[1395] The server monitors the data collected in real time and responds immediately if any sudden fluctuations or discrepancies occur.

[1396] Input: Energy consumption data and weather data collected in real time

[1397] Output: Regulated energy supply

[1398] Specifically, the server adjusts the supply based on the new data, increasing or decreasing the supply from the energy supplier as needed.

[1399] Step 10:

[1400] Acquiring and processing emotion data

[1401] The device acquires emotional data from the user's facial expressions and voice and sends it to the server.

[1402] Input: User's facial and voice data

[1403] Output: Emotion data

[1404] Specifically, the device's camera and microphone capture data in real time, analyze it using the emotion engine, and send the results to the server.

[1405] Step 11:

[1406] Creating an emotional energy plan

[1407] Based on the emotional data, the server creates an energy supply plan that suits the emotional state.

[1408] Input: Emotion data

[1409] Output: Coordinated energy supply plan

[1410] Specifically, the server schedules the user to avoid peak power consumption during times when they are feeling stressed, and sends notifications to switch to power-saving mode when they are relaxing.

[1411] Examples of Proomp statements:

[1412] "The household's electricity consumption is recorded as 3kW at 8am and the solar illuminance is recorded as 800W / m². Please create an energy supply plan based on this situation."

[1413] (Application example 2)

[1414] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1415] Conventional energy management systems are effective at simulating supply-demand balances and creating supply plans based on power consumption and weather data, but they are unable to optimize energy efficiency while taking into account the emotional state of workers. Furthermore, there is a need for systems that can further optimize power consumption in complex and rapidly changing environments such as factories. Therefore, there is a demand for more advanced energy management systems that can reduce worker stress and improve work efficiency.

[1416] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting power consumption data from homes and businesses in real time, means for acquiring weather data, means for acquiring renewable energy supply forecast data, means for analyzing power consumption patterns based on the collected data, means for analyzing fluctuations in renewable energy supply due to weather changes, means for generating a consumption forecast model, means for integrating consumption forecasts and supply forecasts to simulate the supply and demand balance, means for creating an optimal supply plan, means for notifying homes and businesses of the supply plan, means for adjusting the supply amount, means for collecting worker emotion data, and means for adjusting the energy supply based on the emotion data. This optimizes power consumption, reduces worker stress, and enables efficient energy management.

[1417] "Home and business electricity consumption data" refers to data collected by measuring the amount of electricity consumed by homes and businesses in real time.

[1418] "Weather data" is information related to weather, including weather conditions such as temperature, wind speed, and sunlight illuminance.

[1419] "Renewable energy supply forecast data" is data that indicates the forecast supply of electricity generated from renewable energy resources such as solar and wind power.

[1420] "Power consumption patterns" are data that show usage trends, such as peak hours and average consumption, based on past power consumption data for a particular household or business.

[1421] "Weather fluctuations" refers to changes in weather conditions such as temperature, wind speed, and sunlight intensity over time.

[1422] A "consumption prediction model" is an algorithm or model for predicting future electricity consumption based on past consumption data.

[1423] "Supply and demand balance simulation" is a simulation that integrates predicted electricity consumption and supply to create an optimal energy supply plan.

[1424] A "supply plan" is a plan for efficiently supplying energy based on predicted demand and supply.

[1425] "Supply adjustment" refers to dynamically changing the supply amount in order to maintain the balance between supply and demand of electricity.

[1426] "Worker emotion data" refers to data relating to emotions acquired from the worker's facial expressions and voice, and includes stress and relaxation states.

[1427] "Adjusting energy supply based on emotional data" means adjusting the amount and method of energy supply in consideration of the emotional state of the worker.

[1428] This invention is a system that provides efficient energy management by using an application installed on a factory robot to effectively manage energy consumption and supply and also take into account the emotional state of the worker. Hereinafter, an embodiment of the invention will be described in detail.

[1429] First, to monitor the power consumption status within the factory in real time, the robots are equipped with smart meters and IoT devices. These devices collect power consumption data from the entire factory and send it to a server. The server then aggregates and stores this data in real time using cloud services such as Amazon Web Services (AWS) and Google Cloud Platform (GCP).

[1430] Next, the server obtains weather data from the internet via API. At this time, weather conditions (temperature, wind speed, sunlight illuminance, etc.) are obtained using Weather API, etc., and this data is stored in an analytical database. The weather data is used to forecast the supply of renewable energy.

[1431] The server uses past electricity consumption data and acquired weather data to predict renewable energy supply. The prediction also combines historical data and current supply status data, allowing for more accurate prediction of future supply.

[1432] The server then analyzes each robot's power consumption patterns based on past consumption data. This allows it to identify usage trends, such as peak times and average consumption, and generate a consumption prediction model. The generated consumption prediction model is then integrated with supply prediction data to simulate the supply and demand balance.

[1433] For example, if a particular robot consumes 5kW of power at 8am, the server records this value and retrieves from local weather data that the sunlight intensity is 1000W / m². Using this information, it predicts that the renewable energy supply will increase at 10am and plans to power the robot with renewable energy.

[1434] Furthermore, the server makes real-time adjustments. For example, if a sudden weather change reduces the supply of renewable energy, it can immediately adjust to increase the supply from the power company. It can also dynamically change the robot's movement patterns to avoid peak consumption.

[1435] This invention also includes an emotion engine. The emotion engine uses a camera and microphone attached to the robot to analyze the worker's facial expressions and voice to acquire emotion data. The acquired emotion data is sent to a server, which identifies the worker's stress and relaxation state. Based on this information, the energy supply is adjusted. For example, during times when the worker is feeling stressed, the supply plan is adjusted to avoid peak power consumption.

[1436] For example, if a worker arrives at work at 8 a.m. and the emotion engine detects the worker's stress, the server will use that information to create an energy supply plan to reduce stress. This reduces the burden on the worker and enables efficient energy management.

[1437] Prompt Sentence Examples

[1438] "Please monitor the energy consumption and supply in the factory in real time and generate an optimization model to manage it efficiently. Also, please create a demand forecast and supply plan that takes into account the emotional state of workers, and provide specific suggestions to avoid peak power consumption."

[1439] In this way, by combining an AI energy management system with an emotion engine, it is possible to optimize power consumption and improve work efficiency, while also reducing worker stress.

[1440] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1441] Step 1:

[1442] The server collects power consumption data in real time from smart meters and IoT devices in the factory. Smart meters and IoT devices are attached to robots and other machines and measure their power consumption. The data sent from these devices is collected by the server. The input is power consumption data, and the output is power consumption status data updated in real time.

[1443] Step 2:

[1444] The server obtains weather data through the weather data provider's API. Specifically, it collects weather information such as temperature, wind speed, and sunlight illuminance. This data is stored in an analysis database. The input is the weather data obtained from the API, and the output is the weather data stored in the analysis database.

[1445] Step 3:

[1446] The server predicts renewable energy supply based on past power consumption data and acquired weather data. It analyzes past supply data and uses an algorithm to predict future renewable energy supply based on weather conditions. The input is past power consumption data and weather data, and the output is future renewable energy supply forecast data.

[1447] Step 4:

[1448] The server generates a consumption prediction model based on past power consumption data. It analyzes the past consumption data and uses machine learning algorithms to identify consumption patterns. The input is past consumption data, and the output is a model that predicts future power consumption.

[1449] Step 5:

[1450] The server integrates the consumption forecast model and supply forecast data to simulate the supply and demand balance. This simulation creates an optimal supply plan to balance the supply and consumption of renewable energy. The inputs are the consumption forecast model and supply forecast data, and the outputs are the simulation results of the supply and demand balance and a supply plan.

[1451] Step 6:

[1452] The server issues energy supply instructions to robots and related equipment based on the supply plan. Based on the supply plan, it adjusts the power supply to maximize the use of renewable energy. Specifically, it increases the supply from the power company as needed and recommends the use of renewable energy. The input is the supply plan, and the output is the power supply instructions within the factory.

[1453] Step 7:

[1454] The system monitors data collected in real time and responds immediately if there is a discrepancy between the forecast and reality. For example, if a sudden change in weather causes a decrease in the supply of renewable energy, the server immediately increases the supply from the power company. The input is power consumption data and weather data collected in real time, and the output is the adjusted supply.

[1455] Step 8:

[1456] The server collects the worker's emotional data through the camera and microphone installed on the robot. The emotion engine analyzes facial expressions and voice to detect the worker's stress or relaxation state. The input is the worker's facial expression and voice data, and the output is emotional data.

[1457] Step 9:

[1458] The server adjusts energy consumption based on the acquired emotional data. For example, if a worker is feeling stressed, it readjusts the supply plan to avoid peak power consumption. This reduces worker stress and achieves efficient energy management. The input is emotional data, and the output is the adjusted energy supply plan.

[1459] Through the above steps, the present invention provides an optimal energy management system that effectively manages energy consumption and supply within a factory and also takes into account the emotional state of workers.

[1460] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1461] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1462] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1463] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1464] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1465] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1466] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1467] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1468] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1469] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1470] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1471] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1472] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1473] 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.

[1474] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1475] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1476] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1477] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1478] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1479] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1480] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1481] The following is further disclosed regarding the above embodiment.

[1482] (Claim 1)

[1483] A means of collecting real-time electricity consumption data from homes and businesses;

[1484] a means for obtaining weather data;

[1485] A means for obtaining renewable energy supply forecast data;

[1486] A means of analyzing power consumption patterns based on the collected data;

[1487] a means of analyzing fluctuations in renewable energy supply due to weather variations;

[1488] means for generating a consumption forecasting model;

[1489] A means of integrating consumption forecasts and supply forecasts to simulate the supply and demand balance;

[1490] A means of creating an optimal supply plan;

[1491] A means of communicating supply plans to homes and businesses;

[1492] The system includes a means for implementing supply adjustments.

[1493] (Claim 2)

[1494] A means to detect sudden fluctuations in power consumption and respond immediately,

[1495] a means for monitoring the data collected in real time;

[1496] 10. The system of claim 1, further comprising means for notifying a user in real time.

[1497] (Claim 3)

[1498] 10. The system of claim 1, further comprising means for creating a supply plan that prioritizes the use of renewable energy and supplements supply from the power company as needed.

[1499] "Example 1"

[1500] (Claim 1)

[1501] a means for collecting electricity consumption data in real time;

[1502] a means for obtaining weather information;

[1503] a means for collecting renewable energy supply forecast data;

[1504] a means for analyzing power consumption patterns based on the collected data;

[1505] a means for analyzing fluctuations in renewable energy supply in response to weather variations;

[1506] means for generating a consumption forecasting model;

[1507] A means of integrating consumption forecasts and supply forecasts to simulate the supply and demand balance;

[1508] A means for creating an optimal energy supply plan;

[1509] a means for providing energy supply plans to users;

[1510] a means for adjusting the supply amount;

[1511] A means to monitor energy consumption and supply status in real time

[1512] Including system.

[1513] (Claim 2)

[1514] A means to detect sudden fluctuations in power consumption and respond immediately,

[1515] A way to notify users in real time

[1516] The system of claim 1 further comprising:

[1517] (Claim 3)

[1518] Prioritize the use of renewable energy and take measures to develop an energy supply plan that supplements external supplies in the event of a shortage.

[1519] The system of claim 1 further comprising:

[1520] "Application Example 1"

[1521] (Claim 1)

[1522] A means of collecting real-time electricity consumption data from homes and businesses;

[1523] a means for obtaining weather data;

[1524] A means for obtaining renewable energy supply forecast data;

[1525] A means of analyzing power consumption patterns based on the collected data;

[1526] a means of analyzing fluctuations in renewable energy supply due to weather variations;

[1527] means for generating a consumption forecasting model;

[1528] A means of integrating consumption forecasts and supply forecasts to simulate the supply and demand balance;

[1529] A means of creating an optimal supply plan;

[1530] A means of communicating supply plans to homes and businesses;

[1531] means for effecting supply adjustments;

[1532] Energy consumption data for each piece of equipment in the factory is collected in real time,

[1533] A system including means for optimizing the operating conditions of equipment based on renewable energy supply.

[1534] (Claim 2)

[1535] A means to detect sudden fluctuations in power consumption and respond immediately,

[1536] a means for monitoring the data collected in real time;

[1537] 10. The system of claim 1, further comprising means for notifying a user in real time.

[1538] (Claim 3)

[1539] 10. The system of claim 1, further comprising means for creating a supply plan that prioritizes the use of renewable energy and supplements supply from the power company as needed.

[1540] "Example 2: Combining Emotion Engines"

[1541] (Claim 1)

[1542] A means of collecting real-time energy consumption data for homes and businesses;

[1543] a means for obtaining weather information;

[1544] A means for obtaining renewable resource supply forecast data;

[1545] A means of analyzing energy consumption patterns based on the collected data;

[1546] A means of analyzing fluctuations in renewable energy supply due to weather variations;

[1547] means for generating a consumption forecasting model;

[1548] A means of integrating consumption forecasts and supply forecasts to simulate the supply and demand balance;

[1549] A means of creating an optimal supply plan;

[1550] A means of communicating supply plans to homes and business establishments;

[1551] means for effecting supply adjustments;

[1552] A means for acquiring user emotion data;

[1553] A system including a means for creating an energy management plan based on a user's emotions.

[1554] (Claim 2)

[1555] A means of detecting sudden fluctuations in energy consumption and responding immediately;

[1556] a means for monitoring the data collected in real time;

[1557] 10. The system of claim 1, further comprising means for notifying a user in real time.

[1558] (Claim 3)

[1559] 10. The system of claim 1, further comprising means for creating a supply plan that prioritizes the use of renewable energy and supplements supply from an energy supplier as needed.

[1560] "Application example 2 when combining emotion engines"

[1561] (Claim 1)

[1562] A means of collecting real-time electricity consumption data from homes and businesses;

[1563] a means for obtaining weather data;

[1564] A means for obtaining renewable energy supply forecast data;

[1565] A means of analyzing power consumption patterns based on the collected data;

[1566] a means of analyzing fluctuations in renewable energy supply due to weather variations;

[1567] means for generating a consumption forecasting model;

[1568] A means of integrating consumption forecasts and supply forecasts to simulate the supply and demand balance;

[1569] A means of creating an optimal supply plan;

[1570] A means of communicating supply plans to homes and businesses;

[1571] means for effecting supply adjustments;

[1572] A means for collecting worker emotion data;

[1573] The system includes a means for adjusting energy delivery based on emotion data.

[1574] (Claim 2)

[1575] A means to detect sudden fluctuations in power consumption and respond immediately,

[1576] a means for monitoring the data collected in real time;

[1577] A means of notifying users in real time,

[1578] 10. The system of claim 1, further comprising means for avoiding peaks in energy consumption due to the emotional state of the worker.

[1579] (Claim 3)

[1580] Measures to develop supply plans that prioritize the use of renewable energy and supplement supply from power companies as needed;

[1581] 10. The system of claim 1, further comprising: means for optimizing energy consumption to reduce stress based on emotion data of the worker. [Explanation of symbols]

[1582] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting real-time electricity consumption data from homes and businesses; a means for obtaining weather data; A means for obtaining renewable energy supply forecast data; A means of analyzing power consumption patterns based on the collected data; a means of analyzing fluctuations in renewable energy supply due to weather variations; means for generating a consumption forecasting model; A means of integrating consumption forecasts and supply forecasts to simulate the supply and demand balance; A means of creating an optimal supply plan; A means of communicating supply plans to homes and businesses; The system includes a means for implementing supply adjustments.

2. A means to detect sudden fluctuations in power consumption and respond immediately, a means for monitoring the data collected in real time; The system of claim 1 further comprising means for notifying a user in real time.

3. The system of claim 1 , further comprising means for creating a supply plan that prioritizes the use of renewable energy and supplements supply from the power company as needed.

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