Home electric appliance information generation system

Abstract

To provide a home electric appliance control information generation system for generating control information that can more optimally operate a home electric appliance in accordance with an installed environment.SOLUTION: A home electric appliance control information 1 for generating control information of a home electric appliance on the basis of operation history information from a plurality of home electric appliances is provided with a server 31 having a storage part for storing acquired data to process data. The operation history information includes position information that can specify a region at which each of a plurality of home electric appliances is installed, setting information, and power consumption, and an information processing part acquires location region weather information with the plurality of home electric appliances are installed on the basis of the operation history information and the position information from the plurality of home electric appliances through a network, classifies the operation history information about the plurality of home electric appliances belonging to the location region to be location-based operation history information, classifies the location-based operation history information into a plurality of groups on the basis of the amount of power consumption, analyzes the location-based operation history information belonging to the same group, and outputs control information of home electric appliances.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a home appliance control information generation system, and more particularly to a system for classifying and analyzing information from home appliances received via a network for each region and then generating information for controlling home appliances.

Background Art

[0002] In recent years, due to the influence of changes in the external environment such as global warming, the need to improve the comfort of the living environment has been increasing. For example, among home appliances, the importance of an air conditioner in keeping the thermal sensation of the residents comfortable has been increasing. In order to achieve comfort, a comfort index called PMV (Predicted Mean Vote) has been proposed. An air conditioning control system has been disclosed that monitors the PMV of users in the air-conditioned space and controls the air conditioner.

[0003] Conventional air conditioners are known to control using, for example, PMV (Predicted Mean Vote) as an index. PMV is calculated from a total of six elements, including four environmental elements on the room temperature, radiant temperature, relative humidity, and wind speed, and two human body elements on the clothing quantity and activity level. PMV is a parameter representing the degree of comfort related to a person's thermal sensation. PMV can take a range from -3 to +3. PMV is determined by seven levels of comfort: -3 (quite cold), -2 (cold), -1 (slightly cold), 0 (neutral), +1 (slightly hot), +2 (hot), and +3 (quite hot). Generally, when a person feels comfortable, the PMV is between -0.5 and +0.5.

[0004] When PMV is used as an index in the control of an air conditioner, the air conditioner is controlled to operate so as to approach the value of PMV when a person feels comfortable, based on the temperature, humidity, air flow (wind speed), and radiant temperature (wall temperature) of the indoor space measured as environmental information of the indoor space, and the behavior and clothing quantity of the target person in the room grasped from a camera or the like (see, for example, Patent Document 1).

Prior Art Documents

Patent Document

[0005]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0006] The current living environment may have significantly different heat insulation performances, such as in tower mansions, single-family houses, or apartments, and the temperature and humidity may also vary greatly depending on the region. Thus, in an air conditioner that may be installed in various environments, if the control is based on one index, there is a problem that the control may not be suitable for the installed environment.

[0007] As in the control of the air conditioner disclosed in Patent Document 1, for example, when using PMV, even if the PMV is within the same numerical range, the thermal environments may be different. That is, in terms of the PMV value, for example, there may be a case where the environment with a room temperature of 20°C and no wind and the environment with a room temperature of 24°C and air blowing have the same value. Therefore, even if the air conditioner is controlled based on PMV, it is not possible to clearly determine whether the room temperature should be set lower or the wind speed should be increased for the user in the installed environment to feel comfortable. That is, the optimal operating state of home appliances varies depending on the installed environment and the user, and there is a problem that a large amount of data is required to obtain the optimal operating state.

[0008] An object of the present invention is to provide a home appliance control information generation system that generates control information capable of realizing the operation of a home appliance suitable for the installed environment.

Means for Solving the Problems

[0009] The home appliance control information generation system according to the present invention is a home appliance control information generation system that generates control information for home appliances based on operation history information from a plurality of home appliances. The system includes a storage unit that stores the acquired data, and an information processing unit that processes the data. The operation history information includes position information that can identify the installed area of each of the plurality of home appliances, setting information, and power consumption. The information processing unit acquires location area weather information, which is weather information of the location area where the plurality of home appliances are installed, based on the operation history information and the position information from the plurality of home appliances via a network. The operation history information for the plurality of home appliances belonging to the location area is classified into area-specific operation history information, and the area-specific operation history information is classified into a plurality of groups based on the magnitude of the power consumption. The area-specific operation history information belonging to the same group among the plurality of groups is analyzed, and control information for the home appliances is output.

Effects of the Invention

[0010] According to the above means, the home appliance control information generation system can collect operation history information from a plurality of home appliances, classify it by region, and then extract operation history information suitable for the installed environment in a specific region by analyzing the data.

Brief Description of the Drawings

[0011]

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Mode for Carrying Out the Invention

[0012] The preferred embodiments of the home appliance control information generation system of the present invention are described in detail below with reference to the drawings. Note that the embodiments described below are preferred specific examples of the present invention, and thus are technically subject to various preferable limitations. However, the scope of the present invention is not limited to these aspects unless otherwise specified in the following description to particularly limit the present invention.

[0013] Embodiment 1. <Configuration of the entire system> FIG. 1 is an explanatory diagram showing an example of the hardware configuration included in the home appliance control information generation system 1 according to Embodiment 1. As shown in FIG. 1, the home appliance control information generation system 1 according to Embodiment 1 includes probe devices 11 and 12, a mobile terminal 21, and a server 31 (information processing unit). The probe device 11 is built into an air conditioner 41 (electrical product) installed in the house 1a. In the house 1a, a remote controller 51 for the air conditioner 41 and a home router 52 are arranged. In Embodiment 1, an air conditioner such as the air conditioner 41 installed in the house is taken as an example for explanation. However, the objects for which data is collected by the probe devices 11 and 12 and the mobile terminal 21 are all home appliances whose temperature affects the operation, such as a refrigerator.

[0014] The operation of the probe device 12 will be described later. Note that the home appliance control information generation system 1 may include a number of probe devices different from the probe devices 11 and 12 shown in FIG. 1 and having the same functions as the probe devices 11 and 12. In addition, the home appliance control information generation system 1 may include only one of the probe devices 11 and 12. The network 30 shown in FIG. 1 is a so-called Internet in which various public networks are interconnected. The server 31 and the home router 52 are connected to the network 30.

[0015] The air conditioner 41 is a household room air conditioner (air conditioning device) equipped with a heat pump unit (not shown). The outdoor unit that constitutes a part of the heat pump unit of the air conditioner 41 is not shown in the figure. Electric power is supplied to the air conditioner 41 via a power line Cp (outlet cable) connected to an outlet (not shown) installed in the house 1a.

[0016] The user can operate the air conditioner 41 using the remote control 51. Specifically, the remote control 51 includes, for example, an infrared light emitting unit (not shown), and the air conditioner 41 includes an infrared light receiving unit 43. The remote control 51 transmits an infrared signal from the infrared light emitting unit in response to an operation by the user. The air conditioner 41 operates in response to the infrared signal received by the infrared light receiving unit 43. For example, the user can set (input) the set temperature Ts [°C] of the air conditioner 41 by operating the remote control 51.

[0017] Figure 2 is a hardware configuration diagram of the probe device 11 according to Embodiment 1. The probe device 11 can use a well-known small computer (microcomputer) and includes, as shown in Figure 2, a CPU 61, a ROM 62, a RAM 63, a data communication unit 64, an input / output unit 65, a wattmeter 66, and a room thermometer 67. The CPU 61 reads data, performs numerical calculations, outputs calculation results, etc. by sequentially executing a predetermined program (routine). The ROM 62 stores programs executed by the CPU 61 and maps (lookup tables) referred to by the CPU 61, etc. Part or all of the ROM 62 may be constituted by a flash memory capable of changing the stored data. The RAM 63 temporarily stores data referred to by the CPU 61.

[0018] The data communication unit 64 provides a terminal (slave unit) function compliant with the WiFi (registered trademark) or Bluetooth (registered trademark) standard. The data communication unit 64 controls data communication (data reception and transmission) via the in-house router 52 and the network 30 with various nodes including the server 31. The input / output unit 65 performs data input / output (I / O) with a control device (not shown) of the air conditioner 41.

[0019] The power meter 66 is installed in the power line Cp and detects the power consumption Wa [W] consumed by the air conditioner 41. The room thermometer 67 detects the indoor temperature Tr [°C] which is the temperature of the air flowing into the air conditioner 41 (that is, the temperature of the room where the air conditioner 41 is installed). The light emitting device 68 is a light emitting diode element and is disposed at a position visible to the user on the front surface of the housing of the air conditioner 41 (see FIG. 1). The CPU 61 can control the lighting state of the light emitting device 68 (see FIG. 1) disposed in the air conditioner 41. Further, the CPU 61 acquires the operating state of the air conditioner 41 including the set temperature Ts via the input / output unit 65.

[0020] The in-house router 52 provides a base station (parent unit) function compliant with the WiFi standard. For example, the probe device 11 and the mobile terminal 21 can communicate with the server 31 (in this embodiment, data communication based on the TCP / IPv4 standard) using the base station function of the in-house router 52 and via the network 30. The data communication unit 64 communicates with the server 31 via the in-house router 52.

[0021] FIG. 3 is a hardware configuration diagram of the server 31 according to Embodiment 1. The probe device 11 is a well-known small computer (microcomputer) and includes a CPU 71, a ROM 72, a RAM 73, a data communication unit 74, an input / output unit 65, and a storage device 75 as shown in FIG. 3. The CPU 71 reads data, performs numerical calculations, outputs calculation results, etc. by sequentially executing a predetermined program (routine). The ROM 72 stores programs executed by the CPU 71 and maps (lookup tables) referred to by the CPU 71, etc. Part or all of the ROM 72 may be configured by a flash memory capable of changing the stored data. The RAM 73 temporarily stores data referred to by the CPU 71.

[0022] The data communication unit 74 receives the probe device 11, meteorological observation data, etc. via the network 30. The data acquired by the data communication unit 74 is processed using the CPU 71 or the like as necessary and stored in the storage device 75.

[0023] The storage device 75 is a non-volatile semiconductor memory such as a flash memory, an EPROM (Erasable Programmable ROM), an EEPROM (Electrically Erasable Programmable ROM), or an HDD (Hard disk drive), and serves as a so-called secondary storage device. In Embodiment 1, the storage device 75 may be installed in the terminal or may be a server connected via the network 90. Note that the storage device 75 is not necessarily installed in the terminal, and includes those communicably installed on the network 30. In some cases, the network 30 may also be included and referred to as a storage device.

[0024] (Transmission of driving history information S) FIG. 4 is an explanatory diagram showing an example of information exchange between devices of the home appliance control information generation system 1 according to Embodiment 1. FIG. 5 is an explanatory diagram showing an example of a data set transmitted from the probe device 11 to the server 31 according to Embodiment 1. As shown in FIG. 4(a), the probe device 11 transmits the "driving history information S" to the server 31 via the in-house router 52 and the network 30 every time a predetermined transmission interval Ti elapses. In the present embodiment, the driving history information S is transmitted from the probe device 11 to the server 31 using the POST method of the HTTP request. As shown in FIG. 4, the driving history information S includes the identification information Id, the number of data Nd and the acquisition interval Tw, and a plurality of data sets Sa. The data set Sa is a combination of the power consumption Wv [Wh], the indoor temperature Tr, and the set temperature Ts. The probe device 11 acquires the data set Sa every time a predetermined acquisition interval Tw shorter than the transmission interval Ti elapses.

[0025] The identification information Id is a unique identifier (string or numerical value) pre-assigned to the probe device 11. The number of data Nd is the number of data sets Sa included in the operation history information S. In the present embodiment, the transmission interval Ti is, for example, 30 minutes, and the acquisition interval Tw at which the probe device 11 acquires data from the indoor air conditioner 41 is 1 minute (= 60 seconds). Therefore, the number of data Nd is "30". Note that each of the transmission interval Ti and the acquisition interval Tw can be changed as appropriate.

[0026] The probe device 11 acquires, as the power consumption amount Wv for the acquisition interval Tw, the integrated value of the power consumption Wa repeatedly acquired by the power meter 66 during the acquisition interval Tw (that is, a value corresponding to the value obtained by integrating the power consumption Wa with respect to time, the power correlation amount). In addition, the probe device 11 acquires the power consumption amount Wv, the indoor temperature Tr (detected by the room thermometer 67) and the set temperature Ts (acquired via the input / output unit 65) at the time when the power consumption amount Wv is acquired as a data set Sa.

[0027] Furthermore, when the probe device 11 has acquired the data sets Sa in a number equal to the number of data Nd (that is, when the transmission interval Ti has elapsed), it transmits them to the server 31 as the operation history information S. Note that each of the indoor temperature Tr and the set temperature Ts included in the data set Sa may be the average value of the indoor temperature Tr and the set temperature Ts repeatedly acquired during the acquisition interval Tw.

[0028] When the server 31 receives the operation history information S from the air conditioner 41 (specifically, the probe device 11), it acquires the model information Mc, the location area Lc, and the outside air temperature Te of the air conditioner 41, which will be described later. The server 31 stores, in the storage device 75 as registration data R (see FIG. 8), the identification information Id included in the operation history information S, and each of the plurality of power consumption amounts Wv, indoor temperatures Tr, and set temperatures Ts, together with the location area Lc, the model information Mc, the outside air temperature Te, and the time information Ta.

[0029] The model information Mc is an identifier representing the model (model information) of the air conditioner 41. The model information Mc of the air conditioner 41 is pre-assigned to each combination of the manufacturer (manufacturing company) and the model number assigned by the manufacturer. The server 31 stores the correspondence relationship between the pre-acquired identification information Id and the model information Mc in the storage device 75 as a "model DB". The server 31 acquires the model information Mc by applying the identification information Id of the operation history information S to the model DB.

[0030] The location area Lc in the present embodiment is an identifier representing the area divided by each of the observation points of the "Regional Meteorological Observation System" (AMeDAS, Amedas) installed by the Japan Meteorological Agency. The server 31 acquires the area (in the present embodiment, municipalities in Japan) where the air conditioner 41 is installed based on the transmission source address of the operation history information S (that is, an IP address compliant with the IPv4 standard).

[0031] The outside air temperature Te in the present embodiment is the air temperature observed at each of the observation points publicly disclosed by the Japan Meteorological Agency. The server 31 stores in the storage device 75, as the outside air temperature Te, the air temperature observed by the observation point corresponding to the location area Lc during the period when the power consumption Wv included in the received operation history information S was acquired. In addition, for the said observation point and period, if the outside air temperature Te has already been acquired when receiving the operation history information S from an air conditioner different from the air conditioner 41, the server 31 does not newly acquire the air temperature information from the Japan Meteorological Agency. In addition, the server 31 may acquire the outside air temperature Te from the Japan Meteorological Agency when generating the history comparison information described later.

[0032] More specifically, the server 31 stores, in the storage device 75 as a "location DB", the correspondence relationship between the IP address acquired in advance and the municipality. The server 31 acquires the location of the air conditioner 41 by applying the source IP address of the driving history information S to the location DB. That is, the server 31 acquires the location of the air conditioner 41 using well-known GeoIP technology. Next, the air conditioner 41 acquires, as the location area Lc of the air conditioner 41, an identifier representing the observation point closest to the acquired location. Note that the position information for specifying the location is not limited to the IP address, and other information may be used.

[0033] The time information Ta represents the time when the information related to the data set Sa was acquired (specifically, the start period of the acquisition interval Tw). The server 31 acquires (by inverse calculation) and stores in the storage device 75 the respective time information Ta (in this embodiment, the UNIX (registered trademark) time of Coordinated Universal Time (UTC)) of the data set Sa based on the time when the driving history information S was received and the acquisition interval Tw included in the driving history information S.

[0034] (Viewing of history comparison information) FIG. 6 is an example of the history comparison information displayed on the mobile terminal 21 according to the first embodiment. When the user accesses the server 31 using the web browser of the mobile terminal 21 and performs a predetermined operation, the mobile terminal 21 transmits a "comparison information request" (specifically, an HTTP request) to the server 31 as shown in the upper part of FIG. 4. The comparison information request includes the identification information Id and the "comparison time zone" specified by the user. The comparison time zone is, for example, a period from a point 6 hours before the current time to the current time (as an example, a period from 15:00 to 21:00 on that day).

[0035] When the server 31 receives the comparison information request, it generates "history comparison information" and transmits it to the mobile terminal 21 as a response to the comparison information request (see the upper part of FIG. 4). The history comparison information can also be displayed on the display 21a of the mobile terminal 21 received from the server 31. For example, the mobile terminal 21 may be displayed using a web browser.

[0036] The solid line L1 in FIG. 6 generally shows the change in the power consumption Wv of the air conditioner 41 in each of the comparison periods Pm obtained by dividing the comparison time zone at every predetermined comparison time Tc (that is, having a length equal to the comparison time Tc). Each of the solid lines L2 and L3 generally shows the change in the maximum and minimum values of the power consumption Wv of (a plurality of) "similar products" that satisfy the "approximate conditions" (first approximate conditions) in each of the comparison periods Pm.

[0037] In the present embodiment, the comparison time Tc is 10 minutes. As an example, the comparison period Pm is the period from 15:00 to 15:10 on the same day. The similar products are other air conditioners that have the same model information Mc and location area Lc as the air conditioner 41 (that is, the air conditioner corresponding to the comparison information request, also referred to as the "specific product"). The approximate condition is a condition that holds when the operating situation in the comparison period Pm is similar to that of the air conditioner 41.

[0038] More specifically, for the power history information of the similar products stored in the storage device 75, if the magnitude of the difference between each of the indoor temperature Tr, set temperature Ts, and outside air temperature Te related to the air conditioner 41 in the comparison period Pm (or at the start or end of the comparison period Pm) is smaller than a predetermined temperature threshold Th1, then the power history information satisfies the approximate condition.

[0039] In other words, if |Tr1 - Tr2| < Th1, |Ts1 - Ts2| < Th1, and |Te1 - Te2| < Th1, the approximate condition holds. Here, each of the indoor temperature Tr1, set temperature Ts1, and outside air temperature Te1 is the indoor temperature Tr, set temperature Ts, and outside air temperature Te related to the air conditioner 41. In addition, each of the indoor temperature Tr2, set temperature Ts2, and outside air temperature Te2 is the indoor temperature Tr, set temperature Ts, and outside air temperature Te related to the similar product. The fact that the magnitude of the difference between any of these temperatures related to the air conditioner 41 and the corresponding temperature related to the similar product is smaller than the temperature threshold Th1 is hereinafter simply referred to as (the temperatures being) "approximate".

[0040] The server 31 extracts the power consumption amount Wv related to the power history information (specifically, the combination of the indoor temperature Tr, the set temperature Ts, and the outside air temperature Te) related to similar products and satisfying the approximation conditions as the "comparative power correlation amount". In addition, the server 31 extracts the minimum value and the maximum value from the (plural) comparative power correlation amounts (that is, the power consumption amount Wv) extracted in a certain comparison period Pm. The server 31 executes the process of extracting the minimum value and the maximum value of the comparative power correlation amount for each of the comparison periods Pm included in the comparison time zone, and generates history comparison information.

[0041] For example, since the location areas Lc where the outside air temperature Te is acquired (observed) for the air conditioner 41 and the similar products are equal to each other, the outside air temperature Te related to the power history information of the air conditioner 41 in a certain comparison period Pm and the outside air temperature Te related to the power history information of the similar products in that comparison period Pm are equal to each other. That is, the outside air temperature Te is approximated. Further, if each of the indoor temperature Tr and the set temperature Ts related to the similar products acquired in that comparison period Pm is approximated to the indoor temperature Tr and the set temperature Ts related to the air conditioner 41, the approximation condition is satisfied.

[0042] On the other hand, even for the power history information related to the power consumption amount Wv of similar products acquired at a time point different from the comparison period Pm, the outside air temperature Te of the power history information may be approximated to the outside air temperature Te of the power history information related to the power consumption amount Wv of the air conditioner 41 acquired in the comparison period Pm. In this case, further, if each of the indoor temperature Tr and the set temperature Ts is approximated, the approximation condition is satisfied. That is, the power consumption amount Wv of similar products acquired at a timing different from the comparison period Pm may be extracted as the comparative power correlation amount.

[0043] In this embodiment, the model information Mc is also referred to as "product attributes" for convenience. That is, air conditioners with equal model information Mc have the same product attributes. In this embodiment, the temperature threshold Th1 is 1.5°C. However, each of the above-mentioned comparison time Tc and temperature threshold Th1 may be appropriately changed so that the difference in power consumption Wv (power correlation amount) between a specific product (i.e., the air conditioner corresponding to the comparison information request) and a similar product becomes clear. For example, different values (temperatures) may be set for the temperature threshold Th1 for each of the indoor temperature Tr, the set temperature Ts, and the outside air temperature Te.

[0044] (Delivery of Notification Information) As described above, the server 31 receives the operation history information S from the probe device 11 every time the transmission interval Ti elapses. When there is information to be notified to the user of the air conditioner 41 incorporating the probe device 11, the server 31 transmits "notification information" to the probe device 11 as a response when receiving the operation history information S, as shown in FIG. 4(a). In this embodiment, the notification information is transmitted from the server 31 to the probe device 11 as a response (HTTP response) to the HTTP request transmitted as the operation history information S. That is, the server 31 transmits the notification information to the probe device 11 via the TCP connection established for transmitting the operation history information S.

[0045] The notification information is executed, for example, when a person in charge of the manufacturer that produced the air conditioner 41 inputs the model information Mc to be notified to the server 31 as a "notification request" according to a predetermined procedure. The notification request is input to the server 31, for example, by an HTTP request transmitted to the server 31. Alternatively, the notification request may be input by a maintenance person to the terminal of the server 31.

[0046] When the probe device 11 receives the notification information, it lights up the light-emitting device 68 of the air conditioner 41. A user who notices the lighting of the light-emitting device 68 can check the detailed information by accessing, for example, the website of the manufacturer of the air conditioner 41. Note that the notification information includes a predetermined reason code (numerical value), and the probe device 11 that has received the notification information including the reason code and the air conditioner 41 may be configured to cause a speaker (not shown) built in the air conditioner 41 to play a voice corresponding to the reason code.

[0047] Note that, as shown in FIG. 4(b), the driving history information S may be transmitted from the mobile terminal 21 to the server 31 (instead of the probe device 11). In this case, the probe device 11 and the mobile terminal 21 perform communication compliant with the WiFi or Bluetooth standard. Specifically, the probe device 11 acquires and stores the data set Sa every time the acquisition interval Tw elapses. On the other hand, the mobile terminal 21 transmits a "driving history information request" to the probe device 11 at an arbitrary timing of the user.

[0048] When the probe device 11 receives the driving history information request, it transmits a "driving history information response" to the mobile terminal 21. The driving history information response includes the (plural) data sets Sa acquired by the probe device 11 from the time when the driving history information request was last received until the current time. When the mobile terminal 21 receives the driving history information response, it transmits the driving history information S including the data set Sa received from the probe device 11 to the server 31. Note that when transmitting the driving history information S from the mobile terminal 21 to the server 31, the probe device 11 continues to hold the driving history information S for a certain period until it receives the history information acquisition request from the mobile terminal 21. However, when the upper limit of the holding capacity is reached, it is overwritten from the oldest history.

[0049] When a notification request regarding the model information Mc of the air conditioner 41 is input, the server 31 transmits (replies) notification information to the mobile terminal when receiving the driving history information S from the mobile terminal 21. In this case, the mobile terminal 21 notifies the user that the notification information has been received. Alternatively, if a comparison information request is received after the notification request is input and before the driving history information S is received from the mobile terminal 21, the server 31 transmits the notification information together with the history comparison information.

[0050] (External probe device) Next, the operation of the probe device 12 will be described centering on the differences from the above-described built-in probe device 11. As shown in FIG. 1, the probe device 12 is disposed at a location with a small temperature error from the air conditioner 42 installed in the house 1b. The probe device 12 is interposed in the power line Cp of the air conditioner 42. The probe device 12 and the air conditioner 42 operate by the power supplied from the power line Cp.

[0051] The probe device 12 is a well-known small computer similar to the probe device 11, and includes a CPU 61, a ROM 62, a RAM 63, and a light emitting device 69 (see FIG. 1). The probe device 12 incorporates a wattmeter (not shown) disposed with respect to the power line Cp, and detects the power consumption Wa of the air conditioner 42. In addition, the probe device 12 incorporates a room thermometer (not shown) that detects the temperature of the room in which the air conditioner 42 is installed as the indoor temperature Tr.

[0052] Since the probe device 12 cannot directly obtain the set temperature Ts, an unrealistic value (for example, 256°C) is set for the set temperature Ts included in the data set Sa. In this case, the server 31 estimates the actual set temperature Ts. Specifically, when the server 31 determines that the power consumption Wa of the air conditioner 42 is in the vicinity of the minimum value during operation (minimum power state) based on the power consumption Wv included in the received data set Sa and has continued for a predetermined time, the server 31 acquires (estimates) the indoor temperature Tr at that time as the set temperature Ts. For example, the server 31 treats the indoor temperature Tr when the minimum power state has continued for a predetermined time or more as the set temperature Ts during the period from after the power consumption Wa has increased after the minimum power state occurred previously until the minimum power state occurs again and continues for a predetermined time or more.

[0053] The probe device 12 transmits the operation history information S to the server 31 via the in-house router 52 arranged in the house 1b and the network 30 every time the transmission interval Ti elapses. Naturally, the identification information Id assigned to the probe device 12 is different from the identification information Id of the probe device 11.

[0054] When the probe device 12 receives the notification information from the server 31, it lights up the light emitting device 69. In addition, similar to the example of the house 1a described above, when the user operates the mobile terminal 21 in the house 1b to send a comparison information request to the server 31, the server 31 sends the history comparison information to the mobile terminal 21. As a result, a graph related to the history comparison information (see the example of FIG. 6) is displayed on the display 21a.

[0055] (Modification example of transmission of operation history information S) A modification of Embodiment 1 (the first modification) will be described. In Embodiment 1 described above, the server 31 extracted the power consumption amount Wv related to the power history information related to similar products and satisfying the first approximation condition as the comparison power correlation amount. The similar products in Embodiment 1 were air conditioners (other than the specific product) with the same model information Mc and location area Lc as the specific product. In contrast, the similar products in the first modification are air conditioners (other than the specific product) with the same "product group" and location area Lc as the specific product. Hereinafter, the description will focus on this difference.

[0056] The server 31 according to the first modification stores in the storage device 75 the set of model information Mc belonging to each of a plurality of product groups as the "product group DB". That is, when a certain model information Mc is applied to the product group DB, a plurality of model information Mc included in the product group to which the model information Mc belongs are acquired. Each of the product groups according to this modification is a set of model information Mc of air conditioners whose annual energy consumption efficiency (APF) announced by the manufacturer and the standard of the usage environment (specifically, the size of the room used, for example, represented by the number of tatami mats in a Japanese-style room or the area of a Western-style room) are approximately the same as each other.

[0057] In other words, the product group is a set of model information Mc of air conditioners for which the power consumption Wa consumed when the usage situation is similar to that of a specific product (for example, the air conditioner 41) is likely to be approximated to the specific product. In this modification, the product group may include an air conditioner with the same model information Mc as the specific product, but the same model information Mc as the specific product may be omitted. In this modification, the product group is also referred to as "product attribute" for convenience. That is, the air conditioners included in the same product group have the same product attributes as each other.

[0058] When the server 31 receives a comparison information request, it acquires the type information Mc by applying the identification information Id included in the comparison information request to the type DB. In addition, the server 31 acquires the product group related to the comparison information request (that is, the set of type information Mc corresponding to the similar products according to this modification example) by applying the type information Mc related to the comparison information request to the product group DB. Further, the server 31 extracts the power consumption amount Wv (that is, the comparison power correlation amount) related to the power history information related to the similar products and satisfying the first approximation condition. Next, the server 31 executes, for each of the comparison periods Pm, the process of extracting the minimum value and the maximum value from the extracted comparison power correlation amounts, and generates history comparison information.

[0059] (Regarding data analysis using the collected information) FIG. 7 is a block diagram for explaining the outline of the functions of the home appliance control information generation system 1 according to the first embodiment. FIG. 8 is a block diagram for explaining the flow of information processed in the home appliance control information generation system 1 according to the first embodiment. The server 31 is a device that not only collects information from the probe devices 11 and 12 or the mobile terminal 22 described above, but also processes the collected information and generates information for controlling air conditioning devices such as the air conditioners 41 and 42.

[0060] Generally, for air conditioners 41 and 42, etc., it is preset how to control the operation when the set temperature Ts is set for each product or how to control the indoor temperature Tr, wind force, etc. to make the indoor state comfortable when in the automatic operation mode. These controls are usually set individually according to comfort indexes such as PMV and the types of air conditioners 41 and 42. However, the environments where air conditioning devices such as air conditioners 41 and 42 are installed are various. For example, even within Japan, the climate varies by region, so the outside air temperature Te and outside air humidity Tf are different, and the clothes worn by users indoors are also different. Also, since the structures of the buildings where they are installed are different, the comfort levels felt by users also vary due to these factors. Therefore, the operation based on the control information preset in air conditioners 41 and 42 may be comfortable in one region but not in another region. The home appliance control information generation system 1 according to Embodiment 1 acquires the operation history information S from a large number of air conditioning devices using a probe device 11, etc., and analyzes it to obtain the optimal operation conditions of the air conditioning device in a certain region.

[0061] Since the server 31 can determine in which region the probe devices 11, 12, and the mobile terminal 21 are located based on the source address (IP address) of the operation history information S, it can acquire the region where the air conditioners 41, 42, etc. are installed. Therefore, even if the server 31 acquires the operation history information S from all over the country, for example, it can determine in which location region Lc the operation history information S is from.

[0062] In addition, the server 31 can collect data from weather observation stations Wo in various regions. Therefore, if it can be determined which location area Lc the driving history information S is in, for example, the weather observation station Wo closest to the location area Lc can be selected, and information such as the outside air temperature Te and the outside air humidity Tf announced by the weather observation station Wo can be associated with the driving history information S as reference values. Note that the weather information of the weather observation station Wo used to classify the driving history information S may be referred to as "location area weather information". In addition, since the data announced by the weather observation station Wo also includes information such as the altitude of that point, for example, when the altitude of the area where the air conditioners 41, 42, etc. are actually installed is different from the altitude of the weather observation station Wo, the outside air temperature Te and the outside air humidity Tf serving as reference values can be corrected according to the altitude difference and then associated with the driving history information S.

[0063] Also, as shown in FIG. 8, the acquired driving history information S is associated with other information by the information processing unit 31a and stored in the storage unit 31b. The driving history information S is stored in the storage unit 31b as registration data R in association with, for example, the time information Ta at the time of information acquisition detected by the timer unit 31c and the weather information obtained from the weather observation station Wo. The server 31 stores the registration data R in the following procedure.

[0064] FIG. 9 is a flowchart until the server 31 of the home appliance control information generation system 1 according to Embodiment 1 stores the registration data R. As shown in FIG. 7, the server 31 acquires driving history information S (data set Sa) from a plurality of location areas Lc via the network 30. At that time, the server 31 specifies the location area Lc from the IP address of the probe device (step A1).

[0065] Next, the server 31 specifies the weather observation station Wo closest to the location area Lc and associates it with the driving history information S (step A2).

[0066] Next, the server 31 acquires the weather information of the corresponding weather observation station Wo. The content of the weather information is, for example, the outside air temperature Te, the outside air humidity Tf, and the altitude. The server 31 extracts data from the weather information as needed and associates it with the operation history information S as a reference value (step A3). The reference value is the weather information that serves as a reference for the operation history information S in the location area Lc, and can also be used to classify the data collected from a large number of air conditioners based on the reference value.

[0067] Next, the server 31 associates the operation history information S and the reference value and stores them in the storage unit 31b as registration data R (step A4). Note that the operation history information S and the reference value are also associated with time information. Since the operation history information S is a collection of a plurality of data sets (for example, 30) collected over a certain period of time in the probe device 11 and transmitted together, based on the time information given by the timer unit 31c at the time of acquisition, the time when the data was collected in the probe device 11 can be estimated from the number of data sets Sa included in the plurality of data sets Sa and assigned to each data set Sa. The weather information is data announced by the weather observation station Wo, for example, every 10 minutes, and the one with the time information closest to the time information assigned to each data set Sa is associated with the data set Sa and stored in the storage unit 31b.

[0068] The server 31 analyzes the acquired registration data R and derives control information (operation conditions) for the air conditioner suitable for the location area Lc. The server 31 classifies the registration data R based on the power consumption Wv among the operation conditions of the air conditioner, and derives the optimal control information from the classified registration data R. Specifically, the server 31 derives the optimal combination of the environmental information including the indoor temperature and indoor humidity of the environment where the air conditioner included in the registration data R is installed, the weather information, and the control information including the power consumption of the air conditioner, the output of the compressor, the outdoor blower, and the indoor blower. The following describes the procedure for the server 31 to classify the registration data R based on the power consumption Wv.

[0069] Specifically, the server 31 extracts region-specific driving history information Sr based on the location region Lc from the driving history information S included in the registration data R, and further extracts driving history information by power consumption based on the region-specific driving history information Sr and the power consumption Wv. The server 31 derives control information for the air conditioner that is optimal for the environmental information and weather information from the extracted multiple pieces of driving history information by power consumption and the reference values (data from the weather observation station) related thereto.

[0070] FIG. 10 is an example of a flowchart in which the server 31 of the home appliance control information generation system 1 according to the first embodiment classifies and analyzes the registration data R based on the power consumption Wv. First, the server 31 extracts the maximum value and the minimum value of the power consumption Wv from the registration data R of a plurality of air conditioners belonging to the same location region Lc (step B1).

[0071] From the extracted power consumption Wv, the server 31 obtains the difference ΔWv between the maximum value and the minimum value of the power consumption Wv (step B2). Depending on the magnitude of the difference ΔWv, the server 31 creates a plurality of groups for classifying the power consumption Wv (step B3). For example, when the maximum value of the power consumption Wv is 10 kWh and the minimum value is 0 kWh, the server 31 creates 10 groups where the power consumption Wv is 0 kWh or more and less than 1 kWh, 1 kWh or more and less than 2 kWh, …, and 9 kWh or more and less than 10 kWh. The number of the plurality of groups is not limited. When the maximum value is 5 kWh and the minimum value is 0 kWh, it may be divided into 5 groups or a smaller number of groups.

[0072] Next, the server 31 analyzes the registration data R for each group (step B5). Since each group classifies the registration data R based on the power consumption amount Wv, for example, the registration data R included in the same group includes the transition of the set temperature Ts, the power consumption Wa, the indoor temperature Tr, and the outside air temperature Te of a plurality of air conditioners operated with substantially the same output within a predetermined time. From the registration data R classified based on these power consumption amounts Wv, the server 31 extracts a combination of the set temperature Ts and the power consumption Wa that seems to be optimal. The extracted set temperature Ts and power consumption Wa are also associated with the indoor temperature Tr and the outside air temperature Te at that time, and are stored in the storage unit 31b as an optimal operating state under certain environmental information (indoor temperature Tr) and weather information (outside air temperature Te). The server 31 derives control information for the air conditioner from the optimal operating state stored in the storage unit 31b.

[0073] As described above, the server 31 classifies the registration data R based on the power consumption amount Wv and then performs analysis to derive optimal control information for the air conditioner. The derivation of the control information is performed, for example, based on the degree of variation of the setting information of the air conditioner within a predetermined time. An example of the derivation of the control information in the server 31 will be described below.

[0074] FIG. 11 is an example of two data sets included in the registration data R of one group classified based on the power consumption amount Wv in the server 31. What is shown in FIG. 11(a) is called a data set S1, and what is shown in FIG. 11(b) is called a data set S2. The data sets S1 and S2 are the temporal transitions of the outside air temperature Te, the indoor temperature Tr, the set temperature Ts, and the power consumption Wa during a predetermined period in the same time zone. FIGS. 11(a) and (b) have the time t on the horizontal axis and the temperature T [°C] and the power W [W] on the vertical axis.

[0075] Although the data sets S1 and S2 have different ways of varying power consumption, they have approximately the same power consumption in the time period shown in FIG. 11. Also, since the data sets S1 and S2 belong to the same location Lc, the outside air temperature Te is also equal. In FIG. 11, the air conditioner is operating in the cooling mode.

[0076] For the data set S1, the initial set temperature Ts is set high, but it shows the case where the user lowers the set temperature Ts because the indoor temperature Tr is high. When the user lowers the set temperature Ts, the air conditioner increases its output, and gradually the indoor temperature Tr decreases. When the indoor temperature Tr drops to the set temperature Ts, the air conditioner reduces its output. Then, the indoor temperature Tr rises. When the indoor temperature Tr exceeds the set temperature Ts by a predetermined temperature, the air conditioner increases its output again. In the data set S1, similar control is repeated thereafter.

[0077] For the data set S2 as well, the initial set temperature Ts is set high as in the data set S1, but it shows the case where the user lowers the set temperature Ts because the indoor temperature Tr is high. When the user lowers the set temperature Ts, the air conditioner increases its output, and gradually the indoor temperature Tr decreases. Here, the user feels that the indoor temperature Tr has dropped too much and raises the set temperature Ts, and the air conditioner also reduces its output. After a while, when the indoor temperature Tr rises, the user lowers the set temperature again, and the air conditioner increases its output. Thereafter, the user repeats the same operation, and the air conditioner also varies its output accordingly.

[0078] When the data sets S1 and S2 shown in FIG. 11 are included in the groups classified by the magnitude of the power consumption Wv, the server 31 extracts, in the analysis unit 31d, those with less frequent setting changes of the air conditioner, and derives optimal control information from their operation history information in the control information generation unit 31e. That is, when there are the data sets S1 and S2, the server 31 derives control information from the operation state of the data set S1, assuming that the data set S1 with less variation in the set temperature Ts is in a more ideal operation state.

[0079] Since the dataset S1 shows at least the relationship between the passage of time and the power consumption Wa, the server 31 obtains, for example, the power consumption Wv at a predetermined time t1 shown in Fig. 11(a), and generates control information so that the air conditioner operates to achieve this power consumption Wv when operating for the predetermined time t1. Alternatively, the server 31 may use the operating state along the change in the power consumption Wa at the predetermined time t1 shown in Fig. 11(a) as the control information.

[0080] In Fig. 11, only the set temperature Ts is shown as the setting information of the air conditioner, but it may also include other information such as the wind force, wind direction, and change in the operating mode. If the setting information includes information such as the wind force, these may also be included in the evaluation of the change frequency.

[0081] The process by which the server 31 derives the control information has been described above using the example shown in Fig. 11. In reality, the registered data R includes a large number of datasets. The setting change frequency information is extracted from these, and the registered data R is classified based on the setting change frequency information. For example, datasets with a change frequency below a predetermined value in the registered data R are extracted as low change frequency operation history information as being in a more ideal operating state, and the control information is derived based on the operating state of the air conditioner included in the low change frequency operation history information. When deriving the control information, the average output of the compressor, outdoor blower, and indoor blower may be obtained based on the average value of the power consumption included in the operating state of the extracted air conditioner. If the registered data R includes data on the output of the compressor, outdoor blower, and indoor blower, their average values may also be used as the control information.

[0082] FIG. 12 is another example of a flowchart in which the server 31 of the home appliance control information generation system 1 according to Embodiment 1 classifies and analyzes the registration data R based on the power consumption Wv. In the above, an example has been described in which the registration data R is classified based on FIG. 10, an ideal operating state is extracted from among the power consumptions Wv of the same degree, and the extracted data is analyzed. However, in the flow shown in FIG. 12, the server 31 extracts a data set having a predetermined change frequency or less from the registration data R, classifies the extracted data set into a plurality of groups based on the power consumption Wv, and analyzes the data for each group.

[0083] In the case of the example shown in FIG. 12, before steps B1 to B5 shown in FIG. 10, a data set having a predetermined setting change frequency or less is extracted from the registration data R based on the setting change frequency information (step C1). The subsequent steps C2 to C6 have the same content as steps B1 to B5 shown in FIG. 10. By performing such processing, the server 31 can first exclude data unnecessary for generating the control information of the air conditioner.

[0084] (Modification example of analysis and control information generation by server 31) The server 31 extracts a data set from the registration data R based on the setting change frequency information, and derives the control information of the air conditioner based on the operating state of the air conditioner in the extracted data set. When deriving the control information, a learning model may be used. In this case, the analysis unit 31d of the server 31 generates a learning model, and the control information generation unit 31e inputs the operation history information acquired from the probe device 11 or the like into the generated learning model and outputs the control information.

[0085] FIG. 13 is an explanatory diagram of an example of the functions of the analysis unit 31d and the control information generation unit 31e of the server 31 according to Embodiment 1. The operation history information S and the weather information are input to the analysis unit 31d. Since the operation history information S is data including the passage of time and the variation of each parameter as shown in FIG. 11, the setting change frequency information based on the variation of the preset temperature Ts and the power consumption Wa may be extracted and input to the analysis unit 31d in advance.

[0086] The analysis unit 31d generates a learning model based on the registered data R. In the case of unsupervised learning, the analysis unit 31d is designed to automatically identify patterns, relationships, anomalies, etc. of each data. For example, the analysis unit 31d can identify driving states under different environmental information and weather information from the driving history information S using clustering and principal component analysis (PCA), and generate a learning model capable of realizing an efficient driving state therefrom. In the case of supervised learning, the analysis unit 31d uses, for example, a dataset of set temperature, operating state of the air conditioner (output of the air conditioner), environmental information, and weather information when the change frequency is less than a predetermined value as teacher data (labeled data) to generate a learning model. Each time the analysis unit 31d newly acquires driving history information, it learns and can generate a learning model capable of outputting a highly accurate driving state or control information that makes the user feel more comfortable.

[0087] Also, the analysis unit 31d may generate a learning model by reinforcement learning. For example, the analysis unit 31d is set such that the reward obtained is higher as the setting change frequency is lower, and generates a learning model to maximize the reward. Further, the analysis unit 31d can generate a learning model corresponding to various situations by setting such that the reward obtained is higher when the power consumption Wv is low and when the fluctuations such as the indoor temperature Tr are small. The learning model takes environmental information and weather information as inputs and the operating state or control information of the air conditioner as outputs.

[0088] (Example of Control of Air Conditioner by Server 31) FIG. 14 is a flowchart of an example of control of an air conditioner by control information generated by the server 31 according to Embodiment 1. The server 31 identifies the location area Lc from the driving history information S acquired from the probe device 11 of the air conditioner to be controlled (step D1). Next, the server 31 identifies the weather observation station Wo closest to the location area Lc and associates it with the driving history information S (step D2). Further, the server 31 acquires weather forecast data from the corresponding weather observation station Wo (step D3).

[0089] Next, the server 31 analyzes the weather forecast data. Here, it determines whether the forecast value of the outside air temperature Te will rise in the future (step D4). When it is determined that the outside air temperature Te will rise in the case of the air conditioner operating in the heating mode (yes in step D4), the server 31 transmits control information for suppressing the output to the air conditioner (step D5). Also, in the case of the determination that the outside air temperature Te will not rise (no in step D4), the server 31 transmits control information for normal output to the air conditioner (step D6).

[0090] In steps D5 and D6, for example, the result obtained by previously inputting the weather forecast data into the learning model may be used as the control information. In this way, by creating the control information based on the weather forecast data, the air conditioner can operate more efficiently while ensuring the comfort of the user. For example, when a certain air conditioner is operating in the cooling mode, the current temperature is high, and the weather forecast data predicts that the temperature will gradually decrease in the time period ahead, the server 31 generates control information for gradually reducing the output in accordance with the decreasing temperature in the time period ahead. By gently controlling the operating state of the air conditioner in accordance with the temperature in this way, the operation of the air conditioner becomes efficient.

[0091] As described above, according to the home appliance control information generation system 1 according to Embodiment 1, the operation history information S is acquired from air conditioners in different regions, and the data is analyzed based on the location region Lc. As a result, it becomes possible to obtain optimal control information for each region with different climates. Further, since the home appliance control information generation system 1 stores the operation history information S and the weather information in time series and performs analysis, it is possible to obtain optimal control information in each case even when, for example, the seasons or the weather are different. Note that the home appliance control information generation system 1 can also generate control information by inputting weather forecast data into the learning model. In this case, since the learning model outputs optimal control information for the air conditioner according to the weather forecast data, the determination shown in step D4 of FIG. 14 becomes unnecessary. However, the server 31 may switch the learning model used according to the weather forecast data.

[0092] According to the home appliance control information generation system 1 according to Embodiment 1, it becomes possible to output optimal control information every time the registered data R increases. By using the optimal control information suitable for various installation environments generated by the home appliance control information generation system 1 instead of controlling the air conditioner according to indexes such as the conventional PMV, it is possible to always operate the air conditioner in accordance with the latest environment (climate, region, living environment, etc.).

[0093] In the above description, the home appliance control information generation system 1 has been described by taking an air conditioner as an example. However, other home appliances such as refrigerators can be targeted for generating control information. For example, the power consumption Wa of a refrigerator can also vary depending on the opening and closing frequency of the refrigerator, the outside air temperature Te, the set temperature Ts of the air conditioner installed indoors, and the indoor temperature Tr. Therefore, the server 31 may be configured to input the power consumption Wa of the refrigerator, the outside air temperature Te, the set temperature Ts of the air conditioner, and the indoor temperature Tr into the analysis unit 31d for analysis. For example, the power consumption Wa of the refrigerator, the outside air temperature Te, the set temperature Ts of the air conditioner, and the indoor temperature Tr may be input into the analysis unit 31d to generate a learning model, and the server 31 may be configured to output the optimal operating state or control information of the refrigerator according to the outside air temperature Te, the set temperature Ts of the air conditioner, and the indoor temperature Tr. Similar to the case of the above air conditioner, the learning model generated in the analysis unit 31d can output the optimal operating state or control information of the refrigerator by supervised learning, reinforcement learning, etc.

[0094] FIG. 15 is a flowchart showing an example of generating home appliance control information by the home appliance control information generation system 100 according to Embodiment 1. The power consumption Wa of the refrigerator is affected by the indoor temperature Tr and the operating state of the air conditioner. Therefore, in FIG. 15, the home appliance control information generation system 100 generates control information for the air conditioner and the refrigerator such that the power consumption Wv of the refrigerator decreases and the indoor environment becomes comfortable.

[0095] First, the server 31 acquires the set temperature Ts and the operating mode of the air conditioner (step E1). Next, the server 31 determines whether the air conditioner is in the cooling operation (step E2). If the air conditioner is in the cooling operation (yes in step E2), the server 31 obtains the reduction amount of the power consumption of the refrigerator when the indoor temperature Tr drops with respect to a certain predetermined indoor temperature A (step E3). If the air conditioner is not in the cooling operation (no in step E2), the server 31 obtains the increase amount of the power consumption of the refrigerator when the indoor temperature Tr rises with respect to a certain predetermined indoor temperature A (step E4).

[0096] After the power consumption changes of these refrigerators are obtained, the server 31 analyzes the increase and decrease amounts of the power consumption of the refrigerators and the operating conditions of the air conditioner, and obtains good operating conditions for the refrigerators and the air conditioner (step E5).

[0097] As described above, the home appliance control information generation system 100 according to the first embodiment can generate control information for a plurality of types of home appliances related to temperature control. In the above, as an example, the generation of control information related to a refrigerator and an air conditioner has been described, but control information may be similarly generated for other home appliances.

[0098] Although the present invention has been described based on each embodiment above, the present invention is not limited only to the configurations of the above-described embodiments. In each of the above embodiments, the home appliance control information generation system 1 is realized in a system such as a client-server system of a network computer system, but various computers such as a personal computer that does not constitute a client-server system, and various communication terminals and portable information terminals such as a mobile terminal and a tablet can also realize the same function as the home appliance control information generation system 1. At this time, at least a part of the functions of the home appliance control information generation system 1 can also be realized by installing a computer program on various computers and various communication terminals and portable information terminals. That is, a program for causing various computers to function as at least a part of the home appliance control information generation system 1 is also included in the present invention. Further, the above embodiments may be implemented in combination. It is noted that various changes, applications, and usage ranges that a so-called person skilled in the art makes as needed are also included in the gist (technical scope) of the present invention.

Explanation of Signs

[0099] 1: Home appliance control information generation system 1a: House 1b: House 11: Probe device 12: Probe device 21: Mobile terminal 21a: Display 22: Mobile terminal 30: Network 31: Server 31a: Information processing unit 31b: Memory unit 31c: Timing unit 31d: Analysis unit 31e: Control information generation unit 40: Air conditioner 41: Air conditioner 42: Air conditioner 43: Infrared light receiving unit 51: Remote control 52: In-house router 61: CPU 62: ROM 63: RAM 64: Data communication unit 65: Input / output unit 66: Power meter 67: Room thermometer 68: Light emitting device 69: Light emitting device 71: CPU 72: ROM 73: RAM 74: Data communication unit 75: Storage device 90: Network 100: Home appliance control information generation system Cp: Power line Id: Identification information Lc: Location area Mc: Model information Nd: Number of data Pm: Comparison period R: Registered data S: Operation history information S1: Data set S2: Data set Sa: Data set Sa: Data set Sr: Region-based operation history information T: Temperature Ta: Time information Tc: Comparison time Te: Outside air temperature Te1: Outside air temperature Te2: Outside air temperature Tf: Outside air humidity Th1: Temperature threshold Ti: Transmission interval Tr: Indoor temperature Tr1: Indoor temperature Tr2: Indoor temperature Ts: Set temperature Ts1: Set temperature Ts2: Set temperature Tw: Acquisition interval W: Electric power Wa: Power consumption Wo: Meteorological observatory Wv: Power consumption amount t1: Period ΔWv: Difference

Claims

1. A home appliance control information generation system that generates control information for home appliances based on operation history information from a plurality of home appliances, comprising: a storage unit that stores the acquired data; an information processing unit that processes the data, wherein the operation history information includes position information that can identify the installed area of each of the plurality of home appliances, setting information, and power consumption; the information processing unit acquires location area weather information, which is weather information of the location area where the plurality of home appliances are installed, based on the operation history information and the position information from the plurality of home appliances via a network; classifies the operation history information for the plurality of home appliances belonging to the location area as location area operation history information; classifies the location area operation history information into a plurality of groups based on the magnitude of the power consumption; analyzes the location area operation history information belonging to the same group among the plurality of groups, and outputs control information for the home appliances. A home appliance control information generation system.

2. The home appliance control information generation system according to claim 1, wherein the information processing unit extracts setting change frequency information for each of the plurality of home appliances from the setting information included in the location area operation history information, and analyzes the location area weather information and the location area operation history information based on the setting change frequency information. A home appliance control information generation system.

3. The home appliance control information generation system according to claim 2, wherein the information processing unit extracts the location area operation history information in which the setting change frequency within a predetermined time included in the setting change frequency information is within a predetermined range as low change frequency operation history information; analyzes the time information included in the low change frequency operation history information, the setting information at the time information, and the location area weather information at the time information; associates at least the setting information and the location area weather information included in the low change frequency operation history information and stores them in the storage unit. A home appliance control information generation system.

4. The home appliance control information generation system according to claim 3, wherein the information processing unit generates a learning model that takes environmental information including the indoor temperature of the environment where the plurality of home appliances are installed and the location area weather information as input and outputs control information for the home appliances. A home appliance control information generation system.

5. The home appliance control information generation system according to claim 3, wherein the information processing unit Using the low-change-frequency operation history information and the local weather information related to the low-change-frequency operation history information as teacher data, An appliance control information generation system that generates a learning model that takes as input environmental information including the indoor temperature of the environment where the plurality of home appliances are installed and the local weather information, and outputs control information for the home appliances, using the teacher data.

6. The home appliance control information generation system according to claim 2, wherein the information processing unit performs reinforcement learning so that the less frequently the setting of each of the plurality of home appliances is changed from the setting information included in the operation history information by region, the more reward is obtained, and generates a learning model that takes as input environmental information including at least the indoor temperature of the environment where the plurality of home appliances are installed and the local weather information, and outputs control information for the home appliances.

7. The home appliance control information generation system according to any one of claims 4 to 6, wherein the environmental information includes the indoor humidity of the environment where the plurality of home appliances are installed.

8. The home appliance control information generation system according to claim 7, wherein the plurality of home appliances are a plurality of air conditioners, and the control information includes outputs for at least one of the compressor, outdoor blower, and indoor blower provided in the plurality of air conditioners.

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