system

Abstract

The system according to this embodiment aims to monitor energy supply and demand in real time and maximize the use of renewable energy. [Solution] The system according to the embodiment comprises a monitoring unit, a balancing unit, an optimization unit, a management unit, and a participation unit. The monitoring unit monitors energy supply and demand in real time. The balancing unit predicts energy demand and adjusts distribution based on the data collected by the monitoring unit. The optimization unit maximizes the use of renewable energy. The management unit efficiently manages the energy storage system. The participation unit involves consumers in energy saving.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, real-time monitoring of energy supply and demand and maximization of renewable energy have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to monitor energy supply and demand in real time and maximize the utilization of renewable energy.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a monitoring unit, a balancing unit, an optimization unit, a management unit, and a participation unit. The monitoring unit monitors energy supply and demand in real time. The balancing unit predicts energy demand and adjusts distribution based on the data collected by the monitoring unit. The optimization unit maximizes the use of renewable energy. The management unit efficiently manages the energy storage system. The participation unit involves consumers in energy conservation. [Effects of the Invention]

[0007] The system according to this embodiment can monitor energy supply and demand in real time and maximize the use of renewable energy. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. 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. Also, the database 24 and the communication I / F 26 are 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).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An energy grid balancer system according to an embodiment of the present invention is a system that optimizes the distribution, storage, and consumption of energy. This system leverages AI algorithms, IoT sensors, and extensive network infrastructure to significantly improve grid stability, promote the integration of renewable energy, and reduce overall energy costs and carbon emissions. The energy grid balancer system monitors energy supply and demand in real time, uses AI to predict energy demand, and adjusts distribution. Furthermore, it maximizes the use of renewable energy and efficiently manages energy storage systems. Finally, it engages consumers in energy conservation. This system improves grid stability, promotes the integration of renewable energy, and reduces energy costs and carbon emissions. Thus, the energy grid balancer system enables optimization of energy supply and demand, maximization of renewable energy use, efficient management of energy storage systems, and consumer participation in energy conservation.

[0029] The energy grid balancer system according to this embodiment comprises a monitoring unit, a balancing unit, an optimization unit, a management unit, and a participation unit. The monitoring unit monitors energy supply and demand in real time. The monitoring unit monitors energy supply and demand in real time, for example, using IoT sensors. The monitoring unit collects energy supply and demand data and provides it to the balancing unit. The balancing unit predicts energy demand based on the data collected by the monitoring unit and adjusts the distribution. The balancing unit predicts energy demand and adjusts the distribution, for example, using an AI algorithm. The balancing unit optimizes the balance between energy supply and demand and improves grid stability. The optimization unit maximizes the use of renewable energy. The optimization unit predicts the amount of renewable energy generated and optimizes the energy supply plan, for example. The optimization unit maximizes the use of renewable energy and reduces energy costs and carbon emissions. The management unit efficiently manages the energy storage system. The management unit optimizes the charging and discharging of the energy storage system, for example, and improves energy efficiency. The management unit monitors the status of the energy storage system in real time and performs optimal management. The participation unit engages consumers in energy conservation. For example, the participation unit provides consumers with incentives for energy conservation and promotes reductions in energy consumption. The participation unit analyzes consumer energy consumption data and proposes optimal energy conservation methods. As a result, the energy grid balancer system according to the embodiment enables optimization of energy supply and demand, maximization of renewable energy utilization, efficient management of the energy storage system, and consumer participation in energy conservation.

[0030] The monitoring unit monitors energy supply and demand in real time. Specifically, it collects energy supply and demand data using IoT sensors and provides this data to the balancing unit. IoT sensors are installed at power plants, substations, and consumer homes and businesses to monitor the flow of electricity in detail. This allows for real-time understanding of energy supply conditions and consumption patterns. For example, it records power plant output, substation load, and consumer power consumption in seconds and transmits it to a central database. The monitoring unit centrally manages this data and can immediately detect abnormal consumption patterns and supply fluctuations. Furthermore, the monitoring unit can accumulate historical data and use it for long-term trend analysis and forecasting. This provides a foundation for maintaining a balance between energy supply and demand and ensuring grid stability.

[0031] The balancing unit predicts energy demand and adjusts distribution based on data collected by the monitoring unit. Specifically, it uses an AI algorithm to predict energy demand and adjust distribution. The AI ​​algorithm analyzes past consumption data, weather information, and economic indicators to predict future energy demand with high accuracy. For example, since cooling demand increases in the summer when temperatures rise, it predicts this and adjusts energy supply in advance. It also takes into account increased demand due to increased economic activity and temporary demand fluctuations due to specific events. Based on these predictions, the balancing unit adjusts the output of power plants to prevent surpluses or shortages in energy supply. Furthermore, the balancing unit utilizes energy storage systems to supplement supply during peak demand and stores surplus energy when demand decreases. This optimizes the balance between energy supply and demand and improves grid stability.

[0032] The optimization unit maximizes the use of renewable energy. Specifically, it predicts the amount of renewable energy generated and optimizes the energy supply plan. Renewable energy includes solar and wind power, and the amount of power generated by these sources fluctuates greatly depending on the weather and season. The optimization unit predicts future power generation based on weather data and past power generation performance. For example, it predicts that the output of solar power will increase on days with continuous sunny weather, and the output of wind power will increase on windy days. Based on this, it formulates an energy supply plan to maximize the use of renewable energy. Furthermore, the optimization unit utilizes an energy storage system to store surplus renewable energy and supply it when demand increases. This reduces energy costs and carbon emissions, enabling sustainable energy use.

[0033] The management department efficiently manages the energy storage system. Specifically, it optimizes the charging and discharging of the energy storage system to improve energy efficiency. The energy storage system includes lithium-ion batteries, flywheels, and compressed air energy storage. The management department monitors the status of these systems in real time and formulates the optimal charging and discharging schedule. For example, it stores energy at night when demand is low and supplies stored energy during the day when demand is high. It also stores surplus energy when renewable energy generation is high and utilizes stored energy when generation is low. This maximizes the efficiency of the energy storage system and improves the stability of the energy supply. Furthermore, the management department formulates a maintenance schedule to extend the lifespan of the energy storage system and performs regular inspections and parts replacements. This ensures the reliability and durability of the energy storage system.

[0034] The participating department will engage consumers in energy conservation. Specifically, it will provide consumers with incentives to conserve energy and promote the reduction of energy consumption. For example, it will offer benefits such as discounts on rates or point rewards to consumers who achieve a certain level of energy conservation. It will also analyze consumer energy consumption data and propose optimal energy conservation methods. For example, it will analyze the energy consumption patterns of consumers' homes and businesses and provide specific advice to reduce peak consumption. Furthermore, the participating department will strengthen communication with consumers and conduct campaigns to raise awareness of the importance and effectiveness of energy conservation. This will raise consumer awareness and promote energy conservation efforts. The participating department will collect feedback from consumers and use it to improve energy conservation programs. For example, based on consumer opinions and requests, it will review the content and method of providing incentives and build more effective energy conservation programs. In this way, the participating department can encourage active consumer participation and achieve reductions in energy consumption.

[0035] The monitoring unit monitors energy supply and demand in real time using IoT sensors. The monitoring unit can monitor energy supply and demand using, for example, a temperature sensor. The monitoring unit can also monitor energy supply and demand using, for example, a power sensor. The monitoring unit can also monitor energy supply and demand using, for example, a smart meter. This makes it possible to monitor energy supply and demand in real time by using IoT sensors. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or without AI. For example, the monitoring unit can input data acquired from IoT sensors into a generating AI and have the generating AI perform energy supply and demand monitoring.

[0036] The balancing unit predicts energy demand and adjusts the distribution based on data collected by the monitoring unit. The balancing unit can, for example, use an AI algorithm to predict energy demand and adjust the distribution. The balancing unit can, for example, use a machine learning algorithm to predict energy demand and adjust the distribution. The balancing unit can, for example, use a deep learning algorithm to predict energy demand and adjust the distribution. This makes it possible to predict energy demand and adjust the distribution. Some or all of the above processing in the balancing unit may be performed using AI, for example, or without AI. For example, the balancing unit can input data acquired from the monitoring unit into a generating AI and have the generating AI perform energy demand prediction and distribution adjustment.

[0037] The optimization unit maximizes the use of renewable energy. For example, the optimization unit can predict the amount of renewable energy generated and optimize the energy supply plan. For example, the optimization unit can predict the amount of solar power generated and optimize the energy supply plan. For example, the optimization unit can predict the amount of wind power generated and optimize the energy supply plan. This makes it possible to maximize the use of renewable energy. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input renewable energy generation data into a generating AI and have the generating AI perform the optimization of the energy supply plan.

[0038] The management unit efficiently manages the energy storage system. For example, the management unit can optimize the charging and discharging of the energy storage system to improve energy efficiency. For example, the management unit can monitor the status of the energy storage system in real time and perform optimal management. For example, the management unit can monitor the degradation status of the energy storage system and determine the optimal maintenance schedule. This enables efficient management of the energy storage system. Some or all of the above processes in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input status data of the energy storage system into a generating AI and have the generating AI perform the management of the energy storage system.

[0039] The participating unit involves consumers in engaging in energy conservation. For example, the participating unit can provide consumers with incentives for energy conservation and promote the reduction of energy consumption. For example, the participating unit can analyze consumers' energy consumption data and propose optimal energy conservation methods. For example, the participating unit can provide consumers with energy conservation education programs and promote the reduction of energy consumption. This enables consumers to participate in energy conservation. Some or all of the above processes in the participating unit may be performed using AI, for example, or without AI. For example, the participating unit can input consumers' energy consumption data into a generating AI and have the generating AI generate suggestions for energy conservation methods.

[0040] The monitoring unit acquires weather data in real time when monitoring energy supply and demand to improve prediction accuracy. For example, the monitoring unit can acquire real-time weather data to predict the amount of solar power generation. For example, the monitoring unit can acquire real-time wind speed data to predict the amount of wind power generation. For example, the monitoring unit can acquire real-time temperature data to predict fluctuations in energy demand. As a result, the accuracy of energy supply and demand predictions is improved by acquiring weather data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input weather data into a generating AI and have the generating AI perform the task of improving the accuracy of energy supply and demand predictions.

[0041] The monitoring unit applies an anomaly detection algorithm based on monitoring data to immediately identify abnormal energy consumption patterns. For example, the monitoring unit can detect abnormal energy consumption patterns based on monitoring data and issue an alert. For example, the monitoring unit can also detect a sudden increase in energy consumption based on monitoring data and identify the cause. For example, the monitoring unit can detect an abnormal decrease in energy consumption based on monitoring data and identify a system failure. This enables a rapid response by immediately identifying abnormal energy consumption patterns. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input monitoring data into a generating AI and have the generating AI identify abnormal energy consumption patterns.

[0042] The monitoring unit, during monitoring, divides energy supply and demand data by region and analyzes region-specific energy consumption patterns. For example, the monitoring unit can analyze energy consumption data by region to identify region-specific consumption patterns. For example, the monitoring unit can analyze energy supply data by region to identify region-specific supply patterns. For example, the monitoring unit can analyze energy demand data by region to identify region-specific demand patterns. By analyzing region-specific energy consumption patterns, region-specific countermeasures become possible. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input energy consumption data by region into a generating AI and have the generating AI perform an analysis of region-specific energy consumption patterns.

[0043] The monitoring unit identifies peak energy consumption times based on monitoring data and proposes peak shifts. For example, the monitoring unit can identify peak energy consumption times based on monitoring data. For example, the monitoring unit can propose peak shifts based on monitoring data to distribute energy consumption. For example, the monitoring unit can propose ways to reduce energy consumption during peak hours based on monitoring data. By identifying peak energy consumption times and proposing peak shifts, energy consumption can be distributed. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input monitoring data into a generating AI and have the generating AI identify peak energy consumption times and propose peak shifts.

[0044] The balancing unit, when forecasting energy demand, refers to past consumption data and considers seasonal demand fluctuations. For example, the balancing unit can forecast seasonal energy demand based on past consumption data. The balancing unit can also forecast seasonal energy supply based on past consumption data. For example, the balancing unit can also forecast the balance between seasonal energy demand and supply based on past consumption data. This makes it possible to forecast energy demand that takes seasonal demand fluctuations into account by referring to past consumption data. Some or all of the above processing in the balancing unit may be performed using AI, for example, or without AI. For example, the balancing unit can input past consumption data into a generating AI and have the generating AI perform an energy demand forecast that takes seasonal demand fluctuations into account.

[0045] The balancing unit incorporates real-time market price data during energy distribution to achieve cost-effective distribution. For example, the balancing unit can achieve cost-effective energy distribution based on real-time market price data. The balancing unit can also minimize the cost of energy supply based on real-time market price data. The balancing unit can also minimize the cost of energy demand based on real-time market price data. As a result, cost-effective energy distribution becomes possible by incorporating real-time market price data. Some or all of the above processing in the balancing unit may be performed using AI, for example, or without AI. For example, the balancing unit can input market price data into a generating AI and have the generating AI perform cost-effective energy distribution.

[0046] The balancing unit analyzes industry-specific consumption data and considers the demand characteristics of each industry when forecasting energy demand. For example, the balancing unit can analyze industry-specific consumption data and forecast energy demand for each industry. For example, the balancing unit can analyze industry-specific consumption data and forecast energy supply for each industry. For example, the balancing unit can analyze industry-specific consumption data and forecast the balance between energy demand and supply for each industry. This makes it possible to forecast energy demand that takes into account the demand characteristics of each industry by analyzing industry-specific consumption data. Some or all of the above processing in the balancing unit may be performed using AI, for example, or without AI. For example, the balancing unit can input industry-specific consumption data into a generating AI and have the generating AI perform an energy demand forecast that takes into account the demand characteristics of each industry.

[0047] The balancing unit, when distributing energy, considers the diversity of energy sources and selects the optimal source. For example, the balancing unit can consider the diversity of energy sources and select the optimal source. For example, the balancing unit can consider the diversity of energy sources and maximize the use of renewable energy. For example, the balancing unit can consider the diversity of energy sources and select a cost-effective source. In this way, by considering the diversity of energy sources, the optimal source can be selected. Some or all of the above processing in the balancing unit may be performed using AI, for example, or without AI. For example, the balancing unit can input energy source data into a generating AI and have the generating AI perform the selection of the optimal source.

[0048] The optimization unit incorporates weather forecast data when using renewable energy and predicts fluctuations in power generation. For example, the optimization unit can predict the amount of solar power generation based on weather forecast data. For example, the optimization unit can also predict the amount of wind power generation based on weather forecast data. For example, the optimization unit can also predict fluctuations in renewable energy generation based on weather forecast data. In this way, fluctuations in power generation can be predicted by incorporating weather forecast data. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input weather forecast data into a generating AI and have the generating AI perform a prediction of fluctuations in power generation.

[0049] The optimization unit monitors the state of the energy storage system in real time when renewable energy is used and determines the optimal timing for use. For example, the optimization unit can monitor the state of the energy storage system in real time and determine the optimal charging timing. For example, the optimization unit can monitor the state of the energy storage system in real time and determine the optimal discharge timing. For example, the optimization unit can monitor the state of the energy storage system in real time and determine the optimal timing for use. In this way, the optimal timing for use can be determined by monitoring the state of the energy storage system in real time. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input energy storage system state data to a generating AI and have the generating AI perform the determination of the optimal timing for use.

[0050] The optimization unit, when utilizing renewable energy, considers regional energy demand and creates a region-specific optimization plan. For example, the optimization unit can consider regional energy demand and create a region-specific optimization plan. For example, the optimization unit can also consider regional energy supply and create a region-specific optimization plan. For example, the optimization unit can also consider the balance between regional energy demand and supply and create a region-specific optimization plan. In this way, a region-specific optimization plan can be created by considering regional energy demand. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input regional energy demand data into a generating AI and have the generating AI create a region-specific optimization plan.

[0051] The optimization unit, when utilizing renewable energy, considers the diversity of energy sources and selects the optimal source. For example, the optimization unit can consider the diversity of energy sources and select the optimal source. For example, the optimization unit can diversify renewable energy sources and select the optimal combination. For example, the optimization unit can consider the diversity of energy sources and select a cost-effective source. In this way, by considering the diversity of energy sources, the optimal source can be selected. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input energy source data into a generating AI and have the generating AI perform the selection of the optimal source.

[0052] The management department monitors the battery degradation status in real time and determines the optimal maintenance schedule when managing the energy storage system. For example, the management department can monitor the battery degradation status in real time and determine the optimal maintenance schedule. For example, the management department can monitor the battery degradation status in real time and issue an alert if degradation is progressing. For example, the management department can monitor the battery degradation status in real time and suggest replacement if degradation is progressing. In this way, the optimal maintenance schedule can be determined by monitoring the battery degradation status in real time. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input battery degradation status data into a generating AI and have the generating AI determine the optimal maintenance schedule.

[0053] The management unit, when managing the energy storage system, considers the balance between energy supply and demand and determines the optimal charging and discharging timing. For example, the management unit can determine the optimal charging timing by considering the balance between energy supply and demand. The management unit can also determine the optimal discharging timing by considering the balance between energy supply and demand. The management unit can also determine the optimal charging and discharging timing by considering the balance between energy supply and demand. In this way, the optimal charging and discharging timing can be determined by considering the balance between energy supply and demand. Some or all of the above processing in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input energy supply and demand data into a generating AI and have the generating AI perform the determination of the optimal charging and discharging timing.

[0054] The management department, when managing the energy storage system, considers the energy demand of each region and creates a region-specific management plan. For example, the management department can create a region-specific management plan by considering the energy demand of each region. The management department can also create a region-specific management plan by considering the energy supply of each region. The management department can also create a region-specific management plan by considering the balance between energy demand and supply of each region. In this way, a region-specific management plan can be created by considering the energy demand of each region. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input regional energy demand data into a generating AI and have the generating AI create a region-specific management plan.

[0055] The management department, when managing the energy storage system, considers the diversity of energy sources and selects the optimal source. For example, the management department can consider the diversity of energy sources and select the optimal source. For example, the management department can consider the diversity of energy sources and maximize the use of renewable energy. For example, the management department can consider the diversity of energy sources and select a cost-effective source. In this way, by considering the diversity of energy sources, the optimal source can be selected. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input energy source data into a generating AI and have the generating AI perform the selection of the optimal source.

[0056] The participating unit, when a user participates in energy saving, refers to the user's past consumption data and proposes the optimal energy-saving method. For example, the participating unit can propose the optimal energy-saving method based on the user's past consumption data. For example, the participating unit can identify peak energy consumption times based on the user's past consumption data and propose saving methods. For example, the participating unit can propose ways to reduce wasted energy consumption based on the user's past consumption data. In this way, the optimal energy-saving method can be proposed by referring to the user's past consumption data. Some or all of the above processing in the participating unit may be performed using AI, for example, or without AI. For example, the participating unit can input the user's past consumption data into a generating AI and have the generating AI execute a proposal for the optimal energy-saving method.

[0057] The participant unit, when participating in energy saving, takes into account the user's lifestyle and provides a customized energy saving plan. For example, the participant unit can provide a customized energy saving plan that takes into account the user's lifestyle. For example, the participant unit can also provide a reasonable energy saving plan that takes into account the user's lifestyle. For example, the participant unit can also provide an effective energy saving plan that takes into account the user's lifestyle. In this way, a customized energy saving plan can be provided by taking into account the user's lifestyle. Some or all of the above processing in the participant unit may be performed using AI, for example, or without AI. For example, the participant unit can input the user's lifestyle data into a generating AI and have the generating AI perform the task of providing a customized energy saving plan.

[0058] When participating in energy conservation, the participating unit considers the energy demand of each region and creates a region-specific conservation plan. For example, the participating unit can consider the energy demand of each region and create a region-specific energy conservation plan. For example, the participating unit can consider the energy supply of each region and create a region-specific energy conservation plan. For example, the participating unit can consider the balance between energy demand and supply of each region and create a region-specific energy conservation plan. In this way, by considering the energy demand of each region, a region-specific conservation plan can be created. Some or all of the above processing in the participating unit may be performed using AI, for example, or without AI. For example, the participating unit can input regional energy demand data into a generating AI and have the generating AI create a region-specific conservation plan.

[0059] The participating unit, when participating in energy conservation, considers the diversity of energy sources and proposes the optimal conservation method. For example, the participating unit can propose the optimal energy conservation method considering the diversity of energy sources. For example, the participating unit can propose a conservation method that maximizes the use of renewable energy considering the diversity of energy sources. For example, the participating unit can propose a cost-effective energy conservation method considering the diversity of energy sources. In this way, by considering the diversity of energy sources, the optimal conservation method can be proposed. Some or all of the above processing in the participating unit may be performed using AI, for example, or without AI. For example, the participating unit can input energy source data into a generating AI and have the generating AI execute a proposal for the optimal conservation method.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The energy grid balancer system may also include an anomaly detection unit. The anomaly detection unit analyzes energy supply and demand data in real time and detects abnormal patterns. For example, the anomaly detection unit can detect sudden increases or decreases in energy consumption and issue an alert. The anomaly detection unit can also detect unstable energy supply conditions and prompt a rapid response. This allows for the immediate identification of abnormal energy consumption patterns and supply instability, enabling a swift response. Some or all of the above-described processes in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can input energy supply and demand data into a generating AI and have the generating AI perform the detection of abnormal patterns.

[0062] The energy grid balancer system may further include a forecasting unit. The forecasting unit analyzes historical energy consumption data and predicts future energy demand. For example, the forecasting unit can analyze seasonal energy consumption patterns and predict seasonal demand fluctuations. It can also consider the impact of specific events and holidays and predict peak demand. This makes it possible to accurately predict future energy demand based on historical data and optimize energy supply planning. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input historical energy consumption data into a generating AI and have the generating AI perform future demand forecasts.

[0063] The energy grid balancer system may further include a region-specific unit. This unit analyzes energy consumption patterns for each region and creates region-specific energy supply plans. For example, the region-specific unit can analyze energy demand data for each region and identify region-specific demand patterns. It can also analyze the use of renewable energy in each region and create an optimal supply plan. This makes it possible to create an optimal energy supply plan that takes into account region-specific energy consumption patterns. Some or all of the above-described processes in the region-specific unit may be performed using AI, for example, or without AI. For example, the region-specific unit can input energy consumption data for each region into a generating AI and have the generating AI create a region-specific supply plan.

[0064] The energy grid balancer system may further include a market price-linked unit. This unit incorporates real-time market price data and adjusts energy supply and demand. For example, it can create a cost-effective energy supply plan based on real-time market price data. It can also suggest shifting demand if market prices surge during peak energy demand periods. This enables cost-effective energy supply and demand adjustments by utilizing real-time market price data. Some or all of the above-described processes in the market price-linked unit may be performed using AI, for example, or without AI. For instance, the market price-linked unit can input market price data into a generating AI and have the generating AI perform the energy supply and demand adjustments.

[0065] The energy grid balancer system may further include a user education department. This department educates users about the importance of energy conservation and promotes the reduction of energy consumption. For example, the user education department can provide online courses explaining how to reduce energy consumption. It can also offer incentives to users who successfully reduce energy consumption. This helps educate users about the importance of energy conservation and promotes the reduction of energy consumption. Some or all of the above-described processes in the user education department may be performed using AI, for example, or without AI. For example, the user education department can input energy consumption data into a generating AI and have the generating AI provide the educational program.

[0066] The energy grid balancer system may further include a weather-linked unit. The weather-linked unit takes in real-time weather data and adjusts energy supply and demand. For example, the weather-linked unit can predict the amount of electricity generated by solar and wind power based on real-time weather data. It can also predict fluctuations in energy demand in response to temperature fluctuations and adjust the supply plan. This makes it possible to maximize the use of renewable energy by adjusting energy supply and demand using weather data. Some or all of the above processing in the weather-linked unit may be performed using AI, for example, or without AI. For example, the weather-linked unit can input weather data into a generating AI and have the generating AI perform the adjustment of energy supply and demand.

[0067] The energy grid balancer system may further include an energy storage optimization unit. The energy storage optimization unit monitors the status of the energy storage system in real time and determines the optimal charging and discharging timing. For example, the energy storage optimization unit can monitor the degradation status of the energy storage system and determine the optimal maintenance schedule. The energy storage optimization unit can also determine the optimal charging and discharging timing by considering the balance between energy supply and demand. This enables efficient management of the energy storage system. Some or all of the above-described processes in the energy storage optimization unit may be performed using AI, for example, or without AI. For example, the energy storage optimization unit can input status data of the energy storage system into a generating AI and have the generating AI determine the optimal charging and discharging timing.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The monitoring unit monitors energy supply and demand in real time. For example, it collects energy supply and demand data using IoT sensors and provides this data to the balancing unit. Step 2: The balancing unit predicts energy demand based on data collected by the monitoring unit and adjusts the distribution. For example, it uses AI algorithms to predict energy demand, optimize the balance between energy supply and demand, and improve grid stability. Step 3: The optimization unit maximizes the use of renewable energy. For example, it reduces energy costs and carbon emissions by predicting renewable energy generation and optimizing energy supply plans. Step 4: The management department efficiently manages the energy storage system. For example, it optimizes the charging and discharging of the energy storage system to improve energy efficiency. The management department monitors the status of the energy storage system in real time and performs optimal management. Step 5: The participating department engages consumers in energy conservation. For example, it provides consumers with incentives to conserve energy and encourages them to reduce energy consumption. The participating department analyzes consumer energy consumption data and suggests optimal energy conservation methods.

[0070] (Example of form 2) An energy grid balancer system according to an embodiment of the present invention is a system that optimizes the distribution, storage, and consumption of energy. This system leverages AI algorithms, IoT sensors, and extensive network infrastructure to significantly improve grid stability, promote the integration of renewable energy, and reduce overall energy costs and carbon emissions. The energy grid balancer system monitors energy supply and demand in real time, uses AI to predict energy demand, and adjusts distribution. Furthermore, it maximizes the use of renewable energy and efficiently manages energy storage systems. Finally, it engages consumers in energy conservation. This system improves grid stability, promotes the integration of renewable energy, and reduces energy costs and carbon emissions. Thus, the energy grid balancer system enables optimization of energy supply and demand, maximization of renewable energy use, efficient management of energy storage systems, and consumer participation in energy conservation.

[0071] The energy grid balancer system according to this embodiment comprises a monitoring unit, a balancing unit, an optimization unit, a management unit, and a participation unit. The monitoring unit monitors energy supply and demand in real time. The monitoring unit monitors energy supply and demand in real time, for example, using IoT sensors. The monitoring unit collects energy supply and demand data and provides it to the balancing unit. The balancing unit predicts energy demand based on the data collected by the monitoring unit and adjusts the distribution. The balancing unit predicts energy demand and adjusts the distribution, for example, using an AI algorithm. The balancing unit optimizes the balance between energy supply and demand and improves grid stability. The optimization unit maximizes the use of renewable energy. The optimization unit predicts the amount of renewable energy generated and optimizes the energy supply plan, for example. The optimization unit maximizes the use of renewable energy and reduces energy costs and carbon emissions. The management unit efficiently manages the energy storage system. The management unit optimizes the charging and discharging of the energy storage system, for example, and improves energy efficiency. The management unit monitors the status of the energy storage system in real time and performs optimal management. The participation unit engages consumers in energy conservation. For example, the participation unit provides consumers with incentives for energy conservation and promotes reductions in energy consumption. The participation unit analyzes consumer energy consumption data and proposes optimal energy conservation methods. As a result, the energy grid balancer system according to the embodiment enables optimization of energy supply and demand, maximization of renewable energy utilization, efficient management of the energy storage system, and consumer participation in energy conservation.

[0072] The monitoring unit monitors energy supply and demand in real time. Specifically, it collects energy supply and demand data using IoT sensors and provides this data to the balancing unit. IoT sensors are installed at power plants, substations, and consumer homes and businesses to monitor the flow of electricity in detail. This allows for real-time understanding of energy supply conditions and consumption patterns. For example, it records power plant output, substation load, and consumer power consumption in seconds and transmits it to a central database. The monitoring unit centrally manages this data and can immediately detect abnormal consumption patterns and supply fluctuations. Furthermore, the monitoring unit can accumulate historical data and use it for long-term trend analysis and forecasting. This provides a foundation for maintaining a balance between energy supply and demand and ensuring grid stability.

[0073] The balancing unit predicts energy demand and adjusts distribution based on data collected by the monitoring unit. Specifically, it uses an AI algorithm to predict energy demand and adjust distribution. The AI ​​algorithm analyzes past consumption data, weather information, and economic indicators to predict future energy demand with high accuracy. For example, since cooling demand increases in the summer when temperatures rise, it predicts this and adjusts energy supply in advance. It also takes into account increased demand due to increased economic activity and temporary demand fluctuations due to specific events. Based on these predictions, the balancing unit adjusts the output of power plants to prevent surpluses or shortages in energy supply. Furthermore, the balancing unit utilizes energy storage systems to supplement supply during peak demand and stores surplus energy when demand decreases. This optimizes the balance between energy supply and demand and improves grid stability.

[0074] The optimization unit maximizes the use of renewable energy. Specifically, it predicts the amount of renewable energy generated and optimizes the energy supply plan. Renewable energy includes solar and wind power, and the amount of power generated by these sources fluctuates greatly depending on the weather and season. The optimization unit predicts future power generation based on weather data and past power generation performance. For example, it predicts that the output of solar power will increase on days with continuous sunny weather, and the output of wind power will increase on windy days. Based on this, it formulates an energy supply plan to maximize the use of renewable energy. Furthermore, the optimization unit utilizes an energy storage system to store surplus renewable energy and supply it when demand increases. This reduces energy costs and carbon emissions, enabling sustainable energy use.

[0075] The management department efficiently manages the energy storage system. Specifically, it optimizes the charging and discharging of the energy storage system to improve energy efficiency. The energy storage system includes lithium-ion batteries, flywheels, and compressed air energy storage. The management department monitors the status of these systems in real time and formulates the optimal charging and discharging schedule. For example, it stores energy at night when demand is low and supplies stored energy during the day when demand is high. It also stores surplus energy when renewable energy generation is high and utilizes stored energy when generation is low. This maximizes the efficiency of the energy storage system and improves the stability of the energy supply. Furthermore, the management department formulates a maintenance schedule to extend the lifespan of the energy storage system and performs regular inspections and parts replacements. This ensures the reliability and durability of the energy storage system.

[0076] The participating department will engage consumers in energy conservation. Specifically, it will provide consumers with incentives to conserve energy and promote the reduction of energy consumption. For example, it will offer benefits such as discounts on rates or point rewards to consumers who achieve a certain level of energy conservation. It will also analyze consumer energy consumption data and propose optimal energy conservation methods. For example, it will analyze the energy consumption patterns of consumers' homes and businesses and provide specific advice to reduce peak consumption. Furthermore, the participating department will strengthen communication with consumers and conduct campaigns to raise awareness of the importance and effectiveness of energy conservation. This will raise consumer awareness and promote energy conservation efforts. The participating department will collect feedback from consumers and use it to improve energy conservation programs. For example, based on consumer opinions and requests, it will review the content and method of providing incentives and build more effective energy conservation programs. In this way, the participating department can encourage active consumer participation and achieve reductions in energy consumption.

[0077] The monitoring unit monitors energy supply and demand in real time using IoT sensors. The monitoring unit can monitor energy supply and demand using, for example, a temperature sensor. The monitoring unit can also monitor energy supply and demand using, for example, a power sensor. The monitoring unit can also monitor energy supply and demand using, for example, a smart meter. This makes it possible to monitor energy supply and demand in real time by using IoT sensors. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or without AI. For example, the monitoring unit can input data acquired from IoT sensors into a generating AI and have the generating AI perform energy supply and demand monitoring.

[0078] The balancing unit predicts energy demand and adjusts the distribution based on data collected by the monitoring unit. The balancing unit can, for example, use an AI algorithm to predict energy demand and adjust the distribution. The balancing unit can, for example, use a machine learning algorithm to predict energy demand and adjust the distribution. The balancing unit can, for example, use a deep learning algorithm to predict energy demand and adjust the distribution. This makes it possible to predict energy demand and adjust the distribution. Some or all of the above processing in the balancing unit may be performed using AI, for example, or without AI. For example, the balancing unit can input data acquired from the monitoring unit into a generating AI and have the generating AI perform energy demand prediction and distribution adjustment.

[0079] The optimization unit maximizes the use of renewable energy. For example, the optimization unit can predict the amount of renewable energy generated and optimize the energy supply plan. For example, the optimization unit can predict the amount of solar power generated and optimize the energy supply plan. For example, the optimization unit can predict the amount of wind power generated and optimize the energy supply plan. This makes it possible to maximize the use of renewable energy. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input renewable energy generation data into a generating AI and have the generating AI perform the optimization of the energy supply plan.

[0080] The management unit efficiently manages the energy storage system. For example, the management unit can optimize the charging and discharging of the energy storage system to improve energy efficiency. For example, the management unit can monitor the status of the energy storage system in real time and perform optimal management. For example, the management unit can monitor the degradation status of the energy storage system and determine the optimal maintenance schedule. This enables efficient management of the energy storage system. Some or all of the above processes in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input status data of the energy storage system into a generating AI and have the generating AI perform the management of the energy storage system.

[0081] The participating unit involves consumers in engaging in energy conservation. For example, the participating unit can provide consumers with incentives for energy conservation and promote the reduction of energy consumption. For example, the participating unit can analyze consumers' energy consumption data and propose optimal energy conservation methods. For example, the participating unit can provide consumers with energy conservation education programs and promote the reduction of energy consumption. This enables consumers to participate in energy conservation. Some or all of the above processes in the participating unit may be performed using AI, for example, or without AI. For example, the participating unit can input consumers' energy consumption data into a generating AI and have the generating AI generate suggestions for energy conservation methods.

[0082] The monitoring unit estimates the user's emotions and adjusts the monitoring frequency of energy supply and demand based on the estimated emotions. For example, if the user is stressed, the monitoring unit can set the monitoring frequency low and reduce notifications of energy consumption. For example, if the user is relaxed, the monitoring unit can set the monitoring frequency high and provide detailed energy consumption data. For example, if the user is in a hurry, the monitoring unit can set the monitoring frequency to a moderate level and provide only the necessary information. This allows for more appropriate monitoring of energy supply and demand by adjusting the monitoring frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI or not using AI. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI adjust the monitoring frequency of energy supply and demand.

[0083] The monitoring unit acquires weather data in real time when monitoring energy supply and demand to improve prediction accuracy. For example, the monitoring unit can acquire real-time weather data to predict the amount of solar power generation. For example, the monitoring unit can acquire real-time wind speed data to predict the amount of wind power generation. For example, the monitoring unit can acquire real-time temperature data to predict fluctuations in energy demand. As a result, the accuracy of energy supply and demand predictions is improved by acquiring weather data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input weather data into a generating AI and have the generating AI perform the task of improving the accuracy of energy supply and demand predictions.

[0084] The monitoring unit applies an anomaly detection algorithm based on monitoring data to immediately identify abnormal energy consumption patterns. For example, the monitoring unit can detect abnormal energy consumption patterns based on monitoring data and issue an alert. For example, the monitoring unit can also detect a sudden increase in energy consumption based on monitoring data and identify the cause. For example, the monitoring unit can detect an abnormal decrease in energy consumption based on monitoring data and identify a system failure. This enables a rapid response by immediately identifying abnormal energy consumption patterns. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input monitoring data into a generating AI and have the generating AI identify abnormal energy consumption patterns.

[0085] The monitoring unit estimates the user's emotions and adjusts the display method of the monitoring data based on the estimated user emotions. For example, if the user is tense, the monitoring unit can provide a simple and highly visible display method. For example, if the user is relaxed, the monitoring unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the monitoring unit can also provide a display method that gets straight to the point. By adjusting the display method according to the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI adjust the display method of the monitoring data.

[0086] The monitoring unit, during monitoring, divides energy supply and demand data by region and analyzes region-specific energy consumption patterns. For example, the monitoring unit can analyze energy consumption data by region to identify region-specific consumption patterns. For example, the monitoring unit can analyze energy supply data by region to identify region-specific supply patterns. For example, the monitoring unit can analyze energy demand data by region to identify region-specific demand patterns. By analyzing region-specific energy consumption patterns, region-specific countermeasures become possible. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input energy consumption data by region into a generating AI and have the generating AI perform an analysis of region-specific energy consumption patterns.

[0087] The monitoring unit identifies peak energy consumption times based on monitoring data and proposes peak shifts. For example, the monitoring unit can identify peak energy consumption times based on monitoring data. For example, the monitoring unit can propose peak shifts based on monitoring data to distribute energy consumption. For example, the monitoring unit can propose ways to reduce energy consumption during peak hours based on monitoring data. By identifying peak energy consumption times and proposing peak shifts, energy consumption can be distributed. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input monitoring data into a generating AI and have the generating AI identify peak energy consumption times and propose peak shifts.

[0088] The balancing unit estimates the user's emotions and adjusts the priority of energy allocation based on the estimated emotions. For example, if the user is stressed, the balancing unit can set the priority of energy allocation lower and reduce notifications about energy consumption. For example, if the user is relaxed, the balancing unit can set the priority of energy allocation higher and provide detailed energy consumption data. For example, if the user is in a hurry, the balancing unit can set the priority of energy allocation to a medium level and provide only the necessary information. This allows for more appropriate energy allocation by adjusting the priority of energy allocation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the balancing unit may be performed using AI or not using AI. For example, the balancing unit can input user emotion data into a generative AI and have the generative AI adjust the priority of energy allocation.

[0089] The balancing unit, when forecasting energy demand, refers to past consumption data and considers seasonal demand fluctuations. For example, the balancing unit can forecast seasonal energy demand based on past consumption data. The balancing unit can also forecast seasonal energy supply based on past consumption data. For example, the balancing unit can also forecast the balance between seasonal energy demand and supply based on past consumption data. This makes it possible to forecast energy demand that takes seasonal demand fluctuations into account by referring to past consumption data. Some or all of the above processing in the balancing unit may be performed using AI, for example, or without AI. For example, the balancing unit can input past consumption data into a generating AI and have the generating AI perform an energy demand forecast that takes seasonal demand fluctuations into account.

[0090] The balancing unit incorporates real-time market price data during energy distribution to achieve cost-effective distribution. For example, the balancing unit can achieve cost-effective energy distribution based on real-time market price data. The balancing unit can also minimize the cost of energy supply based on real-time market price data. The balancing unit can also minimize the cost of energy demand based on real-time market price data. As a result, cost-effective energy distribution becomes possible by incorporating real-time market price data. Some or all of the above processing in the balancing unit may be performed using AI, for example, or without AI. For example, the balancing unit can input market price data into a generating AI and have the generating AI perform cost-effective energy distribution.

[0091] The balancing unit estimates the user's emotions and adjusts the energy distribution notification method based on the estimated emotions. For example, if the user is stressed, the balancing unit can provide a simple and highly visible notification method. For example, if the user is relaxed, the balancing unit can provide a notification method that includes detailed information. For example, if the user is in a hurry, the balancing unit can provide a notification method that gets straight to the point. By adjusting the notification method according to the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the balancing unit may be performed using AI, for example, or not using AI. For example, the balancing unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the energy distribution notification method.

[0092] The balancing unit analyzes industry-specific consumption data and considers the demand characteristics of each industry when forecasting energy demand. For example, the balancing unit can analyze industry-specific consumption data and forecast energy demand for each industry. For example, the balancing unit can analyze industry-specific consumption data and forecast energy supply for each industry. For example, the balancing unit can analyze industry-specific consumption data and forecast the balance between energy demand and supply for each industry. This makes it possible to forecast energy demand that takes into account the demand characteristics of each industry by analyzing industry-specific consumption data. Some or all of the above processing in the balancing unit may be performed using AI, for example, or without AI. For example, the balancing unit can input industry-specific consumption data into a generating AI and have the generating AI perform an energy demand forecast that takes into account the demand characteristics of each industry.

[0093] The balancing unit, when distributing energy, considers the diversity of energy sources and selects the optimal source. For example, the balancing unit can consider the diversity of energy sources and select the optimal source. For example, the balancing unit can consider the diversity of energy sources and maximize the use of renewable energy. For example, the balancing unit can consider the diversity of energy sources and select a cost-effective source. In this way, by considering the diversity of energy sources, the optimal source can be selected. Some or all of the above processing in the balancing unit may be performed using AI, for example, or without AI. For example, the balancing unit can input energy source data into a generating AI and have the generating AI perform the selection of the optimal source.

[0094] The optimization unit estimates the user's emotions and adjusts the renewable energy usage plan based on the estimated emotions. For example, if the user is stressed, the optimization unit can set the renewable energy usage plan low and reduce energy consumption notifications. For example, if the user is relaxed, the optimization unit can set the renewable energy usage plan high and provide detailed energy consumption data. For example, if the user is in a hurry, the optimization unit can set the renewable energy usage plan to a medium level and provide only the necessary information. This allows for more appropriate energy use by adjusting the renewable energy usage plan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI or not using AI. For example, the optimization unit can input user emotion data into a generative AI and have the generative AI adjust the renewable energy usage plan.

[0095] The optimization unit incorporates weather forecast data when using renewable energy and predicts fluctuations in power generation. For example, the optimization unit can predict the amount of solar power generation based on weather forecast data. For example, the optimization unit can also predict the amount of wind power generation based on weather forecast data. For example, the optimization unit can also predict fluctuations in renewable energy generation based on weather forecast data. In this way, fluctuations in power generation can be predicted by incorporating weather forecast data. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input weather forecast data into a generating AI and have the generating AI perform a prediction of fluctuations in power generation.

[0096] The optimization unit monitors the state of the energy storage system in real time when renewable energy is used and determines the optimal timing for use. For example, the optimization unit can monitor the state of the energy storage system in real time and determine the optimal charging timing. For example, the optimization unit can monitor the state of the energy storage system in real time and determine the optimal discharge timing. For example, the optimization unit can monitor the state of the energy storage system in real time and determine the optimal timing for use. In this way, the optimal timing for use can be determined by monitoring the state of the energy storage system in real time. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input energy storage system state data to a generating AI and have the generating AI perform the determination of the optimal timing for use.

[0097] The optimization unit estimates the user's emotions and adjusts the method of notifying the user of renewable energy usage based on the estimated emotions. For example, if the user is stressed, the optimization unit can provide a simple and highly visible notification method. For example, if the user is relaxed, the optimization unit can provide a notification method that includes detailed information. For example, if the user is in a hurry, the optimization unit can provide a notification method that gets straight to the point. By adjusting the notification method according to the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input user emotion data into the generative AI and have the generative AI adjust the method of notifying the user of renewable energy usage.

[0098] The optimization unit, when utilizing renewable energy, considers regional energy demand and creates a region-specific optimization plan. For example, the optimization unit can consider regional energy demand and create a region-specific optimization plan. For example, the optimization unit can also consider regional energy supply and create a region-specific optimization plan. For example, the optimization unit can also consider the balance between regional energy demand and supply and create a region-specific optimization plan. In this way, a region-specific optimization plan can be created by considering regional energy demand. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input regional energy demand data into a generating AI and have the generating AI create a region-specific optimization plan.

[0099] The optimization unit, when utilizing renewable energy, considers the diversity of energy sources and selects the optimal source. For example, the optimization unit can consider the diversity of energy sources and select the optimal source. For example, the optimization unit can diversify renewable energy sources and select the optimal combination. For example, the optimization unit can consider the diversity of energy sources and select a cost-effective source. In this way, by considering the diversity of energy sources, the optimal source can be selected. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input energy source data into a generating AI and have the generating AI perform the selection of the optimal source.

[0100] The management unit estimates the user's emotions and adjusts the management method of the energy storage system based on the estimated emotions. For example, if the user is stressed, the management unit can simplify the management method of the energy storage system and reduce notifications. For example, if the user is relaxed, the management unit can provide detailed management instructions and detailed notifications about the status of the energy storage system. For example, if the user is in a hurry, the management unit can set the management method of the energy storage system to a moderate level and provide only the necessary information. This allows for more appropriate management of the energy storage system by adjusting the management method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI or not using AI. For example, the management unit can input user emotion data into a generative AI and have the generative AI adjust the management method of the energy storage system.

[0101] The management department monitors the battery degradation status in real time and determines the optimal maintenance schedule when managing the energy storage system. For example, the management department can monitor the battery degradation status in real time and determine the optimal maintenance schedule. For example, the management department can monitor the battery degradation status in real time and issue an alert if degradation is progressing. For example, the management department can monitor the battery degradation status in real time and suggest replacement if degradation is progressing. In this way, the optimal maintenance schedule can be determined by monitoring the battery degradation status in real time. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input battery degradation status data into a generating AI and have the generating AI determine the optimal maintenance schedule.

[0102] The management unit, when managing the energy storage system, considers the balance between energy supply and demand and determines the optimal charging and discharging timing. For example, the management unit can determine the optimal charging timing by considering the balance between energy supply and demand. The management unit can also determine the optimal discharging timing by considering the balance between energy supply and demand. The management unit can also determine the optimal charging and discharging timing by considering the balance between energy supply and demand. In this way, the optimal charging and discharging timing can be determined by considering the balance between energy supply and demand. Some or all of the above processing in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input energy supply and demand data into a generating AI and have the generating AI perform the determination of the optimal charging and discharging timing.

[0103] The management unit estimates the user's emotions and adjusts the method of notifying the user of the energy storage system's status based on the estimated emotions. For example, if the user is stressed, the management unit can provide a simple and highly visible notification method. For example, if the user is relaxed, the management unit can provide a notification method that includes detailed information. For example, if the user is in a hurry, the management unit can provide a notification method that gets straight to the point. By adjusting the notification method according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI or not using AI. For example, the management unit can input user emotion data into a generative AI and have the generative AI adjust the method of notifying the user of the energy storage system's status.

[0104] The management department, when managing the energy storage system, considers the energy demand of each region and creates a region-specific management plan. For example, the management department can create a region-specific management plan by considering the energy demand of each region. The management department can also create a region-specific management plan by considering the energy supply of each region. The management department can also create a region-specific management plan by considering the balance between energy demand and supply of each region. In this way, a region-specific management plan can be created by considering the energy demand of each region. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input regional energy demand data into a generating AI and have the generating AI create a region-specific management plan.

[0105] The management department, when managing the energy storage system, considers the diversity of energy sources and selects the optimal source. For example, the management department can consider the diversity of energy sources and select the optimal source. For example, the management department can consider the diversity of energy sources and maximize the use of renewable energy. For example, the management department can consider the diversity of energy sources and select a cost-effective source. In this way, by considering the diversity of energy sources, the optimal source can be selected. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input energy source data into a generating AI and have the generating AI perform the selection of the optimal source.

[0106] The participant unit estimates the user's emotions and adjusts the energy-saving participation method based on the estimated emotions. For example, if the user is stressed, the participant unit can suggest a simple energy-saving method. For example, if the user is relaxed, the participant unit can suggest a more detailed energy-saving method. For example, if the user is in a hurry, the participant unit can suggest an energy-saving method that can be implemented quickly. This allows for more appropriate energy saving by adjusting the participation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the participant unit may be performed using AI or not. For example, the participant unit can input user emotion data into a generative AI and have the generative AI adjust the energy-saving participation method.

[0107] The participating unit, when a user participates in energy saving, refers to the user's past consumption data and proposes the optimal energy-saving method. For example, the participating unit can propose the optimal energy-saving method based on the user's past consumption data. For example, the participating unit can identify peak energy consumption times based on the user's past consumption data and propose saving methods. For example, the participating unit can propose ways to reduce wasted energy consumption based on the user's past consumption data. In this way, the optimal energy-saving method can be proposed by referring to the user's past consumption data. Some or all of the above processing in the participating unit may be performed using AI, for example, or without AI. For example, the participating unit can input the user's past consumption data into a generating AI and have the generating AI execute a proposal for the optimal energy-saving method.

[0108] The participant unit, when participating in energy saving, takes into account the user's lifestyle and provides a customized energy saving plan. For example, the participant unit can provide a customized energy saving plan that takes into account the user's lifestyle. For example, the participant unit can also provide a reasonable energy saving plan that takes into account the user's lifestyle. For example, the participant unit can also provide an effective energy saving plan that takes into account the user's lifestyle. In this way, a customized energy saving plan can be provided by taking into account the user's lifestyle. Some or all of the above processing in the participant unit may be performed using AI, for example, or without AI. For example, the participant unit can input the user's lifestyle data into a generating AI and have the generating AI perform the task of providing a customized energy saving plan.

[0109] The participating unit estimates the user's emotions and adjusts the energy-saving notification method based on the estimated emotions. For example, if the user is stressed, the participating unit can provide a simple and highly visible notification method. For example, if the user is relaxed, the participating unit can provide a notification method that includes detailed information. For example, if the user is in a hurry, the participating unit can provide a notification method that gets straight to the point. This allows for more appropriate information to be provided by adjusting the notification method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the participating unit may be performed using AI or not using AI. For example, the participating unit can input user emotion data into a generative AI and have the generative AI adjust the energy-saving notification method.

[0110] When participating in energy conservation, the participating unit considers the energy demand of each region and creates a region-specific conservation plan. For example, the participating unit can consider the energy demand of each region and create a region-specific energy conservation plan. For example, the participating unit can consider the energy supply of each region and create a region-specific energy conservation plan. For example, the participating unit can consider the balance between energy demand and supply of each region and create a region-specific energy conservation plan. In this way, by considering the energy demand of each region, a region-specific conservation plan can be created. Some or all of the above processing in the participating unit may be performed using AI, for example, or without AI. For example, the participating unit can input regional energy demand data into a generating AI and have the generating AI create a region-specific conservation plan.

[0111] The participating unit, when participating in energy conservation, considers the diversity of energy sources and proposes the optimal conservation method. For example, the participating unit can propose the optimal energy conservation method considering the diversity of energy sources. For example, the participating unit can propose a conservation method that maximizes the use of renewable energy considering the diversity of energy sources. For example, the participating unit can propose a cost-effective energy conservation method considering the diversity of energy sources. In this way, by considering the diversity of energy sources, the optimal conservation method can be proposed. Some or all of the above processing in the participating unit may be performed using AI, for example, or without AI. For example, the participating unit can input energy source data into a generating AI and have the generating AI execute a proposal for the optimal conservation method.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The energy grid balancer system may also include an anomaly detection unit. The anomaly detection unit analyzes energy supply and demand data in real time and detects abnormal patterns. For example, the anomaly detection unit can detect sudden increases or decreases in energy consumption and issue an alert. The anomaly detection unit can also detect unstable energy supply conditions and prompt a rapid response. This allows for the immediate identification of abnormal energy consumption patterns and supply instability, enabling a swift response. Some or all of the above-described processes in the anomaly detection unit may be performed using AI, for example, or without AI. For example, the anomaly detection unit can input energy supply and demand data into a generating AI and have the generating AI perform the detection of abnormal patterns.

[0114] The energy grid balancer system may further include a forecasting unit. The forecasting unit analyzes historical energy consumption data and predicts future energy demand. For example, the forecasting unit can analyze seasonal energy consumption patterns and predict seasonal demand fluctuations. It can also consider the impact of specific events and holidays and predict peak demand. This makes it possible to accurately predict future energy demand based on historical data and optimize energy supply planning. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input historical energy consumption data into a generating AI and have the generating AI perform future demand forecasts.

[0115] The energy grid balancer system may further include an emotion estimation unit. The emotion estimation unit estimates the user's emotions and adjusts energy supply and demand based on the estimated emotions. For example, if the user is stressed, the emotion estimation unit can reduce energy consumption notifications. If the user is relaxed, it can also provide detailed energy consumption data. This allows for more appropriate energy management by adjusting energy supply and demand according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the emotion estimation unit may be performed using AI or not using AI. For example, the emotion estimation unit can input the user's emotion data into the generative AI and have the generative AI perform the adjustment of energy supply and demand.

[0116] The energy grid balancer system may further include a region-specific unit. This unit analyzes energy consumption patterns for each region and creates region-specific energy supply plans. For example, the region-specific unit can analyze energy demand data for each region and identify region-specific demand patterns. It can also analyze the use of renewable energy in each region and create an optimal supply plan. This makes it possible to create an optimal energy supply plan that takes into account region-specific energy consumption patterns. Some or all of the above-described processes in the region-specific unit may be performed using AI, for example, or without AI. For example, the region-specific unit can input energy consumption data for each region into a generating AI and have the generating AI create a region-specific supply plan.

[0117] The energy grid balancer system may further include a market price-linked unit. This unit incorporates real-time market price data and adjusts energy supply and demand. For example, it can create a cost-effective energy supply plan based on real-time market price data. It can also suggest shifting demand if market prices surge during peak energy demand periods. This enables cost-effective energy supply and demand adjustments by utilizing real-time market price data. Some or all of the above-described processes in the market price-linked unit may be performed using AI, for example, or without AI. For instance, the market price-linked unit can input market price data into a generating AI and have the generating AI perform the energy supply and demand adjustments.

[0118] The energy grid balancer system may further include a user education department. This department educates users about the importance of energy conservation and promotes the reduction of energy consumption. For example, the user education department can provide online courses explaining how to reduce energy consumption. It can also offer incentives to users who successfully reduce energy consumption. This helps educate users about the importance of energy conservation and promotes the reduction of energy consumption. Some or all of the above-described processes in the user education department may be performed using AI, for example, or without AI. For example, the user education department can input energy consumption data into a generating AI and have the generating AI provide the educational program.

[0119] The energy grid balancer system may further include an emotional feedback unit. The emotional feedback unit estimates the user's emotions and provides feedback on energy consumption based on the estimated emotions. For example, if the user is feeling stressed, the emotional feedback unit can provide positive feedback emphasizing the success in reducing energy consumption. If the user is relaxed, it can also provide detailed energy consumption data and suggest further improvements. This makes it possible to promote energy consumption reduction by providing feedback that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the emotional feedback unit may be performed using AI or not using AI. For example, the emotional feedback unit can input the user's emotional data into the generative AI and have the generative AI provide the feedback.

[0120] The energy grid balancer system may further include a weather-linked unit. The weather-linked unit takes in real-time weather data and adjusts energy supply and demand. For example, the weather-linked unit can predict the amount of electricity generated by solar and wind power based on real-time weather data. It can also predict fluctuations in energy demand in response to temperature fluctuations and adjust the supply plan. This makes it possible to maximize the use of renewable energy by adjusting energy supply and demand using weather data. Some or all of the above processing in the weather-linked unit may be performed using AI, for example, or without AI. For example, the weather-linked unit can input weather data into a generating AI and have the generating AI perform the adjustment of energy supply and demand.

[0121] The energy grid balancer system may further include an emotion notification unit. The emotion notification unit estimates the user's emotion and adjusts the energy consumption notification method based on the estimated emotion. For example, if the user is stressed, the emotion notification unit can provide a simple and highly visible notification method. If the user is relaxed, it can provide a notification method that includes detailed information. This allows for more appropriate information to be provided by adjusting the notification method according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the emotion notification unit may be performed using AI or not using AI. For example, the emotion notification unit can input the user's emotion data into the generative AI and have the generative AI perform the adjustment of the notification method.

[0122] The energy grid balancer system may further include an energy storage optimization unit. The energy storage optimization unit monitors the status of the energy storage system in real time and determines the optimal charging and discharging timing. For example, the energy storage optimization unit can monitor the degradation status of the energy storage system and determine the optimal maintenance schedule. The energy storage optimization unit can also determine the optimal charging and discharging timing by considering the balance between energy supply and demand. This enables efficient management of the energy storage system. Some or all of the above-described processes in the energy storage optimization unit may be performed using AI, for example, or without AI. For example, the energy storage optimization unit can input status data of the energy storage system into a generating AI and have the generating AI determine the optimal charging and discharging timing.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The monitoring unit monitors energy supply and demand in real time. For example, it collects energy supply and demand data using IoT sensors and provides this data to the balancing unit. Step 2: The balancing unit predicts energy demand based on data collected by the monitoring unit and adjusts the distribution. For example, it uses AI algorithms to predict energy demand, optimize the balance between energy supply and demand, and improve grid stability. Step 3: The optimization unit maximizes the use of renewable energy. For example, it reduces energy costs and carbon emissions by predicting renewable energy generation and optimizing energy supply plans. Step 4: The management department efficiently manages the energy storage system. For example, it optimizes the charging and discharging of the energy storage system to improve energy efficiency. The management department monitors the status of the energy storage system in real time and performs optimal management. Step 5: The participating department engages consumers in energy conservation. For example, it provides consumers with incentives to conserve energy and encourages them to reduce energy consumption. The participating department analyzes consumer energy consumption data and suggests optimal energy conservation methods.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0127] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the monitoring unit, balancing unit, optimization unit, management unit, and participation unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the monitoring unit monitors energy supply and demand in real time using the IoT sensors of the smart device 14 and collects data using the specific processing unit 290 of the data processing unit 12. The balancing unit uses the specific processing unit 290 of the data processing unit 12 to predict energy demand using an AI algorithm and adjust the distribution. The optimization unit uses the specific processing unit 290 of the data processing unit 12 to predict the amount of renewable energy generated and optimize the energy supply plan. The management unit uses the specific processing unit 290 of the data processing unit 12 to optimize the charging and discharging of the energy storage system and improve energy efficiency. The participation unit provides consumers with an incentive to save energy and promotes a reduction in energy consumption using the control unit 46A of the smart device 14. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the monitoring unit, balancing unit, optimization unit, management unit, and participation unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit monitors energy supply and demand in real time using the IoT sensors of the smart glasses 214 and collects data using the specific processing unit 290 of the data processing unit 12. The balancing unit uses the specific processing unit 290 of the data processing unit 12 to predict energy demand using an AI algorithm and adjust the distribution. The optimization unit uses the specific processing unit 290 of the data processing unit 12 to predict the amount of renewable energy generated and optimize the energy supply plan. The management unit uses the specific processing unit 290 of the data processing unit 12 to optimize the charging and discharging of the energy storage system and improve energy efficiency. The participation unit provides consumers with an incentive to save energy and promotes a reduction in energy consumption using the control unit 46A of the smart glasses 214. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0153] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] Each of the multiple elements described above, including the monitoring unit, balancing unit, optimization unit, management unit, and participation unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the monitoring unit monitors energy supply and demand in real time using the IoT sensor of the headset terminal 314 and collects data using the specific processing unit 290 of the data processing unit 12. The balancing unit uses the specific processing unit 290 of the data processing unit 12 to predict energy demand using an AI algorithm and adjust the distribution. The optimization unit uses the specific processing unit 290 of the data processing unit 12 to predict the amount of renewable energy generated and optimize the energy supply plan. The management unit uses the specific processing unit 290 of the data processing unit 12 to optimize the charging and discharging of the energy storage system and improve energy efficiency. The participation unit provides consumers with an incentive to save energy and promotes a reduction in energy consumption using the control unit 46A of the headset terminal 314. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0163] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0164] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0165] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0166] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0167] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0168] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0169] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0170] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0171] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0172] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0173] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0174] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0175] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0176] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0177] Each of the multiple elements described above, including the monitoring unit, balancing unit, optimization unit, management unit, and participation unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the monitoring unit monitors energy supply and demand in real time using the IoT sensors of the robot 414 and collects data using the specific processing unit 290 of the data processing unit 12. The balancing unit uses the specific processing unit 290 of the data processing unit 12 to predict energy demand using an AI algorithm and adjust the distribution. The optimization unit uses the specific processing unit 290 of the data processing unit 12 to predict the amount of renewable energy generated and optimize the energy supply plan. The management unit uses the specific processing unit 290 of the data processing unit 12 to optimize the charging and discharging of the energy storage system and improve energy efficiency. The participation unit provides consumers with an incentive to save energy and promotes a reduction in energy consumption through the control unit 46A of the robot 414. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0178] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0179] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0180] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0181] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0182] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0183] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0184] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0185] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0186] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0187] 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.

[0188] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0189] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0190] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0191] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0192] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0193] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0194] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0195] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0196] (Note 1) A monitoring unit that monitors energy supply and demand in real time, A balancing unit predicts energy demand and adjusts distribution based on data collected by the monitoring unit, An optimization unit that maximizes the use of renewable energy, A management department that efficiently manages the energy storage system, It includes a participatory section that allows consumers to participate in energy saving. A system characterized by the following features. (Note 2) The monitoring unit, Use IoT sensors to monitor energy supply and demand in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The balancing unit is, Based on the data collected by the aforementioned monitoring unit, energy demand is predicted and distribution is adjusted. The system described in Appendix 1, characterized by the features described herein. (Note 4) The optimization unit, Maximize the use of renewable energy. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned management department, Efficiently manage energy storage systems The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned participating section is, Get consumers involved in energy conservation The system described in Appendix 1, characterized by the features described herein. (Note 7) The monitoring unit, It estimates user sentiment and adjusts the frequency of energy supply and demand monitoring based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The monitoring unit, By incorporating weather data in real time during energy supply and demand monitoring, we can improve forecast accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 9) The monitoring unit, Based on monitoring data, an anomaly detection algorithm is applied to immediately identify abnormal energy consumption patterns. The system described in Appendix 1, characterized by the features described herein. (Note 10) The monitoring unit, It estimates the user's emotions and adjusts how monitoring data is displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The monitoring unit, During monitoring, energy supply and demand data are segmented by region to analyze region-specific energy consumption patterns. The system described in Appendix 1, characterized by the features described herein. (Note 12) The monitoring unit, Based on monitoring data, we identify peak energy consumption periods and propose peak shifting strategies. The system described in Appendix 1, characterized by the features described herein. (Note 13) The balancing unit is, It estimates the user's emotions and adjusts the priority of energy allocation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The balancing unit is, When forecasting energy demand, historical consumption data is referenced, and seasonal demand fluctuations are taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 15) The balancing unit is, By incorporating real-time market price data during energy allocation, cost-effective distribution can be achieved. The system described in Appendix 1, characterized by the features described herein. (Note 16) The balancing unit is, It estimates the user's emotions and adjusts the energy distribution notification method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The balancing unit is, When forecasting energy demand, industry-specific consumption data is analyzed, and the demand characteristics of each industry are taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The balancing unit is, When allocating energy, the diversity of energy sources should be considered, and the optimal source should be selected. The system described in Appendix 1, characterized by the features described herein. (Note 19) The optimization unit, The system estimates user sentiment and adjusts renewable energy utilization plans based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The optimization unit, When using renewable energy, weather forecast data is incorporated to predict fluctuations in power generation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The optimization unit, When using renewable energy, the status of the energy storage system is monitored in real time to determine the optimal timing for use. The system described in Appendix 1, characterized by the features described herein. (Note 22) The optimization unit, We estimate user sentiment and adjust how we notify users about their renewable energy usage based on that sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The optimization unit, When using renewable energy, consider the energy demand of each region and create a region-specific optimization plan. The system described in Appendix 1, characterized by the features described herein. (Note 24) The optimization unit, When using renewable energy, consider the diversity of energy sources and select the optimal source. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned management department, It estimates the user's emotions and adjusts the management method of the energy storage system based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned management department, When managing energy storage systems, the battery degradation status is monitored in real time to determine the optimal maintenance schedule. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned management department, When managing energy storage systems, the optimal charging and discharging timing is determined by considering the balance between energy supply and demand. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned management department, We estimate the user's emotions and adjust how we notify them of the energy storage system's status based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned management department, When managing energy storage systems, regional energy demands should be considered, and region-specific management plans should be created. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned management department, When managing energy storage systems, consider the diversity of energy sources and select the optimal source. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned participating section is, It estimates the user's emotions and adjusts how they participate in energy saving based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned participating section is, When users participate in energy saving initiatives, the system refers to their past consumption data to suggest the most suitable energy-saving methods. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned participating section is, When users participate in energy saving initiatives, we provide customized saving plans that take their lifestyle into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned participating section is, It estimates the user's emotions and adjusts how energy-saving notifications are sent based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned participating section is, When participating in energy conservation efforts, consider the energy demands of each region and create a region-specific conservation plan. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned participating section is, When participating in energy conservation, we consider the diversity of energy sources and propose the most suitable conservation methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A monitoring unit that monitors energy supply and demand in real time, A balancing unit predicts energy demand and adjusts distribution based on data collected by the monitoring unit, An optimization unit that maximizes the use of renewable energy, A management department that efficiently manages the energy storage system, It includes a participatory section that allows consumers to participate in energy saving. A system characterized by the following features.

2. The monitoring unit, Use IoT sensors to monitor energy supply and demand in real time. The system according to feature 1.

3. The balancing unit is, Based on the data collected by the aforementioned monitoring unit, energy demand is predicted and distribution is adjusted. The system according to feature 1.

4. The optimization unit, Maximize the use of renewable energy. The system according to feature 1.

5. The aforementioned management department, Efficiently manage energy storage systems The system according to feature 1.

6. The aforementioned participating section is, Get consumers involved in energy conservation The system according to feature 1.

7. The monitoring unit, It estimates user sentiment and adjusts the frequency of energy supply and demand monitoring based on the estimated user sentiment. The system according to feature 1.

8. The monitoring unit, By incorporating weather data in real time during energy supply and demand monitoring, we can improve forecast accuracy. The system according to feature 1.

9. The monitoring unit, Based on monitoring data, an anomaly detection algorithm is applied to immediately identify abnormal energy consumption patterns. The system according to feature 1.

10. The monitoring unit, It estimates the user's emotions and adjusts how monitoring data is displayed based on the estimated user emotions. The system according to feature 1.

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