JP2012168675A - Electric power management server, and electric power management program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a system and the like efficiently suppressing power consumption in stores and reducing adverse effects on environment, when performing notification or an operation control of electric apparatuses for reducing power consumption based on acquired power consumption information.SOLUTION: An electric power management server includes: power consumption acquiring means 29 that acquires power consumption of a store 3 or power consumption of electric apparatuses 6, 7, 8, 9, and 11 installed in the store 3; and management means 27 that performs notification or controls the operation of the electronic apparatuses 6, 7, 8, 9, and 11 for the purpose of power consumption reduction based on the power consumption information acquired via the power consumption acquiring means 29. The electric power management server further includes: information acquiring means 31 for acquiring POS information in the store 3; a storage device 24 for storing the POS information and the power consumption information associated with each other; and power consumption estimating means 33 for estimating a subsequent power consumption in the store 3 from the sequentially acquired POS information, based on relevance between the past POS information stored in the storage device 24 and the power consumption information.

Description

  The present invention relates to a power management server that manages the power of a store, and a power management program.

  A store that sells food, daily necessities, and the like requires a large amount of electric power because it is necessary to constantly operate lighting, air conditioning, and the like that illuminate the store during business hours. And with the expansion of business hours in recent years, the amount of power consumed in stores tends to increase further. Such an increase in power consumption not only results in an increase in carbon dioxide, but is also contrary to the modern social situation in which interest in the environment is increasing.

  Therefore, the purpose is to reduce the power consumption based on the power consumption acquisition means for acquiring the power consumption of the store or the power consumption of the electrical equipment installed in the store, and the power consumption information acquired through the power consumption acquisition means. A power management server including a management unit that performs notification or operation control of an electric device has been developed and publicly known (see, for example, Patent Document 1).

JP 2000-356453 A

  The power management server of the above document is intended to reduce the power consumption in the store only by monitoring the power consumption of the store acquired by the power consumption acquisition means or the power consumption of the electrical equipment. Since the situation of the store is not reflected, there is a case where power management according to the situation at the store is not performed and the power consumption at the store cannot be efficiently suppressed.

  In the present invention, when performing notification for the purpose of reducing power consumption based on the acquired power consumption information or operation control of electrical equipment, power consumption in the store is efficiently suppressed, and adverse effects on the environment can be reduced. It is an object to provide a power management server and a power management program.

  The power management server of the present invention includes a power consumption acquisition unit that acquires power consumption of a store or power consumption of an electrical device installed in the store, and power consumption based on the power consumption information acquired through the power consumption acquisition unit. A power management server provided with a management unit that performs notification for the purpose of reduction or operation control of electrical equipment, and associates the POS information and the power consumption information with each other, an information acquisition unit that acquires POS information at a store Power storage for storing power consumption in the store based on the relationship between the past POS information stored in the storage device and the power consumption information, based on the sequentially acquired POS information. A predicting means is provided.

  By adopting the above configuration, POS information is added to the power consumption information, and the future power consumption is expected. Therefore, the power consumption in the store is efficiently suppressed, and the adverse effects on the environment can be reduced. .

  Sales information, which is a kind of POS information for each store, is stored in the storage device in association with the power consumption information, and the power consumption prediction means relates the past sales information stored in the storage device to the power consumption information. Based on the characteristics, it may be configured to predict subsequent power consumption at the store from sales information or power consumption information acquired sequentially.

  Inventory information, which is a kind of POS information of each store, is associated with power consumption information and stored in a storage device, and the power consumption predicting means associates past inventory information stored in the storage device with power consumption information. Based on the characteristics, the power consumption after the store may be predicted from inventory information or power consumption information acquired sequentially.

  The products handled in each store are stored in a storage device in association with the display location in the store, and the distance or route that the user has moved in the store is estimated from the display location of each product purchased by the user. The estimated movement information is stored in the storage device in association with the power consumption information, and the power consumption prediction unit sequentially estimates the estimated movement information or the power consumption information based on the relationship between the past estimated movement information and the power consumption information. Therefore, the power consumption after the store may be estimated.

  The number of products purchased by each user is stored in a storage device, the stay time in each user's store is estimated from the number of products purchased by each user, and the estimated estimated stay information is stored in association with power consumption information. The power consumption predicting means stores the power consumption in the store from the estimated stay information or the power consumption information sequentially estimated based on the relationship between the past estimated stay information and the power consumption information. It may be configured.

  The power consumption acquisition means acquires the power consumption information of each store from each store or the electrical equipment of each store via the network, and the information acquisition means acquires the POS information of each store via the network. It may be said.

  The power management program of the present invention is based on power consumption acquisition processing for acquiring power consumption of a store or power consumption of an electrical device installed in the store, and power consumption information acquired via the power consumption acquisition processing. A power management program having a notification for reducing power consumption or a management process for controlling the operation of electrical equipment, wherein information acquisition means for acquiring POS information in a store, POS information and power consumption information Based on the relationship between past POS information stored in the storage device and power consumption information in a computer having a storage device associated with the storage device, the POS information or power consumption information acquired sequentially is stored in the store thereafter. This is to execute a power consumption prediction process for predicting the power consumption.

  According to the present invention, the POS information is added to the power consumption information and the future power consumption is predicted. Based on this power consumption prediction, the power consumption in the store is efficiently suppressed, and the environment is improved. The adverse effect of can be reduced.

It is a block diagram which shows the example of the management server to which this invention is applied. It is a block diagram which shows the example of the shop side system connected to the management server of FIG. 1 via the network. (A) thru | or (D) is explanatory drawing which shows the example of the data table which each comprises a POS database. (A) thru | or (D) is explanatory drawing which shows the example of the data table which each comprises a power consumption database. (A) thru | or (D) is explanatory drawing which shows the example of the data table which comprises a visitor number database, respectively. (A) thru | or (C) are explanatory drawings which show the example of the data table which respectively comprises an equipment database. (A) is a flowchart of a power consumption prediction unit, and (B) is a notification screen that graphically displays changes over time of power consumption in a store predicted by the power consumption prediction unit. (A) is a flowchart which shows the example of a visitor number estimation means, (B) is the alerting | reporting screen which displayed the time-dependent change of the visitor number in the store estimated by the visitor number prediction means in the graph. (A) is a flowchart showing an example of a main routine of the investment effect prediction means, (B) is a flowchart showing an example of a subroutine for predicting a replacement effect, and (C) is an example of a subroutine for predicting a repair effect. (D) is a flowchart showing an example of a subroutine for predicting an expansion effect. It is a flowchart which shows the example of the main routine of a management means. It is a flowchart which shows the example of the electric power management subroutine which a management means performs. (A) thru | or (C) are explanatory drawings explaining the content of the action instruction | indication screen in power management, respectively. (A) thru | or (C) are explanatory drawings explaining the content of the action instruction | indication screen in power management, respectively. It is a flowchart which shows the example of the subroutine of the electric equipment management which a management means performs. (A) thru | or (C) are explanatory drawings explaining the content of the action instruction | indication screen in electrical equipment management, respectively.

Hereinafter, an embodiment of the present invention will be described based on the illustrated example.
FIG. 1 is a block diagram of a management server to which the present invention is applied, and FIG. 2 is a block diagram of a store-side system connected to the management server of FIG. 1 via a network. The management system shown in FIGS. 1 and 2 includes a management server (power management server, electrical equipment management server) 1 and one or a plurality (illustrated) connected to the management server 1 via a network 2 including a WAN such as the Internet. In this example, it is composed of one store 3.

  The management server 1 manages the power consumption of each store 3, manages various electric devices installed in each store 3, and POS information including various management information such as product information, sales information, and inventory information. Are collected from each store 3. Incidentally, POS is an abbreviation for “Point of sale”, and this POS information includes detailed sales performance information such as “when, which product, what price, how many were sold” and the like.

  The store 3 is at least minimally managed by the management terminal 4, and various electric devices that operate by consuming electric power are provided in the store. In the example shown in the figure, as an electrical device, a register facility 6 that performs accounting processing and sales aggregation of products purchased by a user (customer), an air conditioning facility 7 that keeps the inside of the store at a comfortable temperature, food refrigeration / freezing, and the like A refrigeration / freezing facility 8 to be performed, a cooking facility 9 to cook foods, and a lighting facility 11 for illuminating the inside of the store or products are installed in the store.

  In the plurality of electric devices 6, 7, 8, and 9.11, power consumption is individually detected by the power detection unit 12, and each electric appliance detected by the power detection unit (power detection means) 12 in this way is detected. The power consumption information of the devices 6, 7, 8, 9, 11 is sent to the management terminal 4.

  Of these electrical devices 6, 7, 8, 9, and 11, POS information and other accounting information and sales summary information sent from the register facility 6 are sent to the management terminal 4 by other electrical devices. A certain air conditioning equipment 7, refrigeration / freezing equipment 8, cooking equipment 9, and lighting equipment 11 can be switched between operation and operation stop by a driver unit (operation means) 13, respectively.

  On the other hand, the management terminal 4 includes a communication unit 14 for communicating with the management server 1 via the network 2, a storage device 16 such as an HDD or SSD for storing various data, the register facility 6, and the power detection unit 12. Information management means 17 that receives and stores data sent to the storage device 16 and transmits it to the management server 1, the air conditioning equipment 7, the refrigeration / freezing equipment 8, the cooking equipment 9, and the like described above via the driver unit 13 An operation control means 18 capable of individually controlling the operation of the lighting equipment 11 and the like, and an informing means 19 for informing a store clerk or manager of the store 3 by voice or a graphical image are provided. .

  That is, the management terminal 4 installed in each store 4 collects the POS information in the store 4 and the power consumption information of each electric device 6, 7, 8, 9, 11 based on the information from the register facility 6. And the operation of the other electrical devices 7, 8, 9, 11 excluding the register facility 6 is controlled directly from the store 3 side or from the management server 1 side via the management terminal 4. .

  For this reason, in the storage device 16, the POS data 21A in the store 3 to be managed, the power consumption data 21B, and the equipment that is information on the electrical devices 6, 7, 8, 9, and 11 installed in the store 3 are stored. Data 21 </ b> D is stored, and such information also includes a management server 1 that manages a plurality of stores 4. Incidentally, the power consumption data 21B includes the power consumption information of the entire store 4 in addition to the power consumption information of the electric appliances 6, 7, 8, 9, and 11 installed in the store 4 to be managed. Yes.

  The management server 1 is configured as shown in FIG. 1 in order to centrally manage such a plurality of stores 4 collectively.

  Specifically, the management server 1 stores the communication unit 22 that communicates with each store 3, the acquisition unit 23 that acquires information from the store 4 via the communication unit, and the information acquired by the acquisition unit. The storage unit 24 such as SSD or HDD, the information acquired by the acquisition unit 23 and the information stored in the storage device, the prediction unit 26 that predicts the power consumption at each store 4, and the prediction unit 26 A management unit 27 that performs various managements based on predictions and the like, and a notification unit 28 that reports various states by voice and images are provided.

  The acquisition unit 23 automatically or upon request, the power consumption information of the entire store 3 transmitted from the management terminal 4 individually installed in the plurality of stores 3 and the electric devices 6, 7, 8, 9 , 11 and information acquisition means 31 for acquiring POS information that is information related to the POS data 21A in the store 3, equipment information that is information related to the equipment data 21D, and other various information. Has been.

  Incidentally, the power consumption acquisition means 29 and the information acquisition means 31 are connected via the information management means 17 of the management terminal 4 of the store 3, directly from the power detection unit 12, the cash register facility 6, or as shown in the figure. The above-described various information is acquired from 16.

  The storage device 24 is constituted by a conventionally known relational database. Specifically, the POS database 32A obtained by collecting the POS data 21A from each store 3 into a database and the power consumption from each store 3 are stored. Collecting data 21B, a power consumption database 32B that has been converted into a database, a visitor number database 32C that has been collected from the POS information, and that has been converted into a beta base, and facility information for each store 3 And an equipment database 32D that is converted into a database.

  Incidentally, the power consumption database 32B is associated with necessary information such as POS information, and the number-of-customers database 32C is the number of coupons that are coupons or discount coupons that can be used at a predetermined store 3. And the like, information on the number of stocks of sale products (discounted products) at each store 3, POS information, and the like. Details of these data structures will be described later.

  The prediction unit 26 sequentially acquires each store 3 based on the relationship between the POS information and power consumption information in the past store 4 stored in the storage device 24, and the like. Power consumption prediction means 33 for predicting the subsequent power consumption at the store from the power consumption information at the store, and the number of customers at the past store 4 stored in the storage device 24 for each store 3 Based on the above, etc., the number of visitors predicting means 34 for predicting the number of visitors in the store and the power consumption of the electric devices 6, 7, 8, 9, 11 installed in the store 3 for each store 3 By acquiring the information and the power consumption information of the entire store 3 through the power consumption acquisition means 29, the store 3 when the electric devices 6, 7, 8, 9, and 11 are repaired, replaced, or added Investment effect prediction means 36 for predicting the economic effect of

  The management means 27 performs power management of each store 3 based on the predictions of the power consumption prediction means 33 and the visitor number prediction means 34 and is installed in each store 3 based on the prediction of the investment effect prediction means 36. Management (electrical equipment management) of exchange, repair, and expansion of various electric equipments 6, 7, 8, 9, and 11.

  In the case where there is one store 4 or a small number of stores 4, the management server 1 may be installed directly in the store 4. In this case, the power detection unit 12 and the register facility 6 are directly connected to the acquisition unit 23. And the driver unit 13 is connected directly to the management means 27.

  FIGS. 3A to 3D are explanatory diagrams of data tables constituting the POS database. The POS database 32A includes a store table 37 (see (A) in the figure) in which information on the store 3 is stored, a product table 38 (see (B) in the same figure) in which product information is stored, and stores in each store. A display table 39 (see (C) in the figure) in which display information of products is stored, and a sales table 41 (in the same figure (D)) in which various information when products are sold to the user at the store 3 are extracted and stored. Reference).

  The store table 37 has “store ID” consisting of numbers, “store name” in which the name of the store 3 is stored, and “address” in which the address of the store 3 is stored as fields.

  The product table 38 has “product ID” composed of numbers, “product name” in which the name of the product is stored, and “price” in which the price of the product is stored as fields.

  The display table 39 is a “store ID” in which the store ID in the store table 37 of the store 3 is entered for the purpose of indicating the “display ID” consisting of numbers and the store 3 to which the display information relates. For the purpose of indicating what is the product to be displayed, the “product ID” in which the product ID of the product in the product table 38 is entered, the “display location” indicating the display location, and the time zone for display are shown. “Time zone” is included as a field.

  In other words, data relationship is performed from the store table 37 and the product table 38 to the display table 39, and the display table 39 refers to the information of the store table 37 and the product table 38. Thus, the products handled in each store 3 are associated with the display place in the store 3 and stored in the storage device 24.

  The sales table 41 includes a “sales ID” composed of numbers, and a “store ID” in which the store ID in the store table 37 of the store 3 is entered for the purpose of indicating at which store 3 the product is sold to the user. "Estimated movement information" for storing information on the number of products sold to the user and information on the user's movement route and distance in the store 3 estimated from the products sold to the user And a “time zone” for storing a time zone in which the product is sold to the user. In other words, data relationship is performed from the store table 37 to the sales table 41, and information of the store table 37 is referred to from the sales table 41.

  Incidentally, when the user pays money at the store 3, the information regarding the “number of items sold” can be acquired by the register facility 6. The information regarding the “estimated movement information” includes the product purchased by the user and the store of the product. 3 can be estimated by referring to the display table 39 from the display table 39. That is, by knowing in the display table 39 where the product purchased by the user was placed in the store 3, it is possible to estimate what route the user has moved in the store 3 Become. In addition, information on the number of visitors to each store 3 can be acquired by the register facility 6.

  4A to 4D are explanatory diagrams of data tables constituting the power consumption database. The power consumption database 32B shows the relationship between the sales-power table 42 (see FIG. 5A) showing the relationship between sales information and power consumption information at the store 3, and the inventory information and power consumption information at the store 3. The stock-power table 43 shown (see FIG. 5B) and the stay table 44 showing the relationship between the stay time information (stay information) of the user in the store 3 and the power consumption information (see FIG. 10C) And a movement table 46 (see FIG. 4D) showing the relationship between the movement distance and route information (movement information) of the user in the store 3 and the power consumption information.

  The sales-power table 42 is a store ID in the store table 37 of the store 3 for the purpose of indicating to which store 3 the sales information and the power consumption information are related to “sales-power ID” consisting of numbers. “Sales ID” in which the sales information in the store 3 is entered, “Power consumption” in which the power consumption information in the store 3 is entered, and the sales and power consumption information. The field has a “time zone” indicating whether the data is the data of the store, the data relationship from the store table 37 to the sales-power table 42 is performed, and the store-table 37 from the sales-power table 42 is stored. Information is referenced.

  In short, the sales-power table 42 stores the relationship between the sales information at the store 3, which is a kind of POS information, and the power consumption at the store 3, for each store 3, including changes over time. 24 is stored. Then, the power consumption predicting means 33 reads the change with time from the sales information and power consumption information at the store 3 acquired sequentially, and the change with time is taken as the change with time of past sales information and power consumption information. By matching and referring to similar past data, it becomes possible to predict subsequent power consumption information in the store 3. At this time, if a correlation between sales information and power consumption information in each store 3 is obtained by regression analysis or the like, more accurate prediction can be performed.

  The stock-power table 43 is a store ID in the store table 37 of the store 3 for the purpose of indicating the “stock-power ID” made up of numbers and the store 3 to which the stock information and power consumption information relate. “Store ID” in which “Inventory” is entered, “Inventory” in which inventory information in the store 3 is entered, “Power consumption” in which power consumption information in the store 3 is entered, and when the inventory and power consumption information are stored The field includes a “time zone” indicating whether the data is the data of the store, the data relationship from the store table 37 to the stock-power table 43 is performed, and the store-table 37 from the stock-power table 43 is stored. Information is referenced.

  In short, this stock-power table 43 is a storage device for each store 3, including the change over time, including the relationship between the inventory information at the store 3 as a kind of POS information and the power consumption at the store 3. 24 is stored. Then, the power consumption predicting means 33 reads the change with time from the inventory information and power consumption information at the store 3 acquired sequentially, and the change with time is taken as the change with time of past inventory information and power consumption information. By matching and referring to similar past data, it becomes possible to predict subsequent power consumption information in the store 3. At this time, if a correlation between inventory information and power consumption information in each store 3 is obtained by regression analysis or the like, more accurate prediction can be performed.

  The stay table 44 is filled with a “stay ID” consisting of numbers and a store ID in the store table 37 of the store 3 for the purpose of indicating which store 3 the estimated stay information and power consumption information relate to. Stores the “store ID” and the estimated stay information that is the stay time information of the user in the store 3 estimated from the number of items sold (the number of products purchased by the user at the store 3) and the estimated movement information. Fields include “estimated stay information”, “power consumption” for entering power consumption information at the store 3, and “time zone” indicating what time the estimated stay information and power consumption information are. The relationship of data from the store table 37 to the stay table 44 is performed, and information on the store table 37 is referred to from the stay table 44.

  In short, the stay table 44 stores the relevance between the estimated stay information and the power consumption at the store 3 in the storage device 24 for each store 3 including changes over time. Then, the power consumption predicting means 33 reads the temporal change from the estimated stay information and the power consumption information in the store 3 acquired sequentially, and uses the temporal change of the past estimated stay information and the power consumption information over time. By matching the change and referring to similar past data, it becomes possible to predict subsequent power consumption information in the store 3. At this time, if a correlation between estimated stay information and power consumption information in each store 3 is obtained by regression analysis or the like, more accurate prediction can be performed.

  The movement table 46 is filled with a “movement ID” consisting of numbers and a store ID in the store table 37 of the store 3 for the purpose of indicating which store 3 the estimated movement information and power consumption information relate to. “Store ID”, “Estimated stay information” for storing the estimated movement information described above, “Power consumption” for entering the power consumption information in the store 3, and what data is the estimated movement information and the power consumption information A “time zone” indicating whether the table is a field, the data relationship from the store table 37 to the move table 46 is performed, and the information of the store table 37 is referred to from the move table 46. .

  In short, the movement table 46 stores the relationship between the estimated movement information described above and the power consumption at the store 3 in the storage device 24 for each store 3 including changes over time. Then, the power consumption predicting means 33 reads the temporal change from the estimated movement information and the power consumption information in the store 3 acquired sequentially, and uses the temporal change of the past estimated movement information and the power consumption information over time. By matching the change and referring to similar past data, it becomes possible to predict subsequent power consumption information in the store 3. At this time, if a correlation between estimated movement information and power consumption information in each store 3 is obtained by regression analysis or the like, more accurate prediction can be performed.

  In addition, although the estimation process of the above-mentioned estimated movement state and estimated stay information may be performed when data is acquired by the acquisition unit 23, it may be performed in advance on the management terminal 4 side.

  FIGS. 5A to 5D are explanatory diagrams of data tables that constitute the customer number database. The number-of-customers database 32C shows the relationship between the sales-customer table 47 (see FIG. 3A) showing the relationship between the sales information at the store 3 and the visitor number information, and the relationship between the inventory information at the store 3 and the visitor-number information. Coupon table 49 showing the relationship between the stock-shower table 48 shown (see FIG. 5B), the number of coupons issued, and information on the number of visitors who visited the store 3 that can use the coupons during the usage period. (See (C) in the figure), and a sale item table 51 (see (D) in the same figure) showing the relationship between the number of sale items in stock and the number of visitors to the store 3 that sold the sale items during the sales period. ).

  The sales-customer table 47 is a store ID in the store table 37 of the store 3 for the purpose of indicating to which store 3 the sales information and the number-of-visits information are related to “sales-customer ID” consisting of numbers. "Store ID", "Sales" for entering sales information at the store 3, "Number of visitors" for entering the number of visitors at the store 3, and the sales information and visitor number information The field has a “time zone” indicating whether or not the data is from the store table 37 to the sales-customer table 47, and the sales-customer table 47 to the store table 37. Information is referenced.

  In short, this sales-visitor table 47 is a storage device for each store 3 including the temporal change in the relationship between the sales information at the store 3 as a kind of POS information and the number of visitors at the store 3. 24 is stored. Then, the visitor number predicting means 34 reads the change with time from the sales information and the visitor number information at the store 3 acquired sequentially, and the change with time is regarded as the change with time of past sales information and visitor number information. By matching and referring to similar past data, it is possible to predict the number of visitors after that in the store 3. At this time, if a correlation between sales information and visitor number information in each store 3 is obtained by regression analysis or the like, a more accurate prediction can be made.

  The stock-customer table 48 is a store ID in the store table 37 of the store 3 for the purpose of indicating to which store 3 the “stock-customer ID” composed of numbers and the inventory information and the number-of-customers information are related. "Store ID", "Inventory" for entering inventory information at the store 3, "Number of visitors" for entering visitor number information at the store 3, and the inventory and visitor number information The field has a “time zone” indicating whether the data is from the store table 37 to the stock-customer table 48, and the stock-customer table 48 to the store table 37. Information is referenced.

  In short, this inventory-customer table 48 stores the relationship between the inventory information at the store 3 which is a kind of POS information and the visitor number information at the store 3, for each store 3, including changes over time. It is stored in the device 24. Then, the visitor number predicting means 34 reads the change over time from the inventory information and the visitor number information at the store 3 acquired sequentially, and the change over time is taken as the change over time of the past inventory information and the visitor number information. By matching and referring to similar past data, it is possible to predict the number of visitors after that in the store 3. At this time, if the correlation between the inventory information and the visitor number information in each store 3 is obtained by regression analysis or the like, a more accurate prediction can be performed.

  The coupon table 49 has a “coupon ID” composed of numbers, a “coupon name” for entering the name of the coupon, and a store table 37 of the store 3 for the purpose of indicating the store 3 that can use the coupon ticket. “Store ID” in which the store ID is entered, “Number of ticket issuance” in which the number of coupons issued is entered, “Number of visitors” in which the number of visitors who have visited the store 3 during the coupon usage period are stored, The “use period” for storing the use period of the coupon is provided as a field, and the data relationship from the store table 37 to the coupon table 49 is performed. Referenced.

  In short, the coupon table 49 stores, in the storage device 24, the relevance between the coupon ticket issuance number information and the visitor number information at the store 3 for each store 3. And the visitor number prediction means 34 refers to the relationship between the past coupon ticket issue number information and the visitor number information from the issued ticket number information, and refers to similar past data, It becomes possible to predict the number of visitors after that in the store 3. At this time, if the correlation between the coupon ticket issuance number information and the visitor number information in each store 3 is obtained by regression analysis or the like, a more accurate prediction can be made.

  The sale item table 51 is a store table of the store 3 for the purpose of indicating the “sale item ID” consisting of numbers, the “sale item name” for entering the name of the sale item, and the store 3 selling the sale item. “Store ID” in which the store ID at 37 is entered, “Inventory number” in which the number of stocks of the sale item in the store 3 is entered, and the number of visitors who visited the store 3 during sale of the sale item are stored. The field includes “number of visitors” and “time zone” indicating what period the inventory number information and visitor number information indicate, and the data from the store table 37 to the sale item table 51 is stored. A relationship is established, and information on the store table 37 is referenced from the sale item table 51.

  In short, the sale item table 51 stores the relevance between the sale item inventory number information and the visitor number information at the store 3 in the storage device 24 for each store 3 including changes over time. It is. Then, the visitor number predicting means 34 reads the change over time from the inventory information and the visitor number information of the sale items at the store 3 that are sequentially acquired, and the change over time is used as the past sale item inventory information and the visitor number information. It is possible to predict the information on the number of visitors thereafter in the store 3 by referring to similar past data by matching with the change with time. At this time, if the correlation between the sale item inventory information and the visitor number information in each store 3 is obtained by regression analysis or the like, a more accurate prediction can be made.

  FIGS. 6A to 6C are explanatory diagrams of data tables constituting the equipment database, respectively. The equipment database 32D includes an electrical equipment table 52 (see (A) in the figure) that stores various types of information on electrical equipment, and an equipment table 53 (see (B) in the figure) that stores the store 3 in which the electrical equipment is installed. And a power consumption table 54 (see FIG. 1C) in which the power consumption of the electrical equipment installed in the store 3 is stored.

  The electric device table 52 includes an “electric device ID” composed of numbers, an “electric device name” that stores the name of the electric device, an “electric device name” that stores the name of the electric device, and a new product of the electric device. “Price” in which the price at the storage device is stored, “work cost” in which the installation fee and the removal fee of the electric device are stored, and “repair cost” in which the repair cost necessary for repairing the electric device is stored And “standard power consumption” for storing the standard power consumption of a new product and “improved power consumption” for writing the power consumption improved by the repair.

  The equipment table 53 is installed with “equipment ID” consisting of numbers and “store ID” in which the shop ID in the shop table 37 of the shop 3 is written for the purpose of indicating the shop 3 where the electrical equipment is installed. For the purpose of indicating the electrical equipment that is present, an “electric equipment ID” in which the electrical equipment ID is written in the electrical equipment table 52 of the electrical equipment, an “installation date” that stores the installation date of the electrical equipment, and the electrical equipment The field has a “repair date” in which the repair date is stored as a field, and data is transferred from the electrical equipment table 52 and the store table 37 to the equipment table 53, and the equipment table is stored. 53, the information of the electric equipment table 52 and the store table 37 is referred to.

  The power consumption table 54 is a facility ID in the facility table 53 of the facility for the purpose of indicating “power consumption ID” consisting of numbers and facility information which is information about which electrical device is installed in which store 3. “Facility ID”, “Power consumption” for storing the power consumption of the electrical equipment related to the facility, and “Time zone” for writing what period the power consumption is for, As a field, the data relationship from the equipment table 53 to the power consumption table 54 is performed, and the information of the equipment table 53 is referred to from the power consumption table 54.

  By using this power consumption table 54, the power consumption at each store 3 and the power consumption of each electrical device 6, 7, 8, 9, 11 installed at each store 3 change over time. Can also be acquired. For this reason, for each store 3, the occupancy ratio, which is the ratio of the power consumption of each electrical device 6, 7, 8, 9, 11 to the total power consumption of the store 3, or each electrical device 6, 7, 8 , 9, 11 can also be used to know the operating rate, which is the ratio of the operating hours shown in the total time, and by using these pieces of information, the above-described investment effect predicting means 36 can be used for the electrical devices 6, 7, 8, Expect the investment effect of 9, 11 replacement, repair and expansion.

  FIG. 7A is a flowchart of the power consumption predicting means, and FIG. 7B is a notification screen that graphically displays changes with time of power consumption in the store predicted by the power consumption predicting means. The power consumption prediction unit 33 first proceeds to step S1. In step S1, the power consumption in the store 3 is obtained from the sales information and power consumption information in the store 3 that are sequentially acquired based on the information in the sales-power table 42 stored in the storage device 24 by the method described above. And proceed to step S2.

  In step S2, the power consumption in the store 3 is obtained from the inventory information and power consumption information in the store 3 that are sequentially acquired based on the information in the inventory-power table 42 stored in the storage device 24 by the above-described method. Is advanced to step S3.

  In step S3, the power consumption in the store 3 is obtained from the estimated stay information and power consumption information in the store 3 that are sequentially acquired based on the information in the stay table 44 stored in the storage device 24 by the method described above. Predict and proceed to step S4.

  In step S4, the power consumption in the store 3 is obtained from the estimated movement information and power consumption information in the store 3 that are sequentially acquired based on the information in the movement table 46 stored in the storage device 24 by the method described above. Predict and proceed to step S5.

  In step S5, the temporal change in power consumption in the store 3 predicted in steps S1 to S4 is comprehensively evaluated, and the power consumption in the store 3 is predicted including the subsequent temporal change. Specifically, it is possible to perform an overall evaluation by calculating an average of the power consumption values predicted for each, or to perform an overall evaluation by weighting and adding the power consumption values predicted for each. May be.

  Thus, the predicted future power consumption information in the store is displayed as a power consumption prediction screen 56 on the display of the management server 3 or the management terminal 4 as a graph. The power consumption prediction screen 56 has a store display field 56a for displaying the store name of the store 3 and a graph display field 56b for displaying a power consumption prediction graph.

  The power consumption prediction graph displays the power consumption at the store 3 from a time point that is a predetermined time later than the present time to a future time point that has passed a predetermined time from the current time, divided into an actual measurement value and an expected value. By the way, in the graph display field 56b, the actual measurement value is indicated by a solid line, the expected value is indicated by a virtual line, and if the power consumption further increases, there is a possibility that the fee will increase due to a contract with the power company, A threshold value, which is a power consumption value that may cause trouble even when each electrical appliance 6, 7, 8, 9, 11 is smoothly operated, is also displayed.

  FIG. 8A is a flowchart of the visitor number predicting means, and FIG. 8B is a notification screen that graphically displays changes with time of the visitor number in the store predicted by the visitor number predicting means. The visitor number predicting means 34 first proceeds to step S11. In step S11, the number of customers at the store 3 is obtained from the sales information and number-of-visits information at the store 3 that are sequentially acquired based on the information in the sales-customer table 47 stored in the storage device 24 by the method described above. Is advanced to step S12.

  In step S12, based on the information in the inventory-customer table 48 stored in the storage device 24 by the method described above, the number of customers at the store 3 is obtained from the inventory information and the number-of-customers information at the store 3 that are sequentially acquired. Is advanced to step S13.

  In step S13, the number of customers at the store 3 is predicted from the number of coupon tickets that can be used at the store 3 based on the information in the coupon table 49 stored in the storage device 24 by the method described above. Proceed to S4.

  In step S14, the number of sale items in the store 3 and the number of customers in the store 3 that are sequentially acquired are predicted based on the information in the sale item table 51 stored in the storage device 24 by the above-described method. The process proceeds to step S15.

  In step S15, the temporal change in the number of visitors at the store 3 predicted in steps S11 to S14 is comprehensively evaluated, and the number of visitors at the store 3 is predicted including the subsequent temporal change. Specifically, the average of the number of visitors expected by each may be calculated and comprehensive evaluation may be performed, or the total evaluation may be performed by weighting and adding the number of visitors predicted by each. .

  In this way, the predicted future visitor number information at the store is displayed in a graph as a visitor number power prediction screen 57 on the display of the management server 3 or the management terminal 4. The visitor number prediction screen 57 has a store display field 57a for displaying the store name of the store 3 and a graph display field 57b for displaying a visitor number prediction graph.

  The visitor number prediction graph displays the number of visitors at the store 3 from a time point that is a predetermined time later than the present time to a future time point that has passed a predetermined time from the present time, divided into an actual measurement value and an expected value. By the way, in the graph display field 57b, the actual measurement value is indicated by a solid line and the predicted value is indicated by a virtual line, and when the number of visitors further increases, a threshold value indicating the number of visitors that need to switch various processes is also displayed. Has been.

  9A is a flowchart of a main routine of the investment effect prediction means, FIG. 9B is a flowchart of a subroutine for predicting a replacement effect, FIG. 9C is a flowchart of a subroutine for predicting a repair effect, and (D ) Is a flowchart of a subroutine for predicting the expansion effect. The investment effect prediction means 36 executes the exchange effect subroutine for predicting the economic effect when the electrical devices 6, 7, 8, 9, 11 in the store 3 are replaced, and then continues to the electrical device 6 in the store 3. , 7, 8, 9 and 11 are executed, a repair effect subroutine for predicting the economic effect is executed, and then the electrical equipment 6, 7, 8, 9 and 11 in the store 3 is expanded. The expansion effect subroutine for predicting the effect is executed, and the process is terminated.

  In the subroutine for predicting the exchange effect, first, the process proceeds to step S21. In step S21, electric power is evaluated when the target electric devices 6, 7, 8, 9, and 11 are replaced, and the process proceeds to step S22. Specifically, this step S21 is targeted by referring to the electrical equipment table 52, the equipment table 53, and the power consumption table 54, and by sequentially acquiring the power consumption in the store 3 from the power consumption acquisition means 29. Power consumption before replacement, which is the power consumption of the entire store 3 before replacement, and power consumption after replacement, which is the power consumption of the entire store 3 after replacement, when replacing the electrical devices 6, 7, 8, 9, and 11 And evaluate.

  Incidentally, the electric devices 6, 7, 8, 9, and 11 are usually deteriorated with time and the power consumption increases as time passes. Therefore, the electric devices 6, 7, About 8,911, this evaluation becomes high.

  In step S22, by referring to the electrical equipment table 52 and the equipment table 53, the cost required when the electrical equipments 6, 7, 8, 9, and 11 to be exchanged are calculated, and the process proceeds to step S23. In this step S22, specifically, the electric equipment 6, 7, 8, 9, 11 itself is added to the price of the electric equipment 6, 7, 8, 9, 11 after removing the electric equipment 6, 7, 8, 9, 11 itself. .

  In step S23, the electrical device table 52, the facility table 53, and the power consumption table 54 are referred to, and the power consumption in the store 3 is sequentially acquired from the power consumption acquisition unit 29, so that the target electrical device 6, After the occupancy rate and the operation rate in the store of 7, 8, 9, and 11 are calculated and evaluated, the process proceeds to step S24. In step S23, the higher the occupation rate and the operation rate, the more important the role in the store 3 is. Therefore, the higher the occupation rate and the operation rate, the higher the electrical devices 6, 7, 8, 9, and 11 are. The process is performed so that the evaluation is high.

  In step S24, the evaluation in step S21, the evaluation in step S22, and the evaluation in step S23 are comprehensively evaluated, the comprehensive evaluation value is converted into a monetary amount, and the converted monetary amount is estimated as an exchange investment effect prediction. As a value, the process is terminated.

  In the subroutine for predicting the repair effect, first, the process proceeds to step S31. In step S31, electric power is evaluated when the target electric devices 6, 7, 8, 9, and 11 are repaired, and the process proceeds to step S32. Specifically, this step S31 is targeted by referring to the electrical equipment table 52, the equipment table 53, and the power consumption table 54, and sequentially acquiring the power consumption in the store 3 from the power consumption acquisition means 29. Power consumption before repair, which is the power consumption of the entire store 3 before repair, and power consumption after replacement, which is the power consumption of the entire store 3 after repair, when repairing the electrical devices 6, 7, 8, 9, 11 And evaluate.

  In step S32, by referring to the electric device table 52 and the equipment table 53, the cost required for repairing the target electric devices 6, 7, 8, 9, and 11 is calculated, and the process proceeds to step S33. By the way, in general, the cost for repair is considerably lower than the cost for replacement.

  In step S33, the electric device table 52, the equipment table 53, and the power consumption table 54 are referred to, and the power consumption in the store 3 is sequentially acquired from the power consumption acquisition unit 29, thereby making the target electric device 6, After the occupancy rate and the operation rate in the store of 7, 8, 9, and 11 are calculated and evaluated, the process proceeds to step S24.

  In step S34, the evaluation in step S31, the evaluation in step S32, and the evaluation in step S33 are comprehensively evaluated, the total evaluation value is converted into a monetary amount, and the converted monetary value is estimated as a repair investment effect prediction. As a value, the process is terminated.

  In the subroutine for predicting the expansion effect, first, the process proceeds to step S41. In step S41, electric power is evaluated when the target electric devices 6, 7, 8, 9, and 11 are added, and the process proceeds to step S42. Specifically, this step S41 is targeted by referring to the electrical equipment table 52, the equipment table 53, and the power consumption table 54, and sequentially acquiring the power consumption in the store 3 from the power consumption acquisition means 29. Power consumption before repair, which is the power consumption of the entire store 3 before the expansion, and power consumption after replacement, which is the power consumption of the entire store 3 after the expansion, when adding the electrical devices 6, 7, 8, 9, 11 And evaluate.

  In step S42, by referring to the electric device table 52 and the equipment table 53, the cost required when the target electric devices 6, 7, 8, 9, and 11 are added is calculated, and the process proceeds to step S43. Specifically, the labor for attaching the electric devices 6, 7, 8, 9, 11 is added to the price of the electric devices 6, 7, 8, 9, 11 themselves.

  In step S43, the electric device table 52, the facility table 53, and the power consumption table 54 are referred to, and the power consumption in the store 3 is sequentially acquired from the power consumption acquisition means 29, thereby making the target electric device 6, After the occupancy rate and the operation rate in the store of 7, 8, 9, and 11 are calculated and evaluated, the process proceeds to step S44.

  It should be noted that the occupancy rate and the operation rate when evaluating the extension effect are performed with a higher specific gravity than in the case of the effects and repairs described above. This is because if the occupation rate and the operation rate are high, it is considered necessary to increase the number of electrical devices 6, 7, 8, 9, and 11.

  In step S44, the evaluation in step S41, the evaluation in step S42, and the evaluation in step S43 are comprehensively evaluated, the comprehensive evaluation value is converted into an amount of money, and the converted amount is calculated as an expected investment effect of expansion investment. As a value, the process is terminated.

  FIG. 10 is a flowchart of the main routine of the management means. The management means 27 has a power management subroutine for managing power of each store 4 and an electrical equipment management subroutine for managing electrical equipment of each store 4. Incidentally, the main routine of the management means 27 is executed individually for each predetermined time for each store 3.

  FIG. 11 is a flowchart of a power management subroutine performed by the management unit. In the power management subroutine, the management unit 27 first proceeds to step S51. In step S51, the power consumption processing by the power consumption means 33 is performed on the target store 3 as shown in FIG. 7, and the process proceeds to step S52. In step S52, a visitor number prediction process by the visitor number prediction means 34 is performed on the target store 3 as shown in FIG. 8, and the process proceeds to step S53. In step S53, power management control is performed based on the predicted power consumption and the expected number of customers in the store 3 obtained in steps S51 and S52, and the process ends.

  The power management control in the store 3 is roughly divided into two types: action instruction to the store clerk of the store 3 and direct operation control of the electrical equipment.

  FIGS. 12A to 12C and FIGS. 13A to 13C are explanatory diagrams for explaining the contents of the action instruction screen in power management. The action instruction to the clerk of the store 3 is performed by outputting voice data from the management terminal 4 of the store 3 or by displaying the action instruction screen 58 on the management server 1 and the management terminal 4. The action instruction screen 58 includes a store display field 58a that displays whether the instruction is for the store 3, and an action instruction content field 58b that describes the action instruction content.

  The management means 27 selects the electric devices 6, 7, 8, 9, 11 to be operated and the electric devices 6, 7, 8, 9, 11 to be stopped based on the power consumption prediction in the store 3, This is displayed on the action instruction screen 58 (see FIGS. 12A and 12B), or the operation is directly controlled via the driver unit 13. The action instruction and the operation control may be performed at the same time, but when the direct operation control is difficult, the action instruction may be performed. In this way, when the power consumption is likely to increase, the electric devices 6, 7, 8, 9, and 11 that are likely to stop operating may be searched for, or the role may be replaced with one that consumes less power. Also good.

  In addition, when the number of visitors is expected to increase in summer, etc., even if the aerial equipment 7 is stopped, it is necessary to maintain the room temperature in the store 3 appropriately. However, you may output the action instruction | indication screen 58 so that opening of a window etc. may be instruct | indicated (refer FIG.12 (C)). By the way, if the number of visitors exceeds or is likely to exceed the threshold, the operation of the air conditioning equipment 7 can not be stopped so that the user can spend comfortably in the store 3, and the operation can be stopped elsewhere. Search for new electrical devices 6, 7, 8, 9, and 11.

  In addition, it is necessary to keep the room temperature in the store 3 appropriate in accordance with the expected increase in the number of visitors and to complete the cooking of food. Is determined on the basis of the expected number of visitors and is output to the action instruction screen 58 (FIGS. 13A and 13B), or the operation is directly controlled via the management terminal 4 of the store 3 May be.

  Furthermore, when an increase in power consumption of the store 3 is expected (when the threshold value is exceeded or likely to be exceeded), the electrical devices 6, 7, 8, 9, and 11 that are likely to stop operating in the store 3 are predicted. If it is not clear from the number of visitors, etc., an action instruction for saving power may be given to the clerk of the store 3 via the action instruction screen 58 (see FIG. 13C).

  FIG. 14 is a flowchart of an electric equipment management subroutine performed by the management means. In the electric equipment management subroutine, the management means 27 first proceeds to step S61. In step S61, it is checked whether or not there is an electrical device 6, 7, 8, 9, or 11 to be replaced, repaired, or added at the target store 3. By the way, this judgment may be based on the installation date or repair date of the electric devices 6, 7, 8, 9, 11 or may use the occupation rate or the operation rate. Even if the evaluation is completed, it is determined that it is not eligible.

  In step S61, if there are no electrical devices 6, 7, 8, 9, and 11 to be replaced, repaired, or added, the process is terminated. If there is, the process proceeds to step S62. In step S62, the electrical devices 6, 7, 8, 9, and 11 to be replaced, repaired, or added are specified, and the process proceeds to step S63. In step S63, the investment effect prediction means 36 performs the investment effect prediction process shown in FIG. 9, and the replacement investment effect expected value, the repair investment effect expected value, and the expansion investment effect of the electric devices 6, 7, 8, 9, and 11. An expected value is obtained and the process proceeds to step S64.

  In step S64, the most favorable value is selected from the predicted exchange investment effect value, the repair investment effect expected value, and the expansion investment effect expected value, and the process proceeds to step S65. In step S65, it is determined whether or not the exchange investment effect expected value, repair investment effect expected value or expansion investment effect expected value selected as the best value in the previous step exceeds a predetermined scheduled value k. If so, the process proceeds to step S66. If not, the process returns to step S61.

  FIGS. 15A to 15C are explanatory diagrams for explaining the contents of the action instruction screen in the electric device management. As shown in the figure, in step S66, the action selected in step S64 (any action for replacement, repair, or expansion) is performed on the electric devices 6, 7, 8, 9, 11 as described above. The action instruction screen 58 is displayed on the display of the management terminal 4 or the management server 1, and the process returns to step S61.

  In this way, the management server 1 automatically selects the electrical devices 6, 7, 8, 9, and 11 to be effective, repaired, and added.

1 Management server (power management server, electrical equipment management server)
2 Network (Internet, WAN)
3 stores 4 management terminals 6 register facilities (electrical equipment, cash register)
7 Air conditioning equipment (electric equipment, air conditioner)
8 Refrigeration / refrigeration equipment (electrical equipment)
9 Cooking equipment (electric equipment)
11 Lighting equipment (electric equipment)
12 Power detection unit (power detection means)
13 Driver unit (operating means)
14 Communication means 16 Storage device (HDD, SSD)
DESCRIPTION OF SYMBOLS 17 Information management means 18 Operation control means 19 Notification means 21A POS data 21B Power consumption data 21D Equipment data 22 Communication means 23 Acquisition part 24 Storage device 26 Prediction part 27 Management means 28 Notification means 29 Power consumption acquisition means 31 Information acquisition means 32A POS Database 32B Power consumption database 32C Customer number database 32D Equipment database 33 Power consumption prediction means 34 Visitor number prediction means 36 Investment effect prediction means 37 Store table 38 Product table 39 Display table 41 Sales table 42 Sales-power table 43 Inventory-power table 44 Stay table 46 Moving table 47 Sales-customer table 48 Inventory-customer table 49 Coupon table 51 Sale product table 52 Electrical equipment table 53 Equipment table 54 Power consumption table 56 Power consumption prediction screen 56a Store display field 56b Graph display field 57 Visitor number prediction screen 57a Store display field 57b Graph display field 58 Action instruction screen 58a Store display field 58b Action instruction content field

Claims (12)

Power consumption acquisition means for acquiring power consumption of a store or power consumption of an electrical device installed in the store, and notification for the purpose of reducing power consumption based on the power consumption information acquired through the power consumption acquisition means, or A power management server comprising a management means for controlling operation of electrical equipment,
Information acquisition means for acquiring POS information at the store;
A storage device that stores POS information and power consumption information in association with each other;
Based on the relationship between past POS information and power consumption information stored in the storage device, power consumption prediction means is provided for predicting subsequent power consumption in the store from POS information or power consumption information acquired sequentially. A power management server. Sales information, which is a type of POS information for each store, is stored in a storage device in association with power consumption information,
The power consumption prediction means predicts the subsequent power consumption in the store from the sales information or the power consumption information acquired sequentially based on the relationship between the past sales information stored in the storage device and the power consumption information. The power management server according to claim 1. Inventory information that is a kind of POS information of each store is stored in a storage device in association with power consumption information,
The power consumption prediction means predicts subsequent power consumption at the store from sequentially acquired inventory information or power consumption information based on the relationship between the past inventory information stored in the storage device and the power consumption information. The power management server according to claim 1 or 2. The products handled in each store are stored in a storage device in association with the display place in the store,
Estimating the distance or route that the user moved in the store from the display location of each product purchased by the user,
The estimated estimated movement information is stored in the storage device in association with the power consumption information,
The power consumption prediction means estimates subsequent power consumption at a store from estimated movement information or power consumption information sequentially estimated based on a relationship between past estimated movement information and power consumption information. Item 4. The power management server according to any one of Items 3. Store the number of products purchased by each user in the storage device,
Estimate how long each user stays in the store from the number of items purchased by each user,
Storing the estimated stay information in the storage device in association with the power consumption information,
The power consumption prediction means estimates the subsequent power consumption at the store from the estimated stay information or the power consumption information sequentially estimated based on the relationship between the past estimated stay information and the power consumption information. Item 5. The power management server according to any one of Items 4. The power consumption acquisition means acquires the power consumption information of each store from the electrical equipment of each store or each store via a network,
The power management server according to any one of claims 1 to 5, wherein the information acquisition unit acquires the POS information of each store via a network. Power consumption acquisition processing for acquiring power consumption of a store or power consumption of electrical equipment installed in the store, and notification for the purpose of reducing power consumption based on the power consumption information acquired through the power consumption acquisition processing, or A power management program having a management process for controlling operation of the electrical equipment,
Information acquisition means for acquiring POS information at the store;
A computer having a storage device for storing POS information and power consumption information in association with each other;
Based on the relationship between past POS information and power consumption information stored in the storage device, to execute power consumption prediction processing for predicting subsequent power consumption in the store from POS information or power consumption information acquired sequentially Power management program. Sales information, which is a type of POS information for each store, is stored in a storage device in association with power consumption information,
In the power consumption prediction process, based on the relationship between past sales information stored in the storage device and the power consumption information, the subsequent power consumption at the store is predicted from the sequentially acquired sales information or power consumption information. The power management program according to claim 7 for executing processing. Inventory information that is a kind of POS information of each store is stored in a storage device in association with power consumption information,
In the power consumption prediction process, based on the relationship between the past inventory information stored in the storage device and the power consumption information, the subsequent power consumption in the store is predicted from the sequentially acquired inventory information or power consumption information. The power management program according to claim 7 or claim 8 for executing processing. The products handled in each store are stored in a storage device in association with the display place in the store,
Estimating the distance or route that the user moved in the store from the display location of each product purchased by the user,
The estimated estimated movement information is stored in the storage device in association with the power consumption information,
In the power consumption prediction process, in order to execute the process of estimating the subsequent power consumption in the store from the estimated movement information or the power consumption information sequentially estimated based on the relationship between the past estimated movement information and the power consumption information. The power management program according to any one of claims 7 to 9. Store the number of products purchased by each user in the storage device,
Estimate how long each user stays in the store from the number of items purchased by each user,
Storing the estimated stay information in the storage device in association with the power consumption information,
In the power consumption prediction process, in order to execute the process of estimating the subsequent power consumption in the store from the estimated stay information or the power consumption information that is sequentially estimated based on the relationship between the past estimated stay information and the power consumption information. The power management program according to any one of claims 7 to 10. The power consumption acquisition process acquires the power consumption information of each store from the electrical equipment of each store or each store via the network,
The power management program according to any one of claims 7 to 11, wherein the information acquisition process executes a process of acquiring POS information of each store via a network.

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