Programs, off-grid building modules, methods, and systems

Abstract

To reduce the risk of electricity or water depletion in off-grid environments. [Solution] A program that operates a computer equipped with a processor and memory causes the processor to acquire current state information, including the remaining charge of a battery in an off-grid environment independent of the power infrastructure, and uses a machine learning model to predict the balance of power supply and demand. The predicted balance of supply and demand is applied to the current battery charge as a starting point to calculate the future charge trend. The calculated charge trend is evaluated to determine the risk of power depletion by checking whether it falls below a threshold, and if there is a risk, predetermined processing is executed to reduce the risk.

Description

Technical Field

[0005]

[0001] The present disclosure relates to programs, off-grid building modules, methods, and systems.

Background Art

[0002] In recent years, due to the growing interest in sustainable lifestyles, the increasing awareness of disaster prevention, and the provision of new experiential values, the demand for living and conducting business in an off-grid environment independent of public infrastructure such as electricity and water has been increasing. In such an environment, it is essential to efficiently manage energy and resources obtained from nature, such as solar power generation and rainwater harvesting.

[0003] As an energy management system to meet such demands, a HEMS (Home Energy Management System) is known. Further, as a technology related to the stable supply of electricity, for example, Patent Document 1 discloses a technology for estimating the transition of weather conditions from weather forecasts and actual power generation amounts, correcting power supply and demand forecasts, and controlling power distribution between regions.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0006] To solve the above problems, a program according to one aspect of the present disclosure is a program for operating a computer comprising a processor and memory, wherein the program causes the processor to perform the following steps: acquire data including current state information, which includes at least the remaining charge of a battery in an off-grid environment independent of the power infrastructure, and past supply and demand performance information, which shows past power supply and demand performance; predict the power supply and demand balance for a predetermined period using a machine learning model based on the past supply and demand performance information; calculate the change in the remaining charge of the battery over a predetermined period by applying the predicted supply and demand balance starting from the current remaining charge of the battery included in the acquired current state information; determine the risk of power depletion by evaluating whether the calculated change in the remaining charge falls below a predetermined threshold necessary to maintain daily life; and, if it is determined that there is a risk of power depletion, perform a predetermined process to reduce the risk of power depletion. [Effects of the Invention]

[0007] According to this disclosure, the risk of depletion of electricity or water can be reduced in off-grid environments. [Brief explanation of the drawing]

[0008] [Figure 1] This is an external view showing an example of a building module to which this disclosure applies. [Figure 2] This is a block diagram showing the overall configuration of the energy management system related to this disclosure. [Figure 3] This block diagram shows an example of the functional configuration of the terminal device related to this disclosure. [Figure 4] This block diagram shows an example of the functional configuration of the server related to this disclosure. [Figure 5] This block diagram shows an example of the functional configuration of a building module related to this disclosure. [Figure 6]This figure shows an example of the data structure of a sensor data table. [Figure 7] This figure shows an example of the data structure of a weather data table. [Figure 8] This figure shows an example of the data structure of the prediction results table. [Figure 9] This flowchart shows an example of the information processing flow in the energy management system related to this disclosure. [Figure 10] This figure shows an example of a dashboard screen displayed on the terminal device of this disclosure. [Figure 11] This figure shows an example of a dashboard screen displayed on the terminal device of this disclosure. [Figure 12] This is a block diagram representing the basic hardware configuration of a computer. [Modes for carrying out the invention]

[0009] The embodiments of this disclosure will be described below with reference to the drawings. In all the drawings illustrating the embodiments, common components are denoted by the same reference numerals, and repeated explanations are omitted. The following embodiments are not intended to unduly limit the content of this disclosure as described in the claims. Not all components shown in the embodiments are necessarily essential components of this disclosure. Also, each drawing is a schematic diagram and is not necessarily a strict illustration.

[0010] Furthermore, in the following description, "processor" refers to one or more processors. At least one processor is typically a microprocessor such as a CPU (Central Processing Unit), but may be another type of processor such as a GPU (Graphics Processing Unit). At least one processor may be single-core or multi-core.

[0011] Also, at least one processor may be a processor in a broad sense, such as a hardware circuit (e.g., FPGA (Field-Programmable Gate Array) or ASIC (Application Specific Integrated Circuit)) that performs part or all of the processing.

[0012] In the following description, the expression such as "xxx table" may be used to describe information from which an output is obtained for an input. However, this information may be data with any structure or a learning model such as a neural network that generates an output for an input. Therefore, "xxx table" can be referred to as "xxx information".

[0013] In the following description, the configuration of each table is an example. One table may be divided into two or more tables, or all or part of two or more tables may be one table.

[0014] In the following description, the "program" may be used as the subject to describe the processing. However, since the program is executed by a processor to perform the defined processing while appropriately using a storage unit and / or an interface unit, etc., the subject of the processing may be the processor (or a device such as a controller having the processor).

[0015] The program may be installed in a device such as a computer, or may be in, for example, a program distribution server or a computer-readable (e.g., non-temporary) recording medium. In the following description, two or more programs may be realized as one program, or one program may be realized as two or more programs.

[0016] In the following description, an identification number is used as identification information for various objects, but identification information of other types (e.g., an identifier including letters and symbols) may be adopted.

[0017] In the following description, when describing elements of the same type without distinction, reference signs (or common signs among the reference signs) are used, and when describing elements of the same type separately, identification numbers (or reference signs) of the elements may be used.

[0018] In the following description, control lines and information lines indicate those considered necessary for the description, and not necessarily all control lines and information lines are shown on the product. All components may be interconnected.

[0019] Each information processing device is composed of a computer including an arithmetic unit and a storage unit. The basic hardware configuration of the computer and the basic functional configuration of the computer realized by the hardware configuration will be described later. For each of the terminal device 10 and the server 20, descriptions overlapping with the basic hardware configuration and the basic functional configuration of the computer described later are omitted.

[0020] (First Embodiment) <1. Overview> The system 1 according to the first embodiment realizes sustainable use of energy (electric power) in an off-grid type building module. The building module 30 targeted by this system autonomously manages energy supply and demand by means of a control unit and AI installed therein. The control unit of the building module 30 acquires real-time status information from various sensors installed in the building module 30, and further acquires the latest weather forecast from an external weather information providing service 50 via the server 20. The AI installed in the building module 30 accurately predicts future power supply and demand based on this information. Then, it determines the risk of power depletion from the prediction results, and when a risk is predicted, it autonomously controls the operation of the power consumption device or notifies the user to encourage power saving, thereby avoiding power depletion in advance.

[0021] <2. Regarding the off-grid type building module> First, let's describe the off-grid building module (hereinafter also referred to as the "building module") to which the energy management system according to this embodiment is applied. This building module is intended to be installed in areas such as remote islands or mountainous regions where infrastructure such as power grids and water supply is not developed. The aim is to utilize the rich natural environment as a tourism resource while keeping the environmental impact low.

[0022] Figure 1 is an external view showing an example of a building module to which this disclosure applies. As shown in Figure 1, the building module 30 has a shape such as a container house formed in the shape of a rectangular parallelepiped. This building module 30 has an exterior wall 31, a roof 32, and a floor 33 that create a space in which people can stay, and is a mobile structure that can be lifted by a crane or the like and transported by ship or truck.

[0023] This building module 30 is equipped with facilities to maintain a state where people can stay independently without receiving electricity or water from an external source. For electricity, a power generation device that utilizes renewable energy and a storage battery (not shown) for storing the generated electricity are provided. As an example of a power generation device, solar panels 34 are installed on the roof 32 of the building module 30. The electricity generated by these solar panels 34 is stored in the storage battery in preparation for nighttime and rainy weather, and is supplied to the power consumption devices within the module (air conditioning system, hot water supply system, etc.).

[0024] Regarding water, a water storage tank (not shown) that utilizes rainwater and other sources, and a water circulation system (water treatment device, not shown) that purifies used water for reuse through filtration or chemical treatment are provided. This allows for the use of water within the module without the need for an external water supply.

[0025] However, for such building modules 30 to continue operating independently, a stable supply of electricity and water is absolutely essential. In particular, the amount of electricity generated by renewable energy sources such as solar panels 34 is heavily dependent on natural conditions such as weather, so if unexpected bad weather continues, there is a risk that the battery power will be depleted. This depletion of electricity would lead to the shutdown of not only lighting and air conditioning but also the water circulation system that supplies water, thus posing a significant risk to the comfort and safety of occupants. Therefore, advanced energy management is required to manage power supply and demand in response to real-time changes in conditions based on the module specifications and weather conditions of the installation site, and to prevent resource depletion.

[0026] <3. Overall System Configuration> Figure 2 is a block diagram showing an example of the overall configuration of System 1 according to this disclosure. System 1 autonomously manages energy and water in building modules 30 (for example, movable building modules such as container houses or trailer houses) that do not depend on electricity and water infrastructure.

[0027] As shown in Figure 2, System 1 mainly includes a terminal device 10, a server 20, and a building module 30 with autonomous control functions. The building module 30 is equipped with multiple IoT devices 40 inside. The terminal device 10, the server 20, and the building module 30 are connected to each other so as to be able to communicate with one another via a local network 81 established within the facility. In addition, the server 20 is connected to an external weather information service 50 so as to be able to communicate with it via a wide-area network 80 such as the Internet.

[0028] Terminal device 10 is an information processing device operated by a user or administrator of the facility. Terminal device 10 can be implemented, for example, as a touch panel display embedded in a wall, or as a smartphone or tablet device owned by the user. Terminal device 10 displays information provided by server 20 and functions as an interface that accepts settings input from the user (e.g., setting the number of occupants, setting a target room temperature).

[0029] Server 20 is an information processing device that provides comprehensive support to each building module 30 in this system 1. Based on operational status information collected from each building module 30 and weather forecast information obtained from the weather information provision service 50, Server 20 provides weather information and control program updates to each building module 30. It also has a function to monitor the operational status of multiple building modules 30.

[0030] The building module 30 is the core of this system, an autonomously controlled building. Inside the building module 30, there is a control unit (described later) for managing energy supply and demand, and an IoT device 40. The IoT device 40 is a general term for various sensors and actuators installed within the facility. As shown in Figure 2, the IoT device 40 is broadly divided into production and storage devices 41 that produce and store energy and water, and controlled equipment 42 that consume energy and water. Specifically, in the first embodiment (power management), the production and storage devices 41 include energy production and storage devices such as solar panels and storage batteries, and the controlled equipment 42 includes actuators such as air conditioners, hot water heaters, and lighting fixtures whose operation is controlled to suppress power consumption, as well as sensors such as power meters and temperature and humidity sensors. Furthermore, in the second embodiment (water management) described later, the production and storage devices 41 include water production and storage devices such as rainwater collection devices and water storage tanks, and the controlled equipment 42 includes actuators such as hot water heaters and showers whose hot water supply and water volume are limited to suppress water consumption, as well as sensors such as water level sensors. These IoT devices 40 are connected to the control unit within the building module 30 via the local network 81 within the module, and are responsible for transmitting measurement data and receiving control commands.

[0031] The weather information provision service 50 is an external web service that provides future weather information for a specified area via an API (Application Programming Interface), etc. The server 20 aggregates the weather information required for each embodiment from this service and provides it to each building module 30. Specifically, in the power management of the first embodiment, solar radiation forecasts used for predicting power supply and demand are mainly acquired. On the other hand, in the water management of the second embodiment, precipitation forecasts used for predicting water supply and demand balance are mainly acquired.

[0032] In Figure 2, for the sake of illustration simplicity, each component is shown individually; however, in reality, multiple terminal devices 10 and a wide variety of IoT devices 40 can be connected to System 1.

[0033] <4. Configuration of terminal equipment> Figure 3 is a block diagram showing an example of the functional configuration of the terminal device 10. As shown in Figure 3, the terminal device 10 includes a communication unit 120, an input device 13, an output device 14, a location information sensor 150, a camera 160, an audio processing unit 17, a storage unit 180, and a control unit 190. Each block included in the terminal device 10 is electrically connected, for example, by a bus.

[0034] The communication unit 120 performs processing for communicating with the server 20 via the local network 81.

[0035] The input device 13 is a device for the user to input instructions and information, and can be implemented, for example, by a keyboard, mouse, or a capacitive touch-sensitive device 131 integrated into the display surface of the output device 14. The user performs operations via the input device 13, such as setting the number of occupants, setting a target room temperature, or responding to suggestions from the building module 30 (e.g., "Would you like to cooperate in saving energy?").

[0036] The output device 14 is a device for presenting information to the user and is implemented, for example, by an LCD (Liquid Crystal Display) or an organic EL (Electro-Luminescence) display 141. The output device 14 displays data such as images, videos, and text in accordance with the control of the control unit 190, and presents the dashboard screen and warning messages described later.

[0037] The location information sensor 150 detects the current location of the terminal device 10 using GPS (Global Positioning System) or the like.

[0038] Camera 160 is a device that receives light using a photodetector and outputs it as a captured image.

[0039] The audio processing unit 17 modulates and demodulates the audio signal. The audio processing unit 17 modulates the signal received from the microphone 171 and sends the modulated signal to the control unit 190. The audio processing unit 17 also sends the audio signal to the speaker 172.

[0040] The storage unit 180 is composed of, for example, flash memory and stores programs (OS, browser, dedicated applications, etc.) and data necessary for the terminal device 10 to operate. The storage unit 180 may also store user information 181 such as a user ID for identifying the user, the user's name, and their affiliation.

[0041] The control unit 190 controls the operation of the entire terminal device 10 by having the processor read the program stored in the memory unit 180 and executing the instructions contained in the program. By operating according to the program, the control unit 190 performs the functions of an operation reception unit 191, a transmission / reception unit 192, and a presentation control unit 193.

[0042] The operation reception unit 191 processes user input operations to the input device 13. Specifically, it receives input such as setting information like the number of people staying or the target room temperature, and instructions regarding energy-saving actions.

[0043] The transmitting / receiving unit 192 performs processing to send and receive data with external devices such as the server 20 and the building module 30 in accordance with the communication protocol. Specifically, it transmits user information received by the operation reception unit 191 to the building module 30 and receives display data (such as dashboard information) transmitted from the building module 30.

[0044] The display control unit 193 performs processing to present information to the user. Specifically, based on the data received by the transmitting / receiving unit 192 from the building module 30, it performs processing to display a dashboard screen, a future supply and demand forecast graph, and warning messages regarding depletion risk on the display 141 of the output device 14.

[0045] <5. Server Configuration> Figure 4 is a block diagram showing an example of the functional configuration of server 20. Server 20 is an information processing device that comprehensively supports each building module 30 in this system 1. As shown in Figure 4, server 20 comprises a communication unit, a storage unit, and a control unit. Server 20 does not directly control individual building modules 30, but rather plays a role in managing and supporting multiple building modules 30 across the board. This system can be implemented in either a standalone configuration where each building module 30 operates autonomously, or a network configuration where server 20 cooperates to provide additional functions.

[0046] The communications unit 201 performs processing for communicating with other devices (terminal devices 10, building modules 30, weather information provision services 50, etc.) via the local network 81 and the wide-area network 80.

[0047] The memory unit 202 stores programs executed by the server 20's processor and various data used for processing. For example, the memory unit 202 stores operational status information and user information collected from each building module 30, as well as control programs or their update data that are distributed to each building module 30 and executed within them.

[0048] The control unit 203 is implemented by the processor executing a program and supports the autonomous operation of the building module 30. The control unit 203 includes, as the main functional parts of this embodiment, an external information collection module 2031, a monitoring module 2032, and an external information provision module 2033, etc.

[0049] The external information collection module 2031 collects information (such as operation logs) indicating the operating status of multiple building modules 30 connected via a network, for management and analysis purposes. It also has the function of acquiring external environmental information that can be used in common by each module, such as weather forecasts, from the weather information provision service 50 and providing it to each building module 30.

[0050] The monitoring module 2032 receives operational logs and status information from multiple building modules 30 connected to the network 81, and monitors whether each module is operating normally.

[0051] The external information provision module 2033 generates and provides additional information from a longer-term or cross-sectional perspective based on operational status information and external environmental information collected from multiple building modules 30 under its management. Specifically, it compares and analyzes the energy efficiency of each module and analyzes usage trends from past data.

[0052] <6. Building Module Configuration> Figure 5 is a block diagram showing an example of the functional configuration of a building module according to this disclosure. As shown in Figure 5, the building module 30 includes a communication unit 301, a storage unit 302, and a control unit 303.

[0053] The communication unit 301 performs processing for communicating with other devices (terminal device 10, server 20, etc.) via the local network 81.

[0054] The storage unit 302 corresponds to memory and storage and stores the OS, the program related to this disclosure, and various databases or tables. The storage unit 302 stores, for example, a sensor data table 3021 that records collected sensor data, a weather data table 3022 that records acquired weather information, and a prediction result table 3023 that records prediction results by AI.

[0055] The control unit 303 is implemented by the processor executing a program stored in the memory unit 302, and comprehensively controls the operation of the entire building module 30. The control unit 303 includes, as the main functional units of this embodiment, a data acquisition module 3031, a prediction processing module 3032, a risk determination module 3033, a control execution module 3034, and an information provision module 3035, etc.

[0056] The data collection module 3031 collects data to understand the current status of the building module 30. Specifically, it periodically receives current status information (solar power generation amount, battery level, power consumption of each device, etc.) from IoT devices 40 within the building module 30. It also obtains the latest weather forecast data (e.g., solar radiation amount and temperature for the next 24 hours) from the server 20. Furthermore, it receives user information (number of occupants, comfort settings, etc.) from the terminal device 10.

[0057] The prediction processing module 3032 forms the core of the module's prediction function and functions as an AI engine. Based on various collected data and historical power supply and demand information, the prediction processing module 3032 uses a machine learning model to predict the power supply and demand balance for a predetermined period. As this machine learning model, for example, a multiple regression model, an RNN (Recurrent Neural Network) model such as LSTM (Long Short-Term Memory) or GRU (Gated Recurrent Unit) specialized for time series forecasting, or a Transformer-based model can be used. These models may be configured to use historical data accumulated in the sensor data table 3021 and weather data table 3022 over a certain period in the past (e.g., the past year) as training data, and to be retrained periodically (e.g., once a week) or whenever a predetermined amount of new data is accumulated.

[0058] The risk assessment module 3033 determines the risk of power depletion based on the power supply and demand balance calculated by the prediction processing module 3032. Specifically, it first calculates the change in the remaining battery capacity over a predetermined period by applying the predicted supply and demand balance, starting from the current remaining battery capacity included in the acquired current status information. Next, it evaluates whether the calculated change in remaining capacity falls below a predetermined threshold necessary to maintain daily life (hereinafter referred to as the "depletion determination threshold"), and if it is evaluated as falling below the threshold, it determines that there is a "risk of depletion."

[0059] Here, the depletion threshold may be set in advance according to the specifications of the building module 30 or the expected number of occupants, and stored in the memory unit 302. Alternatively, the risk determination module 3033 may calculate it each time as needed based on the latest information such as the number of occupants input from the terminal device 10. For example, this depletion threshold is calculated by multiplying the number of occupants input from the terminal device 10 by a predetermined minimum required power consumption per person per day (e.g., 500Wh for the power required to maintain lighting, communication equipment, and water purification equipment). This threshold concept can be applied similarly to both electricity and water.

[0060] Furthermore, the risk assessment module 3033 may determine the degree of depletion risk at multiple levels. These levels are determined, for example, based on how far the predicted remaining capacity falls below the safety threshold, or how long a negative balance is expected to continue. Specifically, if the predicted battery capacity trend falls below the "minimum maintenance threshold" necessary for maintaining daily life, but exceeds the "emergency shutdown threshold" where forced shutdown of equipment should be avoided, it is determined to be the "first level (warning level)." On the other hand, if the predicted remaining capacity trend is expected to fall below the "emergency shutdown threshold," it can be determined to be the more serious "second level (emergency level)."

[0061] The control execution module 3034 executes processing to suppress power consumption when the risk determination module 3033 determines that there is a "risk of depletion". The method of executing this processing may differ depending on the configuration, such as whether or not the level (degree) of risk is determined. For example, in a simple configuration in which only the presence or absence of a depletion risk is determined, the control execution module 3034 uniformly executes predetermined processing, such as notifying the user or mitigating the operation of power consumption devices, when a risk is determined to exist.

[0062] On the other hand, in a configuration that determines the level of depletion risk in stages, the control execution module 3034 selects and executes one of several predetermined processes according to the level. For example, if the risk is determined to be "Level 1 (Warning Level)," it selects a process that includes control to mitigate the operation of power consumption devices in order to autonomously suppress power consumption to the extent that it does not significantly impair user comfort. In contrast, if the risk is determined to be "Level 2 (Emergency Level)," which is more serious than Level 1, it selects a process that sends a notification to the administrator urging them to supply power to the building module from an external power source such as an electric vehicle (EV). Specifically, this control to mitigate operation includes at least one of the following: control to relax the temperature setting of the air conditioning system (e.g., raise the cooling temperature setting, lower the heating temperature setting) or adjustment of the temperature setting of the hot water supply system (e.g., lower the heat retention temperature). This makes it possible to minimize the impact on daily life while avoiding serious power depletion. It should be noted that the system may also be configured to execute notifications to encourage users to conserve electricity according to the urgency, either in parallel with or independently of these staged processes.

[0063] The information provision module 3035 provides information held by the building module 30 to the terminal device 10 and performs processing for visualization. Specifically, it transmits information such as the current energy supply and demand situation, future supply and demand forecast graphs, or depletion risk to the terminal device 10. In addition, to encourage energy-saving behavior by the user, it may have a function to calculate and present a score based on past performance over a certain period and provide incentives to the user according to that score. Possible incentives include, for example, discount coupons that can be used on future facility visits or gifts such as souvenirs.

[0064] <7. Data Structure> In this embodiment, the main data structures managed by the storage unit 302 of the building module 30 will be described with reference to Figures 6 to 8. Note that the data structures described are examples, and data not described is not excluded. In this embodiment, the data used for power management will be primarily described.

[0065] Figure 6 shows an example of the data structure of the sensor data table 3021. This table stores time-series data collected from each IoT device 40. Specifically, it includes items such as "Log ID," "Device ID," "Data Type," "Timestamp," and "Value." The "Log ID" is identification information to uniquely identify each sensor log record. The "Device ID" records the IoT device that sent the data, the "Data Type" records the type of data (e.g., "Power Generation," "Power Consumption," "Battery Level"), the "Timestamp" records the date and time the data was measured, and the "Value" records the measured value.

[0066] Figure 7 shows an example of the data structure of weather data table 3022. This table stores weather forecast data obtained from the weather information provision service 50. Specifically, it includes items such as "acquisition date and time," "forecast target date and time," and "solar radiation." "Acquisition date and time" records the date and time when the forecast data was acquired, and "forecast target date and time" records the future date and time that will be the target of the forecast. The forecast data for "solar radiation" is used for predicting power supply and demand.

[0067] Figure 8 shows an example of the data structure of the prediction results table 3023. This table stores the prediction results from the prediction processing module 3032 and the judgment results from the risk judgment module 3033. Specifically, it includes items such as "Prediction ID," "Prediction Target Period," "Predicted Energy Balance," and "Depletion Risk Level." The "Prediction ID" is identification information to uniquely identify each prediction result. The "Prediction Target Period" indicates the period covered by the prediction. The "Predicted Energy Balance" stores time-series data of the predicted balance, and optionally, the "Depletion Risk Level" records the determined risk level.

[0068] <8.Operation> Next, the operation of the information processing according to the first embodiment will be explained with reference to the flowchart in Figure 9. This process is repeatedly executed by the control unit 303 of the building module 30 at a predetermined interval.

[0069] First, in step S1, the data collection module 3031 of the building module 30 acquires various data. Specifically, it acquires current status information (battery level, current power generation / consumption, etc.) from IoT devices 40 within the facility and past supply and demand performance information from the sensor data table 3021 of the storage unit 302. Furthermore, it acquires the latest weather information (solar radiation, etc.) for a predetermined period (e.g., the next 24 hours) from an external weather information provision service 50 via the server 20.

[0070] Next, in step S2, the prediction processing module 3032 uses a machine learning model to predict the supply and demand balance of electricity for a predetermined period, based on the data acquired in step S1.

[0071] Next, in step S3, the risk assessment module 3033 determines whether there is a risk of depletion by evaluating whether the trend in the remaining battery capacity, calculated based on the supply and demand balance predicted in step S2, falls below a predetermined threshold (minimum demand) necessary to maintain daily life.

[0072] If a depletion risk is determined, in the following step S4, the building module 30 performs predetermined processing to reduce the risk of power depletion. Specifically, this processing includes the control execution module 3034 autonomously controlling the operation of the corresponding IoT device according to the type and degree of the determined risk, or the information provision module 3035 sending a warning message or a notification to the terminal device 10 to encourage power saving and water saving.

[0073] <9. Screen example> Figure 10 shows an example of a power management dashboard screen displayed on the display 141 of the terminal device 10. This screen is drawn by the display control unit 193 of the terminal device 10 based on data transmitted from the information provision module 3035 of the building module 30.

[0074] The information display area 1411 at the top of the screen displays the current date and time measured by the terminal device 10, as well as the latest weather icons and temperature obtained by the server 20 from an external weather information service 50, allowing the user to grasp the basic situation at a glance.

[0075] In the resource status display area 1412 located below it, the current status of "power generation," "power consumption," and "battery remaining capacity," collected in real time from the IoT device 40, is displayed numerically along with icons.

[0076] In the forecast information area 1413 in the center of the screen, the future power balance predicted by the control unit 303 of the building module 30 is displayed in a graph. This graph uses time on the horizontal axis and visually shows the trend of future power surplus or shortage along with predetermined thresholds. Furthermore, in this example, if a risk of power shortage is detected, a specific warning message such as "There is a possibility of a power shortage tomorrow afternoon. Please cooperate in saving electricity" is displayed to encourage proactive action from the user.

[0077] The Eco Level display area 1414 at the bottom of the screen displays the current resource usage score, the Eco Level, along with a graphical gauge. This score is calculated based on how much the actual power consumption falls below the pre-calculated "standard predicted consumption" (= savings). This standard predicted consumption can be, for example, a predicted value of the standard energy consumption for the period calculated by the server 20 based on weather information and the user's activity status. In this way, the lower the actual consumption is below the standard predicted consumption and the greater the savings, the higher the score is assigned. Such a score display can directly evaluate and visualize the results of the user's specific energy-saving actions and function as an incentive to encourage further cooperation.

[0078] <10.Summary> In off-grid environments, there are always unpredictable fluctuations in energy production (e.g., solar power generation) due to sudden weather changes, and fluctuations in consumption due to the behavior of the users staying there. Therefore, simply monitoring the current resource levels is insufficient, as unexpected power depletion can occur, significantly impairing the quality of life and comfort of users. According to this first embodiment, AI takes these fluctuating factors into consideration to predict future power supply and demand with high accuracy, and autonomously controls power consumption devices based on the predicted depletion risk. This makes it possible to avoid power depletion and realize a sustainable and stable off-grid lifestyle while maintaining user comfort.

[0079] (Second Embodiment) Next, a second embodiment of the present invention will be described. Similar to the power management in the first embodiment, it is also possible to manage the risk of water depletion. Specifically, the building module 30 acquires the remaining amount of water in a storage tank in an off-grid environment independent of the water infrastructure, past supply and demand records, weather information (precipitation forecast), etc. Then, using a machine learning model, it predicts the water supply and demand balance for a predetermined period and calculates the future trend of the remaining amount, starting from the current amount in the storage tank. If the calculated trend of the remaining amount falls below a predetermined threshold, the risk of water depletion is determined. If a risk is determined, predetermined processing to suppress water consumption (e.g., hot water restriction, notification to the user) is executed. Note that the basic hardware configuration of the system (Figures 2 and 12), and the functional block configurations of the terminal device (Figure 3), server (Figure 4), and building module (Figure 5) are the same as in the first embodiment, so here we will focus on the differences.

[0080] <1. Overview of Water Supply and Demand Management> In this embodiment, the building module 30 determines the risk of water depletion and performs control to avoid it. To this end, in addition to the data used in the first embodiment, it collects and processes data specific to water management.

[0081] <2. Data Structure> The data structure used in this second embodiment will now be described. The basic table structure is the same as in the first embodiment, but the data items used for water management are different.

[0082] The sensor data table 3021 (Figure 6) stores data related to water management. "Data type" includes "tank water level" and "water consumption," among others.

[0083] In the weather data table 3022 (Figure 7), the "solar radiation" item is replaced with forecast data for "precipitation," which is used in this embodiment to predict the water supply and demand balance.

[0084] The prediction results table 3023 (Figure 8) stores, instead of the "predicted energy balance" item, data for "predicted water balance" related to water management in this embodiment and the determined "depletion risk level".

[0085] <3.Operation> Next, the operation of the information processing in this embodiment will be explained with reference to the flowchart in Figure 9. The flow of this processing is the same as in the first embodiment, but the content of the data handled in each step is specific to water management.

[0086] First, in the data acquisition step (S1), the data collection module 3031 obtains the remaining water level in the storage tank from the water level sensor as current status information, and obtains the history of past water consumption as past supply and demand performance. Furthermore, it obtains future "precipitation forecasts" from the weather information provision service 50.

[0087] Next, in the supply and demand forecasting step (S2), the forecasting processing module 3032 forecasts the supply and demand balance of water. Specifically, it forecasts future rainwater collection (water production) from acquired precipitation forecasts and information such as the roof area of ​​the facility, and forecasts future water consumption from past water consumption patterns and the number of people staying there.

[0088] In the risk assessment step (S3), the risk assessment module 3033 determines the risk of water depletion based on the predicted water supply and demand balance. It applies the predicted supply and demand balance to the current water tank level and calculates the future trend of the remaining water level. If the calculated trend of the remaining water level falls below the minimum required amount of water (a predetermined threshold) calculated based on the number of people staying there, it is determined that there is a "risk of depletion".

[0089] In the control execution step (S4), if it is determined that there is a risk of water depletion, the control execution module 3034 executes processes to reduce water consumption. Specific processes include temporarily restricting hot water supply from the hot water heater, adjusting the water volume of the shower, and sending a water-saving notification to the user's terminal device 10.

[0090] <4. Screen example> Figure 11 is an example of a water management dashboard screen displayed on the display 141 of the terminal device 10 in this embodiment.

[0091] The information display area 1415 at the top of the screen displays the date and weather, while the resource status display area 1416 displays "water consumption" and "remaining water in the storage tank" in real time.

[0092] In the forecast information area 1417 in the center of the screen, the projected future water balance is displayed in a graph. In the example in Figure 11, it has been determined that there is no risk of depletion, and the message "There is no possibility of water shortage tomorrow" is displayed.

[0093] The eco-level display area 1418 at the bottom of the screen displays the eco-level as a score based on the current water usage, functioning as a gamification element to encourage users to conserve water.

[0094] <5.Summary> In off-grid environments, there are always fluctuations in water production (rainwater collection) due to weather and fluctuations in consumption due to user behavior. Simply monitoring the remaining amount in the water storage tank is insufficient, as unexpected water depletion can occur, posing a risk to users' quality of life. According to this second embodiment, AI can predict the risk of depletion not only for electricity but also for water, taking these fluctuating factors into account, and proactively control the situation. This makes it possible to prevent the depletion of limited water resources in off-grid environments and realize their sustainable use.

[0095] (modified version) In the embodiments described above, the focus was on power management (first embodiment) and water management (second embodiment), respectively. However, the present invention is not limited to these, and the following modifications are also conceivable, for example, for managing the risk of depletion of both power and water.

[0096] In this modified configuration, the risk determination module 3033 of the building module 30 may determine the risks of electricity and water individually, and the control execution module 3034 may perform individual controls for each. Alternatively, the control execution module 3034 of the building module 30 may perform more advanced integrated control if it is determined that there is a depletion risk for both electricity and water. Specifically, the level of electricity depletion risk and the level of water depletion risk are first compared. This comparison is performed, for example, by calculating the remaining amount of each resource as a percentage (sufficiency rate) of the respective depletion determination threshold, and determining that the resource with the lower sufficiency rate has a higher depletion risk. Based on the comparison result, the control priority of equipment that affects the consumption of both resources is dynamically changed. For example, if the risk of electricity is higher than the risk of water, the temperature of the hot water heater may be set lower to prioritize suppressing electricity consumption, and conversely, if the risk of water is high, hot water supply may be restricted to prioritize suppressing water consumption. In addition, a configuration that performs more proactive demand shift control is also possible as another modified configuration. Specifically, the prediction processing module 3032 predicts periods of power shortage and surplus, and the control execution module 3034 notifies the user's terminal device 10 via the information provision module 3035 with a suggestion such as, "Would you like to change the operation of the dryer in the afternoon, when power shortages are predicted, to the morning, when power is abundant?" If the user approves this, the control execution module 3034 automatically changes the operating time, achieving a more advanced optimization of power supply and demand without compromising convenience.

[0097] <Basic Computer Hardware Configuration> Figure 12 is a block diagram showing the basic hardware configuration of computer 90. Computer 90 includes at least a processor 901, main memory 902, auxiliary memory 903, and a communication interface IF991. These are electrically connected to each other by a communication bus.

[0098] The processor 901 is hardware for executing the instruction set written in a program. The processor 901 consists of an arithmetic unit, registers, peripheral circuits, etc.

[0099] Main memory 902 is used to temporarily store programs and data processed by programs, etc. For example, it is a volatile memory such as DRAM (Dynamic Random Access Memory).

[0100] Auxiliary storage device 903 refers to a storage device for saving data and programs. Examples include flash memory, HDD (Hard Disc Drive), magneto-optical disk, CD-ROM, DVD-ROM, and semiconductor memory.

[0101] The IF991 communication interface is an interface for inputting and outputting signals for communication with other computers via a network using wired or wireless communication standards. A network consists of various mobile communication systems, such as the internet, LANs, and wireless base stations. For example, a network includes 3G, 4G, and 5G mobile communication systems, LTE (Long Term Evolution), and wireless networks that can connect to the internet via designated access points (e.g., Wi-Fi®). When connecting wirelessly, communication protocols include, for example, Z-Wave®, ZigBee®, and Bluetooth®. When connecting via a wired connection, the network also includes connections made directly via USB (Universal Serial Bus) cables, etc.

[0102] Furthermore, by distributing all or part of each hardware configuration across multiple computers 90 and connecting them to each other via a network, a computer 90 can be virtually realized. Thus, the concept of computer 90 includes not only a computer 90 housed in a single enclosure or case, but also a virtualized computer system.

[0103] <Basic Functional Configuration of Computer 90> The functional configuration of the computer realized by the basic hardware configuration of computer 90 (Figure 12) will be explained. The computer comprises at least one functional unit: a control unit, a memory unit, and a communication unit.

[0104] Furthermore, the functional units of computer 90 can also be realized by distributing all or part of each functional unit across multiple computers 90 interconnected via a network. The concept of computer 90 includes not only a single computer 90 but also a virtualized computer system.

[0105] The control unit is realized when the processor 901 reads various programs stored in the auxiliary storage device 903, loads them into the main memory device 902, and executes processing according to those programs. The control unit can realize various functional units that perform information processing depending on the type of program. In this way, the computer is realized as an information processing device that performs information processing.

[0106] The memory unit is implemented by the main memory 902 and the auxiliary memory 903. The memory unit stores data, various programs, and various databases. The processor 901 can also reserve memory areas corresponding to the memory unit in the main memory 902 or the auxiliary memory 903 according to the program. The control unit can also cause the processor 901 to perform operations such as adding, updating, and deleting data stored in the memory unit according to the various programs.

[0107] A database, specifically a relational database, is used to manage and link together tabular data sets called masters, which are structurally defined by rows and columns. In a database, tables are called tables, masters are called masters, the columns of tables are called columns, and the rows of tables are called records. In a relational database, relationships can be established and linked between tables and masters. Typically, each table and master has a primary key column to uniquely identify records, but setting a primary key column is not mandatory. The control unit can instruct the processor 901 to add, delete, or update records in specific tables and masters stored in the memory unit, according to various programs. Furthermore, by storing data, various programs, and various databases in the memory unit, the information processing device and information processing system related to this disclosure can be considered to have been manufactured.

[0108] Furthermore, the databases and masters in this disclosure may include any data structures (lists, dictionaries, associative arrays, objects, etc.) in which information is structurally defined. Data structures also include data that can be considered as data structures by combining data with functions, classes, methods, etc., written in any programming language.

[0109] The communication unit is implemented by the communication IF991. The communication unit provides the functionality to communicate with other computers 90 via the network. The communication unit can receive information transmitted from other computers 90 and input it to the control unit. The control unit can cause the processor 901 to perform information processing on the received information according to various programs. The communication unit can also transmit information output from the control unit to other computers 90.

[0110] Furthermore, each of the above-mentioned configurations, functions, processing units, processing means, etc., may be implemented in hardware, either partially or entirely, by designing them as integrated circuits, for example. The present invention can also be implemented by software program code that realizes the functions of the embodiment. In this case, a storage medium on which the program code is recorded is provided to a computer, and the processor of that computer reads the program code stored in the storage medium. In this case, the program code read from the storage medium itself realizes the functions of the embodiment described above, and the program code itself and the storage medium on which it is stored constitute the present invention. Examples of storage media used to supply such program code include flexible disks, CD-ROMs, DVD-ROMs, hard disks, SSDs, optical disks, magneto-optical disks, CD-Rs, magnetic tapes, non-volatile memory cards, ROMs, and the like.

[0111] Furthermore, the program code that implements the functions described in this embodiment can be implemented in a wide range of programming or scripting languages, such as assembler, C / C++, Perl, Shell, PHP, and Java (registered trademark).

[0112] Furthermore, the program code for the software that implements the functions of the embodiment may be distributed via a network and stored in a storage means such as a computer's hard disk or memory, or in a storage medium such as a CD-RW or CD-R, and the computer's processor may read and execute the program code stored in the storage means or storage medium.

[0113] The functions realized by the components described herein may be implemented in a circuit or processing circuitry, including general-purpose processors, application-specific processors, integrated circuits, ASICs (Application Specific Integrated Circuits), CPUs (a Central Processing Unit), conventional circuits, and / or combinations thereof, programmed to realize the functions described herein. A processor is considered a circuit or processing circuitry, including transistors and other circuits. A processor may be a programmed processor that executes a program stored in memory. In this specification, circuitry, unit, and means are hardware programmed to perform or execute the functions described herein. Such hardware may be any hardware disclosed herein, or any hardware known to be programmed to perform or execute the functions described herein. If the hardware is a processor that is considered to be a type of circuitry, then the circuitry, means, or unit is a combination of hardware and software used to constitute the hardware and / or processor.

[0114] While several embodiments of this disclosure have been described above, these embodiments can be implemented in a variety of other forms, and various omissions, substitutions, and modifications are permitted without departing from the spirit of the invention. These embodiments and their variations are included in the scope and spirit of the invention, as well as in the claims and their equivalents.

[0115] (Note) The details described in each of the above embodiments are noted below. (Note 1) A program for operating a computer comprising a processor and memory, the program causing the processor to perform the following steps: acquire data including current state information, which at least includes the remaining charge of a battery in an off-grid environment independent of the power infrastructure, and past supply and demand information, which shows past power supply and demand performance; predict the power supply and demand balance for a predetermined period using a machine learning model based on the past supply and demand information; calculate the change in the remaining charge of the battery over a predetermined period by applying the predicted supply and demand balance, starting from the current remaining charge of the battery included in the current state information; determine the risk of power depletion by evaluating whether the change in the remaining charge falls below a predetermined threshold necessary to maintain daily life; and, if it is determined that there is a risk of power depletion, perform predetermined processing to reduce the risk of power depletion.

[0116] (Note 2) The program described in (Appendix 1) includes controlling the operation of at least one power-consuming device present in an off-grid environment.

[0117] (Note 3) The program described in (Appendix 1) or (Appendix 2) includes sending a notification to the administrator prompting them to supply power to the off-grid environment from an external power source.

[0118] (Note 4) The prescribed processing is a program described in any one of the items (Appendix 1) to (Appendix 3), which includes notifying the user's terminal device of the risk of power depletion.

[0119] (Note 5) In the step of acquiring data, future weather information within a predetermined period is further acquired. A program described in any of (Appendix 1) to (Appendix 4) that, in the step of predicting the balance of electricity supply and demand, further uses future weather information to predict the balance of electricity supply and demand.

[0120] (Note 6) Future weather information includes solar radiation forecasts for a specified period, as described in (Appendix 5).

[0121] (Note 7) A program as described in any of (Appendix 1) to (Appendix 6), wherein in the step of determining the risk of power depletion, the degree of the risk of power depletion is determined, and in the step of executing a predetermined process, one of a predetermined set of processes is selected and executed according to the determined degree of the risk of power depletion.

[0122] (Note 8) In the step of executing the predetermined process, if the degree of the risk of power depletion is at a first level, a process including control to mitigate the operation of the power consumption device is selected. The program described in (Appendix 7) selects to send a notification to the administrator prompting them to supply power to the off-grid environment from an external power source if the degree of the risk of power depletion is at a second level that is more serious than the first level.

[0123] (Note 9) The program described in (Appendix 8) includes at least one of the following: easing the temperature setting of the air conditioning system or adjusting the temperature setting of the hot water supply system.

[0124] (Note 10) A program as described in any of (Appendix 1) to (Appendix 9), which causes the processor to perform the step of causing the user's terminal device to display a score indicating how much margin there was in the actual power consumption relative to a predetermined threshold.

[0125] (Note 11) The program described in (Appendix 10) further causes the processor to perform a step of providing the user with an incentive based on their score.

[0126] (Note 12) A program for operating a computer comprising a processor and memory, comprising the steps of: acquiring data from the processor including current state information, which at least includes the remaining amount in a water storage tank in an off-grid environment independent of water infrastructure, and past supply and demand information, which shows past water supply and demand performance; predicting the water supply and demand balance for a predetermined period using a machine learning model based on the past water supply and demand information; calculating the change in the remaining amount in the water storage tank over a predetermined period by applying the predicted supply and demand balance, starting from the current remaining amount in the water storage tank included in the current state information; determining the risk of water depletion by evaluating whether the calculated change in the remaining amount falls below a predetermined threshold necessary to maintain life; and, if it is determined that there is a risk of water depletion, causing the program to execute predetermined processing to reduce water consumption. A program that executes the command.

[0127] (Note 13) A predetermined process for reducing water consumption includes controlling the operation of at least one water-consuming device present in an off-grid environment, as described in (Appendix 12).

[0128] (Note 14) The prescribed processing for reducing water consumption includes the program described in (Appendix 12) or (Appendix 13), which notifies the user's terminal device of the risk of water depletion.

[0129] (Note 15) In the step of acquiring data, future weather information within a predetermined period is further acquired. A program described in any of (Appendix 12) to (Appendix 14), which, in the step of predicting the water supply and demand balance, further uses future weather information to predict the water supply and demand balance.

[0130] (Note 16) Future weather information includes precipitation forecasts for a specified period, as described in (Appendix 15).

[0131] (Note 17) The program described in (Note 1) further causes the processor to perform the following steps: to acquire the remaining amount of water in a water storage tank as current state information in an off-grid environment, and to acquire past supply and demand information showing past water supply and demand performance; to predict the water supply and demand balance for a predetermined period using a machine learning model based on the past water supply and demand information; to calculate the change in the remaining amount of water in the storage tank over a predetermined period by applying the predicted water supply and demand balance starting from the current remaining amount of water in the storage tank included in the current state information; and to determine the risk of water depletion by evaluating whether the change in the remaining amount of water in the storage tank falls below a predetermined threshold necessary to maintain life; and the predetermined processing for reducing the risk of electricity depletion includes, when it is determined that there is both a risk of electricity depletion and a risk of water depletion, a process that compares the degree of both risks and, based on the comparison result, integrally controls the operation of a device that affects at least one of electricity consumption and water consumption.

[0132] (Note 18) An off-grid building module that is independent of the power infrastructure and can accommodate people, comprising: a power generator; a power storage device for storing the electricity generated by the power generator; a power consumption device for consuming electricity; a processor and memory, which acquires data including current state information that includes at least the remaining amount of the power storage device in an off-grid environment independent of the power infrastructure, and past supply and demand performance information showing past power supply and demand performance; uses a machine learning model based on the past supply and demand performance information to predict the balance of power supply and demand over a predetermined period; applies the predicted balance of supply and demand over a predetermined period starting from the current remaining amount of the power storage device included in the current state information to calculate the trend of the remaining amount of the power storage device over a predetermined period; evaluates whether the calculated trend of the remaining amount falls below a predetermined threshold necessary to maintain life, thereby determining the risk of power depletion; and if it is determined that there is a risk of power depletion, executes predetermined processing to reduce the risk of power depletion.

[0133] (Note 19) A method to be performed on a computer having a processor and memory, wherein the processor performs all steps in any of the programs described in (Appendix 1) to (Appendix 17).

[0134] (Note 20) A system comprising means for executing all steps in any of the programs described in (Appendix 1) to (Appendix 17). [Explanation of symbols]

[0135] 1... System 10…Terminal device 12…Communication IF 13…Input device 14…Output device 15…Memory 16…Storage 19… Processor 20... Server 22...Communication IF 23…Input / Output Interface 25…Memory 2 hours… storage 29… Processor 30…Off-grid building module 40…IoT devices 41…Production and storage devices 42…Controlled devices 50… Weather information provision service 80… Wide-area network 81…Local network

Claims

1. A program for operating a computer that includes a processor and memory, The aforementioned processor, The steps include acquiring data that includes current status information, which at least includes the remaining charge of a battery in an off-grid environment independent of the power infrastructure, and historical supply and demand information, which shows past power supply and demand performance. Based on the aforementioned past supply and demand data, a machine learning model is used to predict the supply and demand balance of electricity for a predetermined period. The steps include: calculating the change in the remaining battery capacity over a predetermined period by applying the predicted supply and demand balance, starting from the current remaining battery capacity included in the current status information; The steps include determining the risk of electricity depletion by evaluating whether the trend of the remaining amount falls below a predetermined threshold necessary to maintain daily life, The steps include obtaining further information on the current state of the off-grid environment, specifically the remaining amount of water in the water storage tanks, and further obtaining historical supply and demand information showing past water supply and demand performance, The steps include: predicting the balance of water supply and demand over a predetermined period using a machine learning model based on the historical supply and demand data for water; The steps include: calculating the change in the remaining amount of water in the storage tank over a predetermined period by applying the predicted water supply and demand balance, starting from the current remaining amount of water in the storage tank included in the current status information; The steps include determining the risk of water depletion by evaluating whether the remaining amount in the aforementioned water storage tank falls below a predetermined threshold necessary for maintaining daily life, If it is determined that there is a risk of the aforementioned power depletion, the system will perform a predetermined process to reduce the risk of the aforementioned power depletion. The predetermined process for reducing the risk of power depletion includes a program that, when it is determined that both the risk of power depletion and the risk of water depletion exist, integrates the control of the operation of devices that affect at least one of the power consumption and water consumption based on the degree of both risks.

2. The program according to claim 1, wherein the predetermined process includes a process of comparing the degree of the risk of electricity depletion with the degree of the risk of water depletion, and based on the comparison result, integrally controlling the operation of a device that affects at least one of electricity consumption and water consumption.

3. The program according to claim 1, wherein the predetermined processing includes controlling the operation of at least one power consumption device present in the off-grid environment.

4. The program according to claim 1, wherein the predetermined process includes sending a notification to the administrator prompting them to supply power to the off-grid environment from an external power source.

5. The program according to claim 1, wherein the predetermined processing includes notifying the user's terminal device of the risk of power depletion.

6. In the step of acquiring the data, further acquire future weather information within the predetermined period, The program according to claim 1, wherein, in the step of predicting the supply and demand balance of electricity, the program further uses the future weather information to predict the supply and demand balance of electricity.

7. The program according to claim 6, wherein the future weather information includes a solar radiation forecast for the predetermined period.

8. In the step of determining the risk of power depletion, the degree of the risk of power depletion is determined, The program according to claim 1, wherein in the step of executing the predetermined process, one of a predetermined number of processes is selected and executed according to the degree of the determined power depletion risk.

9. In the step of performing the predetermined process, if the degree of the risk of power depletion is at a first level, the process is selected to include control to mitigate the operation of at least one power consumption device present in the off-grid environment. The program according to claim 8, which, when the degree of the risk of power depletion is at a second level more serious than the first level, selects to send a notification to the administrator prompting them to supply power to the off-grid environment from an external power source.

10. The program according to claim 9, wherein the control to mitigate the operation includes at least one of mitigating the temperature setting of the air conditioning unit or adjusting the temperature setting of the hot water supply unit.

11. The processor further comprises: The program according to claim 1, which causes the user's terminal device to display a score indicating how much margin there was in the actual power consumption relative to a predetermined threshold.

12. The processor further comprises: The program according to claim 11, which causes the user to perform the step of providing an incentive to the user according to the score.

13. The processor further comprises: The program according to claim 1, which, when it is determined that there is a risk of water depletion, causes the program to perform a predetermined process to reduce water consumption.

14. The program according to claim 13, wherein the predetermined process for suppressing water consumption includes controlling the operation of at least one water-consuming device present in the off-grid environment.

15. The program according to claim 13, wherein the predetermined process for reducing water consumption includes notifying the user's terminal device of the risk of water depletion.

16. In the step of acquiring the data, further acquire future weather information within the predetermined period, The program according to claim 1, wherein, in the step of predicting the water supply and demand balance, the program further uses the future weather information to predict the water supply and demand balance.

17. The program according to claim 16, wherein the future weather information includes a precipitation forecast for the predetermined period.

18. A power generation device, A power storage device that stores the electricity generated by the aforementioned power generation device, Power-consuming devices that consume electricity, A water storage tank for storing water, A water-consuming device that consumes water, Equipped with a processor and memory, The system acquires data including current status information, which includes at least the remaining capacity of the energy storage device in an off-grid environment independent of the power infrastructure, and historical supply and demand information, which shows past power supply and demand performance. Based on the aforementioned past supply and demand data, a machine learning model is used to predict the supply and demand balance of electricity for a predetermined period. Starting from the current remaining amount of the energy storage device included in the current status information, the change in the remaining amount of the energy storage device over the predetermined period is calculated by applying the predicted supply and demand balance. By evaluating whether the calculated trend of the remaining amount falls below a predetermined threshold necessary to maintain daily life, the risk of electricity depletion is determined. As current status information in an off-grid environment, the remaining amount in the water storage tank is further acquired, and historical supply and demand information showing past water supply and demand performance is further acquired. Based on the aforementioned historical water supply and demand data, a machine learning model is used to predict the water supply and demand balance for a predetermined period. Starting from the current remaining amount of the water storage tank included in the current status information, the change in the remaining amount of the water storage tank over the predetermined period is calculated by applying the predicted water supply and demand balance. By evaluating whether the remaining amount in the aforementioned water storage tank falls below a predetermined threshold necessary for maintaining daily life, the risk of water depletion is determined. The system includes a control unit that, when it is determined that there is a risk of the aforementioned power supply being depleted, performs a predetermined process to reduce the risk of the aforementioned power supply being depleted, The predetermined process for reducing the risk of power depletion includes, when it is determined that both the risk of power depletion and the risk of water depletion exist, a process that integrally controls the operation of a device that affects at least one of the power consumption and water consumption based on the degree of both risks. An off-grid building module that can accommodate people even when powered by an electrical infrastructure.

19. A method to be performed on a computer comprising a processor and memory, wherein the processor performs all steps of a program according to any one of claims 1 to 17.

20. A system comprising means for performing all steps in the program described in any one of Claims 1 to 17.

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