Heat storage system and information processing device

Abstract

This heat accumulating system comprises: a hot water supply device for performing a hot water supply operation in accordance with an operating schedule; a storage means for storing past hot water usage transition data for the hot water supply device; an acquiring means for acquiring weather forecast information for a region in which the hot water supply device is installed, for a target date on which the operating schedule is to be set; a supply capability predicting means for obtaining a hot water supply capability of the hot water supply device for each time on the target date, on the basis of the weather forecast information; a demand predicting means for predicting hot water usage for each time on the target date, on the basis of the past hot water usage transition data; and a planning means for determining the operating schedule without a hot water supply insufficiency on the basis of the hot water supply capability for each time, obtained by the supply capability predicting means for the target date, and the hot water usage for each time, predicted by the demand predicting means.

Description

[Technical Field]

[0001] The present disclosure relates to a heat storage system having a hot water supply device and an information processing device connected to the hot water supply device. [Background technology]

[0002] Because heat pump water heaters have a low hot water supply capacity per unit time, they need to store hot water in a tank or the like in advance. Therefore, to prevent a hot water shortage when a user is using hot water, the water heater completes storing hot water in the tank earlier than the user actually starts using the hot water. If there is a large gap between the time when the user starts using hot water and the time when the hot water storage is completed, the amount of heat released from the hot water stored in the tank increases, resulting in a decrease in hot water supply efficiency.

[0003] Conventionally, an operation plan creation system that creates an operation plan for a water heater has been proposed with the aim of reducing the utility costs of the water heater (see, for example, Patent Document 1). Patent Document 1 discloses an operation plan creation device that has an acquisition unit that acquires water heater information including hot water demand, weather data, electricity rate information, etc. from the water heater, and a hot water supply schedule creation unit. The hot water supply schedule creation unit has a water heater state estimation unit that estimates the state of the water heater based on a water heater model, and a hot water supply schedule determination unit that creates an operation plan for the water heater for a predetermined period that minimizes utility costs based on the estimated water heater state. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2017-219249 Summary of the Invention [Problem to be solved by the invention]

[0005] However, the operation plan creation device disclosed in Patent Document 1 aims to reduce utility costs, and does not sufficiently reflect hot water supply demand in the hot water supply schedule, which may result in a hot water shortage.

[0006] The present disclosure has been made to solve the above-mentioned problems, and provides a heat storage system and an information processing device that suppress the occurrence of hot water shortages. [Means for solving the problem]

[0007] The thermal storage system of the present disclosure comprises a water heating device that performs hot water supply operation according to an operation schedule, a storage means for storing trend data on past hot water usage of the water heating device, an acquisition means for acquiring weather forecast information for the area in which the water heating device is installed for the target day for which the operation schedule is set, a supply capacity prediction means for calculating the hot water supply capacity of the water heating device for each hour on the target day based on the weather forecast information, a demand prediction means for predicting the hot water usage for each hour on the target day based on the trend data on past hot water usage, and a planning means for determining the operation schedule that does not cause a hot water shortage based on the hot water supply capacity for each hour calculated by the supply capacity prediction means and the hot water usage for each hour predicted by the demand prediction means for the target day.

[0008] The information processing device of the present disclosure is an information processing device that determines an operation schedule for a water heating device, and includes: a storage means for storing trend data on past hot water usage of the water heating device; an acquisition means for acquiring weather forecast information for the area where the water heating device is installed for the target day for which the operation schedule is set; a supply capacity prediction means for calculating the hot water supply capacity of the water heating device for each hour on the target day based on the weather forecast information; a demand prediction means for predicting the hot water usage for each hour on the target day based on the trend data on past hot water usage; and a planning means for determining the operation schedule that does not result in a hot water shortage based on the hot water supply capacity for each hour determined by the supply capacity prediction means and the hot water usage for each hour predicted by the demand prediction means for the target day. [Effects of the Invention]

[0009] According to the present disclosure, the hot water supply capacity for each hour is predicted based on weather forecast information for the area where the water heater is installed, the hot water demand is predicted based on historical hot water usage trend data, and an operation schedule that prevents hot water shortages is created based on these predicted values. The hot water supply capacity is predicted with high accuracy based on weather forecast information for the area where the water heater is installed, the hot water demand is predicted with high accuracy based on how users use hot water, and an optimal operation schedule is determined based on these predicted values, thereby preventing hot water shortages. [Brief explanation of the drawings]

[0010] [Figure 1] 1 is a block diagram showing a configuration example of a heat storage system according to a first embodiment. [Figure 2] 2 is a circuit diagram showing an example of the configuration of the water heater shown in FIG. 1. FIG. [Figure 3] 2 is a block diagram showing an example of the configuration of an information providing server shown in FIG. 1. FIG. [Figure 4] 4 is a diagram showing an example of weather forecast information provided from the information providing server shown in FIG. 3 to the information processing device. FIG. [Figure 5] 2 is a block diagram showing an example of the configuration of the information processing device shown in FIG. 1. FIG. [Figure 6] FIG. 6 is a diagram illustrating an example of a management table illustrated in FIG. 5. [Figure 7] FIG. 2 is a schematic diagram for explaining an example of a method for dividing a target area into a plurality of regions in the first embodiment. [Figure 8] 6 is a diagram showing another example of the management table stored in the storage device shown in FIG. 5. FIG. [Figure 9] 6 is a diagram showing an example of capability information stored in the storage device shown in FIG. 5. FIG. [Figure 10] 3 is a table showing examples of combinations of areas where the hot water heaters shown in FIG. 2 are installed and amounts of hot water consumed by the hot water heaters in the first embodiment. [Figure 11]6 is a graph showing time series changes in the hot water supply capacity predicted by the supply capacity prediction means shown in FIG. 5 and the hot water consumption predicted by the demand prediction means. [Figure 12] 12 is a graph for explaining a method in which the planning means shown in FIG. 5 determines an operation schedule based on the graph shown in FIG. 11. [Figure 13] 6 is a hardware configuration diagram showing an example of the configuration of a controller shown in FIG. 5. [Figure 14] 6 is a hardware configuration diagram showing another example of the configuration of the controller shown in FIG. 5. [Figure 15] FIG. 3 is a sequence diagram showing an operation procedure of the heat storage system according to the first embodiment. [Figure 16] 4 is a diagram for explaining the flow of a process for determining an operation schedule for a water heating apparatus, performed by an information processing device according to the first embodiment. FIG. [Figure 17] FIG. 4 is a block diagram showing another configuration example of the heat storage system according to the first embodiment. [Figure 18] 18 is a block diagram showing an example of the configuration of the information processing device shown in FIG. 17. FIG. DETAILED DESCRIPTION OF THE INVENTION

[0011] Embodiment 1 The configuration of the heat storage system of the first embodiment will be described. FIG. 1 is a block diagram showing an example of the configuration of the heat storage system according to the first embodiment. Heat storage system 1 of the first embodiment has a plurality of water heating apparatuses 2-1 to 2-n (n is an integer of 2 or more) and an information processing device 3 that determines the operation schedule of water heating apparatuses 2-1 to 2-n. Water heating apparatuses 2-1 to 2-n and information processing device 3 are connected via network 100. An information providing server 6 that provides weather forecast information to information processing device 3 is connected to network 100. In the first embodiment, the case where there are two or more water heating apparatuses 2-k (k is any integer from 1 to n) will be described, but there may be only one water heating apparatus 2-k.

[0012] Network 100 is, for example, the Internet. Each of water heating apparatuses 2-1 to 2-n and information processing device 3 transmits and receives data according to a predetermined communication protocol. Information processing device 3 and information providing server 6 transmit and receive data according to a predetermined communication protocol. The communication protocol is, for example, TCP (Transmission Control Protocol) / IP (Internet Protocol). Each of water heating apparatuses 2-1 to 2-n, information processing device 3, and information providing server 6 is assigned a device identifier ID, which is a mutually different identifier. The device identifier ID is, for example, an IP address. When data is transmitted and received between two apparatuses connected via network 100, the IP address indicating the data destination and the IP address indicating the data sender are set in the header of the data, but a description of this will be omitted below.

[0013] The configuration of water heating devices 2-1 to 2-n shown in Fig. 1 will be described. Since water heating devices 2-1 to 2-n have the same configuration, the configuration of water heating device 2-1 will be described here. Fig. 2 is a circuit diagram showing an example configuration of the water heating device shown in Fig. 1.

[0014] As shown in Fig. 2, water heating apparatus 2-1 has heat source unit 10 that generates heat, tank 21 that stores water, circulation circuit 24 that circulates water between heat source unit 10 and tank 21, and hot water controller 40 that controls the operation of water heating apparatus 2-1. Water heating apparatus 2-1 is also provided with operation terminal 20 that allows the user to input a set temperature Ts. Operation terminal 20 has, for example, a touch panel (not shown) and a display (not shown). Operation terminal 20 may also have a speaker (not shown). Operation terminal 20 is connected to hot water controller 40 via signal line 23.

[0015] The heat source unit 10 functions as a heat pump water heater that generates hot water. The heat source unit 10 has a compressor 12, a water heat exchanger 13, an expansion valve 15, a heat source-side heat exchanger 16, and a fan 17 that supplies air to the heat source-side heat exchanger 16. The compressor 12, the water heat exchanger 13, the expansion valve 15, and the heat source-side heat exchanger 16 are connected via refrigerant piping 18 to form a refrigerant circuit 11 through which the refrigerant circulates. The hot water generated by the refrigerant circuit 11 is transferred via the water heat exchanger 13 to water circulating in a circulation circuit 24, thereby heating the water. The heat source unit 10 is provided with a temperature sensor 14 that detects an intake temperature Tin, which is the temperature of the air drawn in by the fan 17. The compressor 12, the expansion valve 15, the fan 17, and the temperature sensor 14 are connected to a hot water supply controller 40 via a signal line 23.

[0016] The compressor 12 draws in a refrigerant in a low-temperature and low-pressure state, compresses the drawn-in refrigerant, and discharges it as a refrigerant in a high-temperature and high-pressure state. The compressor 12 is an inverter-type compressor whose capacity can be adjusted by controlling the operating frequency of the motor. The water heat exchanger 13 is a refrigerant-to-heat medium heat exchanger that exchanges heat between the refrigerant and water. The water heat exchanger 13 exchanges heat between the refrigerant and water to heat the water. The expansion valve 15 is a pressure-reducing valve that reduces the pressure of the refrigerant to expand it. The expansion valve 15 is, for example, an electronic expansion valve whose opening degree can be adjusted. The heat source-side heat exchanger 16 is a heat exchanger that exchanges heat between the refrigerant and air. The fan 17 adjusts the amount of air supplied to the heat source-side heat exchanger 16 by adjusting the rotation speed of the motor.

[0017] Tank 21 is supplied with water from the outside via a water inlet (not shown), and supplies hot water to a load (not shown) via a supply port (not shown). Tank 21 is provided with outlet 27, which is an opening through which water flows out of tank 21 to water heat exchanger 13, and inlet 25, which is an opening through which water that has flowed through water heat exchanger 13 returns to tank 21. Inlet 25 of tank 21 is connected to water heat exchanger 13 by piping 26a, and outlet 27 is connected to water heat exchanger 13 by piping 26b.

[0018] Circulation circuit 24 has circulation pump 22. Circulation pump 22 is provided on pipe 26b. Circulation pump 22 is a pump that pumps out water that has been drawn in to circulate the water between tank 21 and water heat exchanger 13. Circulation pump 22 is a pump that can adjust the rotation speed H. Circulation pump 22, water heat exchanger 13, and tank 21 are connected via pipes 26a and 26b, forming circulation circuit 24 through which water circulates. Circulation pump 22 is connected to hot water supply controller 40 via signal line 23.

[0019] An inlet temperature sensor 28 is provided on pipe 26b upstream of water heat exchanger 13. An outlet temperature sensor 29 is provided on pipe 26a downstream of water heat exchanger 13. Inlet temperature sensor 28 detects inlet temperature Twin, which is the temperature of water flowing into water heat exchanger 13. Outlet temperature sensor 29 detects outflow temperature Twout, which is the temperature of water flowing out of water heat exchanger 13. Inlet temperature sensor 28 and outlet temperature sensor 29 are connected to hot water supply controller 40 via signal line 23. Each of inlet temperature sensor 28 and outlet temperature sensor 29 transmits its detection value to hot water supply controller 40 via signal line 23.

[0020] The hot water supply controller 40 shown in FIG. 2 will be described. The hot water supply controller 40 is, for example, a microcomputer. As shown in FIG. 2, the hot water supply controller 40 has a memory 41 that stores a program, and a processor 42 such as a CPU (Central Processing Unit) that executes processing in accordance with the program stored in the memory 41. The memory 41 is a storage means that stores trend data on the amount of hot water usage of the hot water supply device 2-1. The trend data on the amount of hot water usage is data that indicates a time series change in the amount of hot water usage used per unit time (for example, one hour). The memory 41 updates the trend data on the amount of hot water usage stored therein at a period tp1. The period tp1 is, for example, one week. That is, in the first embodiment, the memory 41 stores trend data on the amount of hot water usage for one week. The trend data on the amount of hot water usage stored in the memory 41 is not limited to one week, but may be one day or one month.

[0021] Hot water supply controller 40 receives an operation schedule from information processing device 3 via network 100. Hot water supply controller 40 reads trend data of hot water usage from memory 41 at period tp1 and transmits it to information processing device 3 via network 100. When hot water supply controller 40 transmits trend data to information processing device 3, hot water supply controller 40 sets the device identifier ID of information processing device 3, which indicates the destination, and the device identifier ID of hot water supply device 2-1, which indicates the source, in the trend data.

[0022] When the hot water supply controller 40 receives the operation schedule from the information processing device 3, it controls the operation frequency fc of the compressor 12, the rotation speed of the fan 17, and the opening degree of the expansion valve 15 based on the inflow temperature Twin, the outflow temperature Twout, and the set temperature Ts in accordance with the operation schedule. Note that the tank 21 may be provided with a sensor for detecting the volume of water and a sensor for detecting the temperature of the water accumulated in the tank 21.

[0023] Here, with reference to FIG. 2 , the flow of refrigerant in the refrigerant circuit 11 will be described. The refrigerant drawn into the compressor 12 is compressed by the compressor 12 and discharged in a high-temperature, high-pressure state. The refrigerant discharged from the compressor 12 flows into the water heat exchanger 13. In the water heat exchanger 13, the refrigerant is cooled by heat exchange with water circulating through the circulation circuit 24. Through this heat exchange, the water is heated by the refrigerant. The cooled refrigerant flows into the expansion valve 15. In the expansion valve 15, the refrigerant is depressurized and expanded. The depressurized and expanded refrigerant flows into the heat source-side heat exchanger 16. In the heat source-side heat exchanger 16, the refrigerant is heated by heat exchange with air. The heated low-temperature, low-pressure refrigerant is drawn into the compressor 12.

[0024] The flow of water in the circulation circuit 24 will be described with reference to Figure 2. Water is drawn from the tank 21 into the circulation pump 22 via pipe 26b connected to the outlet 27 of the tank 21. The water drawn into the circulation pump 22 flows into the water heat exchanger 13. In the water heat exchanger 13, the water is heated by heat exchange with the refrigerant. The heated water flows into the tank 21 via pipe 26a.

[0025] Next, the configuration of the information providing server 6 shown in Fig. 1 will be described. The information providing server 6 is a server that publishes on the web weather forecast information received from an external information processing device (not shown) via the network 100. The external information processing device (not shown) is, for example, a computer that simulates future weather based on multiple weather observation data received from multiple observation points or observation data received from a weather satellite.

[0026] Fig. 3 is a block diagram showing an example of the configuration of the information providing server shown in Fig. 1. The information providing server 6 has a storage unit 7 and an information providing controller 8. The storage unit 7 is, for example, an HDD (Hard Disk Drive). The information providing controller 8 has a memory (not shown) that stores programs, and a processor (not shown) that executes processing in accordance with the programs stored in the memory.

[0027] The storage unit 7 stores weather forecast information received from an external information processing device (not shown) via the information provision controller 8. The information provision controller 8 includes a timer 50 for measuring time, a management means 51, and an information provision means 53. The management means 51 stores the weather forecast information received from the external information processing device (not shown) via the network 100 at a fixed cycle tp2 in the storage unit 7. The cycle tp2 at which the management means 51 receives weather forecast information from the external information processing device (not shown) is, for example, three hours. The management means 51 receives multiple pieces of weather forecast information with different location information from the external information processing device (not shown).

[0028] The weather information providing means 52 reads out weather forecast information from the storage unit 7 at intervals of tp2 and transmits it to the information processing device 3. The weather forecast information includes location information, and outdoor air temperature Tout [°C], outdoor air humidity RH, and weather information predicted at intervals of tp2 from the current time. The weather information providing means 52 may transmit multiple pieces of weather forecast information with different location information to the information processing device 3.

[0029] The location information is, for example, geographic coordinates expressed by latitude and longitude. In the first embodiment, it is assumed that target area OBR in which water heating apparatuses 2-1 to 2-n are installed is in the northern hemisphere, and latitude is represented as NL and longitude as EL. NL means northern latitude, and EL means eastern longitude. Outdoor air humidity RH is relative humidity [%]. Weather information is information related to weather such as sunny, cloudy, and rainy. Weather information may also be the probability of precipitation [%]. In this case, weather may be classified into sunny, cloudy, rainy, and the like depending on the probability of precipitation. For example, if the probability of precipitation is less than 30%, the weather is considered sunny, if 30%≦probability of precipitation<70%, the weather is considered cloudy, and if the probability of precipitation is greater than or equal to 70%, the weather is considered rainy or snowy.

[0030] FIG. 4 is a diagram showing an example of weather forecast information provided to the information processing device from the information providing server shown in FIG. 3. As shown in FIG. 4, the weather forecast information includes outdoor air temperature Tout [°C], outdoor air humidity RH [%], and weather information for every three hours from 6:00 AM to 9:00 PM. In the weather forecast information shown in FIG. 4, the weather information changes from sunny in the morning to rain in the afternoon. The outdoor air temperature Tout changes from about 6:00 AM, with the temperature rising from the afternoon onwards, but dropping in the afternoon. The outdoor air humidity RH changes from low humidity in the morning to rising in the afternoon.

[0031] Next, the configuration of the information processing device 3 shown in Fig. 1 will be described. Fig. 5 is a block diagram showing an example configuration of the information processing device shown in Fig. 1. The information processing device 3 has a storage device 4 and a controller 5. The storage device 4 is, for example, an HDD. The controller 5 is, for example, a microcomputer.

[0032] Storage device 4 is a storage means for storing a management table, capacity information, weather forecast information, and operation history. The management table is a table for managing information about each water heating device of water heating devices 2-1 to 2-n. The capacity information is information used to predict the hot water supply capacity of each water heating device. The weather forecast information is weather forecast information for each region in which water heating devices 2-1 to 2-n are installed. The operation history is trend data of hot water usage received from water heating devices 2-1 to 2-n. Storage device 4 stores, for example, trend data of hot water usage of water heating devices 2-1 to 2-n for the past year as the operation history.

[0033] Fig. 6 is a diagram showing an example of the management table shown in Fig. 5. In the management table shown in Fig. 6, location information of the water heating apparatus, the region to which the water heating apparatus belongs, a file name of weather forecast information, a file name of capacity information, and a file name of operation history are registered in correspondence with the apparatus identifier ID of each water heating apparatus of water heating apparatus 2-1 to 2-n. The weather forecast information and operation history stored in storage device 4 are updated sequentially over time, so time (t) information is attached so that the most recent file can be identified. Fig. 6 shows a case where target region OBR is divided into multiple regions RG1 to RGm (m is an integer of 2 or greater). j in RGj is an arbitrary integer between 2 and m.

[0034] The management table shown in Figure 6 will be explained for water heating apparatus 2-1. The apparatus identifier ID of water heating apparatus 2-1 is ad2-1. The location information of water heating apparatus 2-1 is expressed by geographical coordinates (NL2-1, EL2-1). The geographical coordinates (NL2-1, EL2-1) mean that water heating apparatus 2-1 is installed at a location specified by latitude NL2-1 and longitude EL2-1. The location of water heating apparatus 2-1 belongs to region RG1. The file name of the weather forecast information for region RG1 to which water heating apparatus 2-1 belongs is WRsat(t). The file name of the performance information of water heating apparatus 2-1 is HCa2-1. The file name of the file in which trend data of hot water usage by water heating apparatus 2-1 is saved is Oph2-1(t).

[0035] Here, an example of a method for dividing target area OBR, in which water heating apparatuses 2-1 to 2-n are installed, into a plurality of areas will be described. Fig. 7 is a schematic diagram for describing an example of a method for dividing the target area into a plurality of areas in the first embodiment.

[0036] As shown in Fig. 7, the target area OBR is divided from north to south into five climate zones, including subarctic, cool temperate, warm temperate, and subtropical. The white circles in Fig. 7 are plots of the location coordinates of each water heating device. In Fig. 7, the location of each water heating device is indicated by a device identifier ID. The black circles are locations indicated by location information included in weather forecast information provided from information providing server 6 to information processing device 3. For example, the weather forecast information described with reference to Fig. 4 for regions RG1 to RGm is weather forecast information for location px1.

[0037] As shown in Figure 7, water heating apparatuses 2-1 and 2-2 belong to the same region RG1. Region RG1 is located the northernmost of regions RG1 to RGm. Water heating apparatus ad2-n belongs to region RGm, which is located the southernmost. Figure 7 shows the case where n=8 and m=5.

[0038] FIG. 8 is a diagram showing another example of the management table stored in the storage device shown in FIG. 5. FIG. 8 is a region management table showing the correspondence between the regions shown in FIG. 7 and weather forecast information. In the region management table shown in FIG. 8, the range of location information for region RGj and the file name of weather forecast information applied to region RGj are registered corresponding to region RGj. As shown in FIG. 8, multiple geographical coordinates are registered in the range of location information for region RGj. The range enclosed by connecting these multiple geographical coordinates in order with broken lines becomes region RGj. The file name WRsat(t) for region RG1 is the file name of the latest weather forecast information for position px1 shown in FIG. 7.

[0039] In the first embodiment, the case where target region OBR is divided into a plurality of regions based on a plurality of climates has been described, but the information used as the basis for dividing the regions is not limited to climate. The regions may be divided, for example, by address notation based on administrative divisions. Furthermore, when a new water heating apparatus 2-z (z is an integer greater than n) is registered in the management table shown in FIG. 6, the region RGj of water heating apparatus 2-z is registered in the management table by the maintenance worker together with its location information. In this case, when the maintenance worker inputs the location information of water heating apparatus 2-z into controller 5, controller 5 may register the region RGj of water heating apparatus 2-z in the management table shown in FIG. 6. This case will be described later.

[0040] Controller 5 has timer 30 that measures time, acquisition means 31, management means 32, supply capacity prediction means 33, demand prediction means 34, planning means 35, and update means 36. Upon acquiring weather forecast information from information providing server 6, acquisition means 31 stores the acquired weather forecast information in storage device 4. Acquisition means 31 stores transition data of hot water usage received from hot water heaters 2-1 to 2-n in storage device 4.

[0041] When the weather forecast information and operation history of water heating apparatuses 2-1 to 2-n are updated, management means 32 registers the file names of the updated weather forecast information and operation history in a management table. Furthermore, management means 32 instructs supply capacity prediction means 33 and demand prediction means 34 to perform calculations for each water heating apparatus before the start of operation on target day Dx for which the operation schedule is set for water heating apparatuses 2-1 to 2-n. For example, management means 32 instructs supply capacity prediction means 33 and demand prediction means 34 to perform calculations for water heating apparatuses 2-1 to 2-n at 4:00 a.m. every day.

[0042] Furthermore, when water heating apparatus 2-z is newly registered in the management table, and location information is input by the management operator, management means 32 refers to the region management table shown in Fig. 8. Management means 32 identifies region RGj to which water heating apparatus 2-z belongs from the input location information and the region management table. Management means 32 then adds a column for water heating apparatus 2-z to the management table shown in Fig. 6, and registers the location information and region RGj in association with the apparatus identifier ad2-z of water heating apparatus 2-z.

[0043] Supply capacity prediction means 33 calculates the hot water supply capacity for each hour of target day Dx of water heating apparatus 2-k based on weather forecast information. Specific operation of supply capacity prediction means 33 will be described with reference to FIG. 9. FIG. 9 is a diagram showing an example of capacity information stored in the storage device shown in FIG. 5. FIG. 9 is a capacity table that determines the hot water supply capacity based on the outdoor air temperature Tout and the outdoor air humidity RH. The vertical axis of FIG. 9 is the coefficient of performance COPd, and the horizontal axis of FIG. 9 is the outdoor air temperature Tout. The coefficient of performance COPd ≈ (hot water supply capacity Qd / compressor power consumption [kW]). The amount of hot water supplied by water heating apparatus 2-k corresponds to the amount of hot water used by the user, and if the amount of hot water used per unit time is Q, then Q ∝ Qd holds.

[0044] Supply capacity prediction means 33 references the capacity table stored in storage device 4 and calculates the hot water supply capacity of water heater 2-k from the outdoor temperature and outdoor humidity information included in the weather forecast information for the time period being estimated. Supply capacity prediction means 33 then corrects the calculated hot water supply capacity based on the weather information included in the weather forecast information. For example, if the outdoor temperature for the time period being estimated is Tout1 and the outdoor humidity RH is 70%, the coefficient of performance is COPd1. If the weather is rainy, supply capacity prediction means 33 corrects COPd1 to a value smaller by ΔCOP to calculate COPd2. Supply capacity prediction means 33 then calculates the hot water supply capacity of water heater 2-k for the time period being estimated based on COPd2. On the other hand, if the weather is sunny, supply capacity prediction means 33 calculates the hot water supply capacity of water heater 2-k for the time period being estimated based on COPd1.

[0045] Demand prediction means 34 predicts the amount of hot water usage for each hour of target day Dx for water heating apparatus 2-k from historical trend data of hot water usage. Specific operations of demand prediction means 34 will be described with reference to Fig. 10. Fig. 10 is a table showing examples of combinations of areas where the water heating apparatus shown in Fig. 2 is installed and the amount of hot water usage of the water heating apparatus in the first embodiment.

[0046] In the table shown in FIG. 10, the horizontal divisions represent the type of region. For simplicity, the explanation will be given here for two types of regions, regions RG1 and RGm. The climate of region RG1 is assumed to be subarctic, and the climate of region RGm is assumed to be subtropical. In the table shown in FIG. 10, the vertical divisions represent different users of water heating apparatus 2-k. The upper division represents a case where water heating apparatus 2-k is installed in a typical home, and is labeled "individual." The lower division represents a case where water heating apparatus 2-k is installed in a facility such as a hospital or a nursing home, and is labeled "facility." Here, the facility will be explained as a nursing home where people requiring nursing care reside.

[0047] Each section shows a graph of the change in hot water usage Q by hour on the same day of the year, for example, from 6:00 AM to 8:00 PM on October 10th. The vertical axis of the graph is the hot water usage Q, and the horizontal axis of the graph is time t. In region RG1, the outdoor air temperature Tout at noon on October 10th is 11°C. In region RGm, the outdoor air temperature Tout at noon on October 10th is 25°C.

[0048] First, in the upper part of Figure 10, we compare the trend data of hot water usage Q of individuals in area RG1 and area RGm. Q1 is the reference value for comparing the hot water usage Q of the two areas. In area RG1, the temperature drops further in the evening. Therefore, users in area RG1 want to warm themselves by taking a bath, and their hot water usage Q increases from 5:00 PM to fill the bathtub with hot water. On the other hand, in area RGm, the outdoor air temperature Tout is 25°C at noon, so the drop in the outdoor air temperature Tout is suppressed even in the evening. Therefore, users in area RGm think that it is sufficient to just take a shower without taking a bath. As a result, the hot water usage Q of users in area RGm after 5:00 PM is lower than that of users in area RG1. Although the hot water usage Q per unit time of individuals in area RG1 sometimes exceeds the reference value Q1, the hot water usage Q per unit time of individuals in area RGm does not exceed the reference value Q1.

[0049] Next, in the lower part of Figure 10, the trend data of hot water usage Q of facilities in area RG1 is compared with the trend data of hot water usage Q of facilities in area RGm. Q2 is the reference value for comparing the hot water usage Q of the two areas. The facilities in area RG1 and area RGm are assumed to be of similar building size and have similar numbers of residents.

[0050] In both the facilities in area RG1 and area RGm, hot water usage Q tends to be high between 7:00 AM and 6:00 PM, except for one hour at lunchtime. This is because multiple residents in the facility take turns washing themselves. In particular, residents requiring care may have difficulty washing themselves, and facility staff must help. If facility staff try to wash multiple residents requiring care in turn starting in the afternoon, the task cannot be completed within the day. Therefore, multiple residents requiring care wash in turn starting in the morning, and hot water usage Q increases from the morning, as shown in Figure 10.

[0051] On the other hand, when comparing the total hot water usage Q of facilities in area RG1 and area RGm, the facilities in area RGm tend to have less. This is because, as with individuals, in area RGm, the outside temperature Tout is high, so people requiring care do not take baths and are less likely to get cold just by taking a shower. Therefore, the daily hot water usage Q of facilities in area RGm is less than the hot water usage Q of facilities in area RG1. While the hot water usage Q per unit time of facilities in area RG1 sometimes exceeds the standard value Q2, the hot water usage Q per unit time of facilities in area RGm does not exceed the standard value Q2.

[0052] As described above, the patterns of hot water usage differ, such as in an ordinary home where users can use the bath whenever they like, but in a facility where residents can only use the bath at designated times. Furthermore, even for the same type of user, the patterns of hot water usage vary depending on the region. Therefore, as mentioned above, the amount of hot water usage Q per hour tends to vary depending on the combination of region RGj and user. In response to this tendency, the demand forecasting means 34 estimates the amount of hot water usage per hour on the target day Dx for the hot water heater 2-k based on historical hot water usage trend data. This allows for accurate forecasting for each region and each user.

[0053] The reference values ​​Q1 and Q2 have a relationship of Q2>Q1. When the reference value Q2 is expressed as Q2=s1×Q1, the coefficient s1 is a value that increases in proportion to the number of residents in the facility.

[0054] The planning means 35 determines an operation schedule that optimizes the coefficient of performance of the hot water supply capacity Qd and prevents a hot water shortage based on the hourly hot water supply capacity calculated by the supply capacity prediction means 33 for the target day Dx and the hourly hot water consumption predicted by the demand prediction means 34. A hot water shortage occurs, for example, when a user is taking a shower in the evening with the water temperature set to 38°C, and the hot water in the tank 21 runs out, making it impossible to supply hot water. The operation schedule includes information on the hourly operating frequency fc of the compressor 12. The planning means 35 transmits the determined operation schedule to the hot water supply apparatus 2-k.

[0055] The specific operation of the planning means 35 to determine an operation schedule will be described with reference to Figs. 11 and 12. Fig. 11 is a graph showing time-series changes in the hot water supply capacity predicted by the supply capacity prediction means shown in Fig. 5 and the hot water consumption predicted by the demand prediction means. The vertical axis of Fig. 11 represents the hot water consumption, and the horizontal axis of Fig. 11 represents time. The bar graph shows changes in the hot water consumption predicted by the demand prediction means 34. Qd represents changes in the hot water supply capacity predicted by the supply capacity prediction means 33 based on the weather forecast information shown in Fig. 4.

[0056] As shown in Figure 11, between 5 PM and 7 PM, the amount of hot water usage predicted by the demand prediction means 34 is greater than the hot water supply capacity predicted by the supply capacity prediction means 33. Therefore, unless the operation of the hot water supply device 2-k is started before 5 PM, a hot water shortage will occur. If the operating frequency fc of the compressor 12 is increased too much to meet the demand, it may not be possible to obtain a hot water supply capacity Qd sufficient for the power consumption of the compressor 12, and the coefficient of performance COPd may decrease.

[0057] Fig. 12 is a graph for explaining how the planning means shown in Fig. 5 determines an operation schedule based on the graph shown in Fig. 11. The vertical axis in Fig. 12 represents the amount of hot water used, and the horizontal axis in Fig. 11 represents time. Qd represents the change in hot water supply capacity predicted by the supply capacity prediction means 33. The bar graph shows an operation schedule that maximizes the coefficient of performance COPd and prevents a hot water shortage.

[0058] Because heat pump water heater 2-k has a low hot water supply capacity per unit time, it is necessary to store hot water in tank 21 before demand exceeds supply while maintaining the coefficient of performance (COPd) at an optimum state. To prevent a hot water shortage from occurring between 4:00 PM and 8:00 PM, planning means 35 creates an operation schedule, as shown in Figure 12, in which operation of water heater 2-k starts at 4:00 PM and hot water supply does not exceed hot water supply capacity (Qd). This prevents a decrease in the coefficient of performance (COPd) and prevents a hot water shortage from occurring.

[0059] When the update means 36 receives the operation schedule from the planning means 35, it stores the received operation schedule in the storage device 4. Then, the update means 36 compares the hot water usage transition date received from the hot water heating apparatus 2-k on the day following the target day Dx with the operation schedule stored in the storage device 4, and reflects the difference between these values ​​in the past hot water usage transition data referenced by the demand forecasting means 34. A specific example will be described below.

[0060] For example, in the example described with reference to FIG. 10, a facility typically has a large number of staff members Monday through Friday and a small number of staff members on Saturday and Sunday. Therefore, the number of care-requiring individuals taking baths on Saturday and Sunday is significantly lower than on Monday through Friday. As a result, hot water usage on Saturday and Sunday is significantly lower than that on Monday through Friday. In other words, if the demand forecasting means 34 forecasts hot water usage on Saturday or Sunday using historical hot water usage data from Monday through Friday, the resulting operation schedule will differ significantly from the actual hot water usage.

[0061] Therefore, the update means 36 can determine the cycle from Sunday to Saturday by performing machine learning with reference to the time measured by the timer 30 and the daily hot water usage of the facility stored in the storage device 4. The update means 36 then presents appropriate trend data for the past hot water usage to be used by the demand forecasting means 34 for prediction, corresponding to the day of the week of the target day Dx. For example, if the storage device 4 stores trend data for hot water usage for at least the past year, the update means 36 provides the demand forecasting means 34 with daily trend data for hot water usage on the same day of the week as the target day Dx, within one week of the same day one year prior to the target day Dx. By reflecting differences in hot water usage depending on the day of the week in the demand forecast, the prediction accuracy of hot water usage is improved.

[0062] The update means 36 may also apply a probability density function such as an exponential distribution or a normal distribution to the time series change in the amount of hot water usage Q in a time period including the time before and after the time when the amount of hot water usage Q reaches its maximum. For example, the time series change in the amount of hot water usage Q from the time when the amount of hot water usage Q reaches its maximum may be calculated as f(t)=λe -λt (λ>0). In this case, the update means 36 uses machine learning to change the value of λ so that it fits the trend data of hot water usage accumulated in the storage device 4. The update means 36 presents a probability density function in which λ is changed to an appropriate value, instead of the trend data of hot water usage used by the demand forecasting means 34 for prediction, for the time period when users use hot water the most. In this case as well, the prediction accuracy of hot water usage is improved.

[0063] Here, an example of hardware of the controller 5 shown in Fig. 5 will be described. Fig. 13 is a hardware configuration diagram showing an example of the configuration of the controller shown in Fig. 5. When the various functions of the controller 5 are executed by hardware, the controller 5 shown in Fig. 5 is configured with a processing circuit 90 as shown in Fig. 13. The functions of the timer 30, acquisition means 31, management means 32, supply capacity prediction means 33, demand prediction means 34, planning means 35, and update means 36 shown in Fig. 5 are realized by the processing circuit 90.

[0064] When each function is performed by hardware, the processing circuit 90 corresponds to, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), or a combination thereof. The functions of each of the means: timer 30, acquisition means 31, management means 32, supply capacity prediction means 33, demand prediction means 34, planning means 35, and update means 36 may be realized individually by the processing circuit 90. Furthermore, the functions of each of the means: timer 30, acquisition means 31, management means 32, supply capacity prediction means 33, demand prediction means 34, planning means 35, and update means 36 may be realized by a single processing circuit 90.

[0065] Another example of hardware for the controller 5 shown in Fig. 5 will now be described. Fig. 14 is a hardware configuration diagram showing another example of the configuration of the controller shown in Fig. 5. When the various functions of the controller 5 are executed by software, the controller 5 shown in Fig. 5 is configured with a processor 91 such as a CPU and a memory 92, as shown in Fig. 14. The functions of the timer 30, the acquisition means 31, the management means 32, the supply capacity prediction means 33, the demand prediction means 34, the planning means 35, and the update means 36 are realized by the processor 91 and the memory 92. Fig. 14 shows that the processor 91 and the memory 92 are communicatively connected to each other via a bus 93.

[0066] When each function is performed by software, the functions of the timer 30, acquisition means 31, management means 32, supply capacity prediction means 33, demand prediction means 34, planning means 35, and update means 36 are realized by software, firmware, or a combination of software and firmware. The software and firmware are written as programs and stored in memory 92. The processor 91 realizes the function of each means by reading and executing the programs stored in memory 92.

[0067] When the functions of the timer 30, the acquisition means 31, the management means 32, the supply capacity prediction means 33, the demand prediction means 34, the planning means 35, and the update means 36 are performed by software, these means do not have to be provided in a single hardware configuration. For example, the timer 30, the acquisition means 31, the management means 32, the planning means 35, and the update means 36 may be provided in the information processing device 3, and the supply capacity prediction means 33 and the demand prediction means 34 may be provided in a server (not shown), which is another information processing device connected to the network 100.

[0068] The memory 92 may be a non-volatile semiconductor memory such as a read only memory (ROM), a flash memory, an erasable programmable read only memory (EPROM), or an electrically erasable programmable read only memory (EEPROM). Alternatively, a volatile semiconductor memory such as a random access memory (RAM) may be used as the memory 92. Furthermore, the memory 92 may be a removable recording medium such as a magnetic disk, a flexible disk, an optical disk, a compact disc (CD), a mini disc (MD), or a digital versatile disc (DVD).

[0069] Next, a description will be given of the operation of the heat storage system 1 of the present embodiment 1. Fig. 15 is a sequence diagram showing the operation procedure of the heat storage system according to the embodiment 1. Here, the case of the hot water supply device 2-k will be described.

[0070] Water heating apparatus 2-k transmits trend data of hot water usage to information processing device 3 (step S101). When information processing device 3 receives the trend data of hot water usage from water heating apparatus 2-k, acquisition means 31 of controller 5 stores the trend data of hot water usage in storage device 4 (step S102). Information providing server 6 transmits weather forecast information to information processing device 3 (step S103). When information processing device 3 receives weather forecast information from information providing server 6, acquisition means 31 of controller 5 stores the weather forecast information in storage device 4 (step S104). In step S103, information providing server 6 only needs to transmit weather forecast information for at least region RGj to which water heating apparatus 2-k belongs to information processing device 3.

[0071] The supply capacity prediction means 33 calculates the hourly hot water supply capacity of the water heater 2-k on the target day Dx based on the weather forecast information (step S105). The demand prediction means 34 predicts the hourly hot water consumption of the water heater 2-k on the target day Dx based on the historical hot water consumption trend data (step S106). The planning means 35 receives information on the predicted hourly hot water supply capacity for the target day Dx from the supply capacity prediction means 33, and receives information on the predicted hourly hot water consumption for the target day Dx from the demand prediction means 34. Then, the planning means 35 determines an operation schedule that optimizes the coefficient of performance of the hot water supply capacity Qd and prevents a hot water shortage based on the hourly hot water supply capacity and hourly hot water consumption for the target day Dx (step S107).

[0072] Planning means 35 transmits the determined operation schedule to water heating apparatus 2-k (step S108). Upon receiving the operation schedule from information processing device 3, water heating apparatus 2-k starts operation in accordance with the operation schedule (step S109).

[0073] Up to this point, the operation of the heat storage system 1 has been described in detail with reference to the sequence diagram of Fig. 15, but an overview of the operation schedule determination method of the first embodiment will now be described with reference to Fig. 16. Fig. 16 is a diagram for explaining the flow of the operation schedule determination process for the hot water supply device performed by the information processing device according to the first embodiment.

[0074] Acquisition means 31 acquires weather forecast information for the region where the water heating apparatus is installed. Supply capacity prediction means 33 calculates the hot water supply capacity based on the weather forecast information corresponding to the region, thereby customizing the hot water supply capacity for each region. Meanwhile, memory 41 of the water heating apparatus or the storage means of storage device 4 accumulates data on the past hot water usage of the water heating apparatus. Demand prediction means 34 predicts hot water demand for each water heating apparatus based on the past hot water usage data, thereby customizing the demand prediction based on hot water usage. The accuracy of the demand prediction may be improved by machine learning.

[0075] Then, the planning means 35 determines the operation schedule of the water heater based on the predicted hot water supply capacity and the predicted hot water demand, thereby customizing the operation schedule for each water heater. In this way, optimal operation can be scheduled for each water heater.

[0076] Furthermore, in the present embodiment 1, the acquisition means 31 may acquire weather forecast information from the information providing server 6 multiple times on the target day Dx. In this case, the supply capacity prediction means 33 determines the hot water supply capacity based on weather forecast information with increasing prediction accuracy over time, thereby improving the prediction accuracy of the hot water supply capacity and the accuracy of the operation schedule.

[0077] The thermal storage system 1 of the first embodiment includes a water heater 2-k that performs hot water supply operation according to an operation schedule, and an information processing device 3. The information processing device 3 includes a storage device 4, an acquisition means 31, a supply capacity prediction means 33, a demand prediction means 34, and a planning means 35. The storage device 4 functions as a storage means for storing historical hot water usage trend data of the water heater 2-k. The acquisition means 31 acquires weather forecast information for the region in which the water heater 2-k is installed for a target day Dx for which the operation schedule is set. The supply capacity prediction means 33 calculates the hourly hot water supply capacity of the water heater 2-k for the target day Dx based on the weather forecast information. The demand prediction means 34 predicts the hourly hot water usage for the target day Dx based on the historical hot water usage trend data. The planning means 35 determines an operation schedule that will prevent a hot water shortage based on the hourly hot water supply capacity calculated by the supply capacity prediction means 33 and the hourly hot water usage predicted by the demand prediction means 34 for the target day Dx.

[0078] According to the first embodiment, the hot water supply capacity for each hour is predicted based on weather forecast information for the area where the water heater is installed, the hot water demand is predicted based on historical hot water usage trend data, and an operation schedule that prevents hot water shortages is created based on these predicted values. The hot water supply capacity is predicted with high accuracy based on weather forecast information for the area where the water heater is installed, and the hot water demand is predicted with high accuracy based on how the user uses hot water, and an optimal operation schedule is determined based on these predicted values. As a result, hot water shortages can be prevented.

[0079] (Variation 1) In this modified example 1, a weather information database is constructed in a storage device of an information processing device. Fig. 17 is a block diagram showing another configuration example of the heat storage system according to embodiment 1. Fig. 18 is a block diagram showing one configuration example of the information processing device shown in Fig. 17.

[0080] As shown in Fig. 17, in the thermal storage system 1 of the present modified example 1, the information processing device 3a does not acquire weather forecast information from the information providing server 6 shown in Fig. 1. In the present modified example 1, as shown in Fig. 18, a weather information database is constructed in the storage device 4 of the information processing device 3a.

[0081] The weather information database stores weather information for the regions RG1 to RGm for at least the past year. The weather information includes at least the outdoor air temperature Tout, the outdoor air humidity RH, and weather information. The weather information may be the probability of precipitation. The acquisition means 31 refers to the weather information database at a predetermined time (for example, 4:00 AM) on the target day Dx, and reads out the weather information for the regions RG1 to RGm for the same day one year prior to the target day Dx. The acquisition means 31 then sets the read-out weather information for the regions RG1 to RGm as weather forecast information WRsat(t) to WRstp(t) that the supply capacity prediction means 33 uses to predict the hot water supply capacity.

[0082] When the acquisition means 31 reads out weather information from the weather information database as weather forecast information, the weather information read out is not limited to the weather information for the same day one year prior to the target day Dx. The acquisition means 31 may also read out weather information for a predetermined number of consecutive days, including the same day one year prior to the target day Dx, from the weather information database. The consecutive days may be, for example, three to seven days. The day that is the same as the target day Dx may be the first day of the multiple days, the last day of the multiple days, or any day of the multiple days excluding the first and last. In this case, the acquisition means 31 calculates the average values ​​of the outside air temperature Tout, the outside air humidity RH, and the probability of precipitation for each of the same time on the consecutive days, and sets these calculated values ​​as the weather forecast information for the target day Dx.

[0083] A specific example of the outdoor air temperature Tout in the weather information will be described. Consider a case where there are three consecutive days, including the day before and the day after the same day one year ago on the target day Dx, and the outdoor air temperatures Tout at 9:00 AM on these three days are 15°C, 17°C, and 16°C. The average value of the three outdoor air temperatures Tout is 16°C. In this case, the acquisition means 31 calculates the average value of the three outdoor air temperatures Tout and sets the calculated value as the outdoor air temperature Tout in the weather forecast information at 9:00 AM on the target day Dx.

[0084] Here, the multiple days are described as multiple consecutive days based on the same day one year ago relative to the target day Dx, but if weather information for the past several years has been accumulated in the weather information database, the multiple days may be the same days for the past several years. Also, the weather forecast information has been described as calculating the average values ​​of the outside air temperature Tout, outside air humidity RH, and precipitation probability for each hour of the day, but they may be calculated using other statistical methods other than the average values.

[0085] In most regions on Earth, weather conditions tend to change on a yearly cycle. Therefore, even if acquisition means 31 does not acquire weather forecast information for each region RG1 to RGm every day from information providing server 6 shown in Fig. 1, supply capacity prediction means 33 can predict the hot water supply capacity by referring to accumulated weather information from one year ago.

[0086] Embodiment 2 In the second embodiment, the demand forecast for hot water usage is corrected using weather forecast information. In the second embodiment, the same components as those in the first embodiment are given the same reference numerals, and detailed explanations thereof will be omitted. In the second embodiment, operations that differ from those in the first embodiment will be explained in detail, and detailed explanations of operations that are similar to those in the first embodiment will be omitted.

[0087] The configuration of the information processing device 3 in the thermal storage system 1 of the second embodiment will be described with reference to Fig. 5. In the second embodiment, the demand prediction means 34 corrects the transition data of past hot water usage using weather forecast information for the target day Dx, and predicts the hot water usage for each hour on the target day Dx. For example, the demand prediction means 34 corrects the predicted hot water usage by decreasing or increasing it by a predetermined usage amount ΔQ in accordance with the weather forecast information.

[0088] The correction performed by the demand forecasting means 34 is derived by an analytical method such as regression analysis based on the relationship between the historical hot water usage trend data and any of the following information: outdoor temperature, outdoor humidity, and weather information. The regression analysis is not limited to simple regression analysis. For example, multiple regression analysis of the historical hot water usage trend data with outdoor temperature, outdoor humidity, and weather information is also possible. The regression analysis differs depending on the type of facility. Examples of facility types include hospitals, nursing homes, and food service centers.

[0089] For example, a specific example of correction will be described for a case where the hot water heater is installed in a nursing home. If it rains on the day that a resident of the facility is scheduled to exercise outside, the resident will be unable to exercise outside. In this case, the resident will likely sweat less than on a sunny day, resulting in a decrease in hot water usage. In this case, the demand forecasting means 34 reduces the hourly hot water usage predicted based on past hot water usage trend data by a predetermined usage amount ΔQ.

[0090] The effects of the second embodiment will be explained. Demand predictions based on historical data on hot water usage trends can sometimes deviate from actual hot water usage. This is thought to be because hot water demand predictions do not take into account parameters that affect people's behavior. One important parameter is weather conditions. Outdoor temperature, outdoor humidity, and weather information are thought to be parameters that have a significant impact on demand predictions. For example, when the weather is clear, the weather induces an increase in the amount of sweat and skin temperature when a user goes out. In this case, it is thought to affect the frequency of hot water usage, such as showering.

[0091] According to the second embodiment, the demand forecast value is corrected based on weather conditions such as outdoor temperature that affect people's behavior. Therefore, it is predicted that the demand for hot water will increase during times when the outdoor temperature rises and people start to sweat, and during times when the outdoor humidity rises and people feel uncomfortable. As a result, it becomes possible to more accurately predict the timing of high hot water usage, such as when a bathtub needs to be filled with hot water.

[0092] Embodiment 3 In the third embodiment, the operation schedule is corrected using information acquired from the hot water supply apparatus during operation. In the third embodiment, the same components as those in the first embodiment are given the same reference numerals, and detailed explanations thereof will be omitted. Furthermore, in the third embodiment, operations that differ from those in the first and second embodiments will be explained in detail, and detailed explanations of operations that are similar to those in the first and second embodiments will be omitted.

[0093] The configuration of the information processing device 3 in the heat storage system 1 of the third embodiment will be described with reference to Fig. 5. The acquisition means 31 acquires information on the intake temperature Tin from the temperature sensor 14 of the hot water supply device 2-k in operation. The supply capacity prediction means 33 calculates the temperature difference ΔT between the outside air temperature Tout and the intake temperature Tin, which are included in the weather forecast information. The supply capacity prediction means 33 corrects the value of the hot water supply capacity calculated based on the weather forecast information, using the temperature difference ΔT.

[0094] Consider a specific example of this correction where heat source unit 10 is installed in the shade. While the weather forecast information indicates that the outdoor air temperature Tout at 9:00 AM is 15°C, the actual outdoor air temperature at the location where heat source unit 10 is installed may be lower than 15°C, e.g., 10°C. In this case, the temperature difference ΔT between the outdoor air temperature Tout and the intake temperature Tin is 5°C. Therefore, in the third embodiment, supply capacity prediction means 33 corrects the hot water supply capacity value calculated based on the weather forecast information to a lower value by the temperature difference ΔT, using the temperature difference ΔT = 5°C. Note that the temperature difference ΔT is significantly affected by the installation environment of water heater 2-k. Therefore, the timing of calculation of temperature difference ΔT by supply capacity prediction means 33 is not limited as long as water heater 2-k is in operation.

[0095] The effects of the third embodiment will now be described. In the first and second embodiments, the hot water supply capacity is predicted using weather forecast information acquired from the external information providing server 6. However, the heat source unit 10 is actually installed in a variety of environments, and if the outside air temperature Tout from the weather forecast information is used as is to predict the hot water supply capacity, the prediction accuracy may decrease. Examples of installation environments that may affect the prediction accuracy of the hot water supply capacity include semi-underground, near an exhaust vent, where strong winds pass, or near the sea.

[0096] An exhaust port is an exhaust port (e.g., a kitchen exhaust port) intended for exhausting heat. Near the exhaust port, the exhaust heat is blown onto the heat source unit 10, so the intake temperature Tin is always higher than the outside air temperature Tout. Near the sea means being close to the sea. There are several possible reasons why being near the sea reduces the accuracy of hot water supply capacity predictions. For example, even if the rotation speed of the fan 17 is the same, the influence of sea breezes changes the wind speed passing through the heat source side heat exchanger 16. In addition, sea breezes change the humidity of the air passing through the heat source side heat exchanger 16. Furthermore, salt damage increases the tendency for metal to corrode, which corrodes the fins of the heat source side heat exchanger 16 and reduces hot water supply capacity.

[0097] In contrast, according to the third embodiment, the value of the hot water supply capacity calculated based on the weather forecast information is corrected by the temperature difference ΔT between the outside air temperature Tout and the intake temperature Tin, which is included in the weather forecast information. Therefore, even if the heat source unit 10 is installed in an environment that reduces the accuracy of the hot water supply capacity prediction, the accuracy of the hot water supply capacity prediction can be improved.

[0098] Embodiment 4 The fourth embodiment is a case where an operation schedule cannot be determined automatically. In the fourth embodiment, the same components as those in the first embodiment are denoted by the same reference numerals, and detailed descriptions thereof will be omitted. In the fourth embodiment, operations different from those in the first to third embodiments will be described in detail, and detailed descriptions of operations similar to those in the first to third embodiments will be omitted.

[0099] The configuration of the information processing device 3 in the heat storage system 1 of the fourth embodiment will be described with reference to Fig. 5. The acquisition means 31 acquires information on the intake temperature Tin from the temperature sensor 14 of the hot water supply device 2-k in operation. The supply capacity prediction means 33 determines whether or not there is a correlation between the outside air temperature Tout and the intake temperature Tin included in the weather forecast information, and does not calculate the hot water supply capacity if it determines that there is no correlation between the outside air temperature Tout and the intake temperature Tin.

[0100] Determining whether there is a correlation means, for example, comparing the temporal changes between the outside air temperature Tout and the intake temperature Tin, and determining that there is no correlation if there is no correlation. The presence or absence of a correlation is determined by whether the absolute value of the correlation coefficient is equal to or greater than a predetermined threshold. For example, if the heat source unit 10 is installed indoors, it is determined that there is no correlation between the outside air temperature Tout and the intake temperature Tin.

[0101] When supply capacity prediction means 33 determines that there is no correlation between outside air temperature Tout and intake temperature Tin, planning means 35 determines an operation schedule based on the hourly hot water consumption predicted for target day Dx by demand prediction means 34. Planning means 35 then issues information to operation terminal 20 of water heating apparatus 2-k that the operation schedule has been determined without taking hot water supply capacity into consideration.

[0102] In the fourth embodiment, for example, when a user operates the operation terminal 20 to modify the operation schedule, the acquisition means 31 may store the modification in the storage device 4. The update means 36 reflects the modification in the accumulated hot water usage transition data, so that the modification is reflected in the demand forecast the next time an operation schedule is planned. Alternatively, the planning means 35 may directly reflect the modification stored in the storage device 4 in the operation schedule to be planned next.

[0103] The effects of the fourth embodiment will be described. In the first and second embodiments, the hot water supply capacity is predicted using weather forecast information acquired from the external information providing server 6. However, heat source unit 10 is actually installed in a variety of environments, and if the outdoor air temperature Tout from the weather forecast information is used directly to predict the hot water supply capacity, the prediction accuracy may decrease. Examples of installation environments that may affect the prediction accuracy of the hot water supply capacity include a semi-underground location, near an exhaust vent, a location where strong winds pass, or near the sea. In such cases, if water heating apparatus 2-k continues to operate according to an automatically planned operation schedule, the user will not be able to set an operation schedule to achieve energy conservation for water heating apparatus 2-k.

[0104] In contrast, according to the fourth embodiment, when heat source unit 10 is installed in a location where wind short-circulates, such as a semi-underground location or near an exhaust port, supply capacity prediction means 33 determines that there is no correlation between outside air temperature Tout and intake temperature Tin. Then, information that the operation schedule has been determined without consideration of hot water supply capacity is notified to the user via operation terminal 20. Therefore, the user can operate operation terminal 20 to cancel the automatically planned operation schedule and set an operation schedule themselves. As a result, water heating apparatus 2-k can be made to perform energy-saving operation. [Explanation of symbols]

[0105] 1 heat storage system, 2-1 to 2-n hot water supply device, 3, 3a information processing device, 4 storage device, 5 controller, 6 information providing server, 7 storage unit, 8 information providing controller, 10 heat source unit, 11 refrigerant circuit, 12 compressor, 13 water heat exchanger, 14 temperature sensor, 15 expansion valve, 16 heat source side heat exchanger, 17 fan, 18 refrigerant piping, 20 operation terminal, 21 tank, 22 circulation pump, 23 signal line, 24 circulation circuit, 25 inlet, 26a, 26b piping, 27 outlet, 28 inlet temperature sensor, 29 outlet temperature sensor, 30 timer, 31 acquisition means, 32 management means, 33 supply capacity prediction means, 34 demand prediction means, 35 planning means, 36 update means, 40 hot water supply controller, 41 memory, 42 processor, 50 timer, 51 Management means, 52 Meteorological information providing means, 53 Information providing means, 90 Processing circuit, 91 Processor, 92 Memory, 93 Bus, 100 Network.

Claims

1. a water heater that performs hot water supply operation according to an operation schedule; a storage means for storing transition data of past hot water usage of the hot water supply device; an acquisition means for acquiring weather forecast information for a region in which the water heating apparatus is installed for a target date for which the operation schedule is set; a supply capacity prediction means for calculating the hot water supply capacity of the hot water supply device for each hour of the target day based on the weather forecast information; a demand prediction means for predicting the hot water usage for each hour of the target day based on the transition data of the past hot water usage; and a planning means for determining the operation schedule that will prevent a shortage of hot water supply based on the hot water supply capacity for each hour calculated by the supply capacity prediction means for the target day and the hot water consumption amount for each hour predicted by the demand prediction means. Heat storage system.

2. The demand forecasting means correcting the transition data of the hot water usage amount using the weather forecast information for the target day, and predicting the hot water usage amount for each hour of the target day; The thermal storage system according to claim 1 .

3. the storage means stores a capacity table in which the hot water supply capacity is determined by an outside air temperature and an outside air humidity; the acquiring means acquires the weather forecast information including the outside air temperature, the outside air humidity, and weather information; The supply capacity prediction means The capacity table stored in the storage means is referenced, the hot water supply capacity is calculated from the information on the outside air temperature and the outside air humidity included in the weather forecast information acquired by the acquisition means, and the calculated hot water supply capacity is corrected using the weather information included in the weather forecast information. The heat storage system according to claim 1 or 2.

4. The water heater includes a heat source-side heat exchanger, a fan that supplies air to the heat source-side heat exchanger, and a temperature sensor that detects an intake temperature, which is the temperature of the air drawn in by the fan; the acquiring means acquires information about the intake temperature from the temperature sensor of the hot water supply device during operation, The supply capacity prediction means calculating a temperature difference between the outside air temperature included in the weather forecast information acquired by the acquisition means and the intake temperature, and correcting the value of the hot water supply capacity calculated based on the weather forecast information using the temperature difference; The heat storage system according to any one of claims 1 to 3.

5. The water heater includes a heat source-side heat exchanger, a fan that supplies air to the heat source-side heat exchanger, and a temperature sensor that detects an intake temperature, which is the temperature of the air drawn in by the fan; the acquiring means acquires information about the intake temperature from the temperature sensor of the hot water supply device during operation, The supply capacity prediction means determining whether there is a correlation between the outdoor air temperature included in the weather forecast information acquired by the acquisition means and the intake temperature, and if it is determined that there is no correlation between the outdoor air temperature and the intake temperature, not calculating the hot water supply capacity; The planning means If the supply capacity prediction means determines that there is no correlation between the outside air temperature and the intake temperature, the operation schedule is determined based on the hot water usage per hour predicted by the demand prediction means for the target day, and information that the operation schedule has been determined without taking the hot water supply capacity into consideration is sent to an operation terminal of the hot water supply device. The heat storage system according to any one of claims 1 to 3.

6. the storage means stores weather information for at least the past year in the area where the hot water heater is installed; The acquisition means acquires, from the storage means, the weather information for the same day one year prior to the target day or the weather information for a plurality of predetermined past days based on the same day as the target day, as the weather forecast information. The heat storage system according to any one of claims 1 to 5.

7. the acquiring means acquires the weather forecast information from a server connected via a network. The heat storage system according to any one of claims 1 to 5.

8. An information processing device that determines an operation schedule of a water heater, a storage means for storing transition data of past hot water usage of the hot water supply device; an acquisition means for acquiring weather forecast information for a region in which the water heating apparatus is installed for a target date for which the operation schedule is set; a supply capacity prediction means for calculating the hot water supply capacity of the hot water supply device for each hour of the target day based on the weather forecast information; a demand prediction means for predicting the hot water usage for each hour of the target day based on the transition data of the past hot water usage; a planning means for determining the operation schedule that will prevent a hot water shortage from occurring based on the hot water supply capacity for each hour calculated by the supply capacity prediction means for the target day and the hot water consumption amount for each hour predicted by the demand prediction means; and An information processing device having the above.

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