JP2017120519A - Device for providing amount of used average power, method for providing amount of used average power, and program for providing amount of used average power - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide information for comparing consumers having similar attribute for the amount of used power.SOLUTION: A device for providing the amount of used average power includes: acquisition means for acquiring attribute information for indicating an attribute of a consumer who receives supply of power; feature amount calculation means for calculating the amount of feature of a consumer as the amount of feature of attribute information of a consumer, based on attribute information acquired by the acquisition means and the amount of feature predetermined according to the attribute; attribute classification means for classifying a consumer to a predetermined cluster, based on the amount of feature of the consumer; means for acquiring the amount of used power for acquiring the amount of power used by a consumer; means for calculating the amount of average power for calculating an average for each cluster of the amount of power used by a consumer classified to a cluster, based on the cluster and the amount of power used by the consumer; and means for providing the amount of average used power for providing a consumer with information for indicating the average of power used by the consumer calculated for each cluster.SELECTED DRAWING: Figure 1

Description

  The present invention relates to an average power usage providing device, an average power usage providing method, and an average power usage providing program.

  2. Description of the Related Art Conventionally, a technique for calculating the amount of power used by a consumer who receives power supply and providing information indicating the calculated amount of power to the consumer is known (for example, Patent Document 1).

JP 2002-117183 A

In recent years, with the improvement of living standards, the amount of power used by consumers receiving power supply has been steadily increasing. Accordingly, consumers are required to promote energy saving from the viewpoint of depletion of energy resources and the progress of global warming.
Examples of means for energy conservation that can be implemented by consumers include table switches with switches for the purpose of reducing the standby power consumption by switching to a device to which energy conservation technology is applied to each device group used by the consumer. The use of midnight power and the use of midnight power to prevent concentration of power consumption are already known.

Patent Document 1 describes a technique for calculating information indicating a breakdown of the amount of electric power used by a consumer by supplying information such as product names and product specifications of each device group used by the consumer. Yes.
Here, there are cases where the customer wants to recognize whether or not the power is being used excessively.
In this case, by comparing the amount of power used by the consumer with the amount of power used by another customer different from the customer, the consumer recognizes whether or not the customer is using too much power. be able to.

However, if a simple comparison is made between the amount of power used by a customer and the amount of power used by another customer different from the customer, it recognizes whether the customer is using too much power. May be difficult to do.
For example, in a large family with 10 or more family members and a consumer A who has a contract current value of 50A and a consumer B who lives alone and has a contract current value of 10A, the amount of power used May be different. In this case, the customer A and the customer B may not be an appropriate comparison target.

Here, the consumer compares the amount of power used by other consumers with similar attributes such as the number of family members and the contract of power with the consumer, and the amount of power used by the consumer. In some cases, it is possible to recognize whether or not power is being used excessively.
In other words, in order to recognize whether or not the consumer is using too much electric power, other consumers with similar attributes such as the customer and the number of family members and the contract form of power use the consumer. Information indicating the amount of power may be provided.
However, the conventional technology as disclosed in Patent Document 1 has a problem that it is impossible to provide information for comparing consumers with similar attributes for the amount of power used.

  One aspect of the present invention is an acquisition unit that acquires attribute information indicating an attribute of a consumer who receives power supply, the attribute information acquired by the acquisition unit, and a feature amount that is predetermined according to the attribute, Based on the feature amount calculating means for calculating a consumer feature amount that is a feature amount of the attribute information of the consumer, and based on the consumer feature amount calculated by the feature amount calculating means. Attribute classification means for classifying the consumers into clusters, power usage amount acquisition means for acquiring power consumption amounts used by the consumers in the past, clusters in which the attribute classification means classifies the consumers, and Based on the power consumption used in the past by the consumer acquired by the power usage acquisition means, the average of the power usage used in the past by the consumer classified into the cluster is calculated for each cluster. The class An average power usage calculating unit that calculates for each cluster, and information indicating an average of the usage of power used by the customer in the past calculated by the average power usage calculating unit for each cluster is provided to the consumer. An average power usage providing device comprising means for providing average power usage.

  Moreover, in the average power usage providing device of one aspect of the present invention, lower limit calculation means for dynamically calculating the lower limit value of the number of consumers classified into the cluster at the timing of classifying the consumers, Based on the lower limit value calculated by the lower limit calculation means and the number of consumers classified into the cluster, a minority cluster extraction means for extracting a cluster having the number of consumers below the lower limit value as an inappropriate cluster. And further comprising.

  Further, in the average power usage providing device according to one aspect of the present invention, the inappropriate cluster extracted by the minority cluster extraction unit may be any one or more of the consumer feature quantities of the consumer belonging to the inappropriate cluster. A minority cluster merging means for merging the unsuitable cluster with another cluster other than the unsuitable cluster based on the consumer feature amount of which the customer feature amount is changed to an arbitrary value. The usage amount providing means includes, for the consumers classified into the other clusters merged by the minority cluster merging means, an average of the amount of power used in the past by the consumers classified into the other clusters. I will provide a.

  Further, in the average power usage providing device of one aspect of the present invention, the lower limit calculating means calculates a number obtained by multiplying the total number of consumers receiving power supply by a predetermined ratio as the lower limit value. .

  Further, in the average power usage providing device according to one aspect of the present invention, a cluster in which the variance of the power usage used by the consumer classified as the cluster in the past is larger than a predetermined reference value is extracted as an inappropriate cluster. And a distributed cluster extracting means.

  Further, in the average power consumption providing device of one aspect of the present invention, a consumer extracting unit that randomly extracts the consumers based on a predetermined number or ratio, and the demand extracted by the consumer extracting unit A total average power usage calculating means for obtaining an average of the power consumption used by the house in the past as an overall average power usage; and the average power usage providing means is classified into the inappropriate cluster. The consumer is provided with the overall average power usage.

  Further, according to one aspect of the present invention, an acquisition step of acquiring attribute information indicating an attribute of a consumer who receives power supply, the attribute information acquired by the acquisition step, and a characteristic predetermined according to the attribute A feature amount calculating step for calculating a consumer feature amount that is a feature amount of the attribute information of the consumer based on the amount, and a predetermined amount based on the consumer feature amount calculated by the feature amount calculating step. An attribute classification step for classifying the consumer into a given cluster, a power usage acquisition step for acquiring a usage amount of power used by the consumer in the past, and a cluster in which the attribute classification step classifies the consumer. , Based on the power consumption used in the past by the consumer acquired in the power usage acquisition step, the usage of power used in the past by the consumer classified into the cluster. An average power usage calculating step for calculating the average of the amount for each cluster for each cluster, and an average of the power usage used by the customer in the past calculated by the average power usage calculating step for each cluster. An average power usage providing method comprising providing an average power usage to provide information to the consumer.

  Further, according to one embodiment of the present invention, an acquisition step of acquiring attribute information indicating an attribute of a consumer receiving power supply to the computer, the attribute information acquired by the acquisition step, and a predetermined value according to the attribute A feature quantity calculating step for calculating a consumer feature quantity that is a feature quantity of the attribute information of the consumer based on the feature quantity obtained, and based on the consumer feature quantity calculated by the feature quantity calculating step An attribute classification step of classifying the consumers into predetermined clusters, a power usage amount acquisition step of acquiring a usage amount of power used by the customer in the past, and the attribute classification step classifying the consumers. The consumer classified into the cluster in the past based on the cluster and the consumption of power used in the past by the consumer acquired in the power usage acquisition step. The average power usage calculation step for calculating the average of the power consumption used for each cluster for each cluster, and the power used in the past by the consumer calculated by the average power usage calculation step for each cluster An average power usage providing program for causing the consumer to execute an average power usage providing step of providing information indicating an average of the usage amount to the consumer.

  ADVANTAGE OF THE INVENTION According to this invention, the information for comparing the consumers with similar attributes about the used electric energy can be provided.

It is a schematic diagram which shows an example of the outline | summary of the average electric power usage amount provision apparatus of 1st Embodiment. It is a block diagram which shows an example of a structure of the average electric power usage amount provision apparatus of this embodiment. It is a flowchart which shows an example of operation | movement of the average electric power usage amount provision apparatus of this embodiment. It is a schematic diagram which shows an example of the attribute information of this embodiment. It is a 1st table | surface which shows an example of a response | compatibility with the attribute of this embodiment, and a feature-value. It is a 2nd table | surface which shows an example of a response | compatibility with the attribute of this embodiment, and a feature-value. It is a 3rd table | surface which shows an example of a response | compatibility with the attribute of this embodiment, and a feature-value. It is a 4th table | surface which shows an example of a response | compatibility with the attribute of this embodiment, and a feature-value. It is a 5th table | surface which shows an example of a response | compatibility with the attribute of this embodiment, and a feature-value. It is a 6th table | surface which shows an example of a response | compatibility with the attribute of this embodiment, and a feature-value. It is a table | surface which shows an example of the consumer feature-value of this embodiment. It is a table | surface which shows an example of the cluster information of this embodiment. It is a table | surface which shows an example of the attribute classification | category information of this embodiment. It is a table | surface which shows an example of the electric power usage-amount of this embodiment. It is a table | surface which shows an example of the average power usage-amount of this embodiment. It is a table | surface which shows an example of the average electric power usage amount provision information of this embodiment. It is a block diagram which shows an example of a structure of the average electric power usage amount provision apparatus of 2nd Embodiment. It is a table | surface which shows an example of the attribute classification | category information after the minority cluster merge means of this embodiment merged. It is a block diagram which shows an example of a structure of the average electric power usage amount provision apparatus of 3rd Embodiment.

[First Embodiment]
Hereinafter, embodiments of the present invention will be described with reference to the drawings. FIG. 1 is a schematic diagram illustrating an example of an overview of an average power usage providing device 1 according to the first embodiment.
As shown in FIG. 1, the average power usage providing device 1 and the customer CS are connected via a network N capable of transmitting and receiving information. In this example, as shown in FIG. 1, a customer CS 1, a customer CS 2, a customer CS 3, a customer CS 4, and an average power usage providing device 1 are connected via a network N. In the following description, when the customer CS1, the customer CS2, the customer CS3, and the customer CS4 are not particularly distinguished, they are collectively referred to as the customer CS.

The average power usage providing device 1 acquires attribute information ATTI, which is information including a plurality of attribute ATTs of the customer CS, from the customer CS via the network N.
The attribute ATT is a feature that indicates the lifestyle of the customer CS. The attribute information ATTI includes a plurality of attribute ATTs. Details of the attribute ATT included in the attribute information ATTI will be described later.
The average power usage amount providing device 1 calculates an average power usage amount APU corresponding to the attribute ATT of the customer CS based on the attribute information ATTI acquired from the customer CS. The average electric power consumption APU is a value indicating an average of electric energy in a certain month used by a certain customer CS and a customer CS having a similar attribute ATT. The average power usage amount providing device 1 supplies the calculated average power usage amount APU to the customer CS.

Hereinafter, the configuration of the average power consumption providing apparatus 1 will be described with reference to FIG. FIG. 2 is a configuration diagram illustrating an example of the configuration of the average power usage providing device 1 of the present embodiment.
As shown in FIG. 2, the average power usage providing device 1 includes a control unit 100 and a storage unit 500.
The storage unit 500 stores feature amount information FAI and cluster information CLI. The feature amount information FAI and the cluster information CLI will be described later.
The control unit 100 includes a CPU (Central Processing Unit), an acquisition unit 110, a feature amount calculation unit 120, an attribute classification unit 130, a power usage amount acquisition unit 140, and an average power usage amount calculation unit 150. The average power usage providing means 160 is provided as a functional unit.

Hereinafter, the operation of each unit included in the control unit 100 and the operation of the average power usage providing device 1 will be described with reference to FIG. FIG. 3 is a flowchart showing an example of the operation of the average power usage providing device 1 of the present embodiment.
As shown in FIG. 3, the acquisition unit 110 acquires attribute information ATTI from the customer CS via the network N (step S100).

The attribute information ATTI acquired by the acquisition unit 110 will be described below with reference to FIG. FIG. 4 is a schematic diagram illustrating an example of attribute information ATTI of the present embodiment.
The average power consumption providing apparatus 1 acquires attribute information ATTI including a plurality of attribute ATTs from the customer CS via the network N. In this example, the average power usage providing device 1 acquires the attribute information ATTI1 from the customer CS1. In addition, the average power consumption providing device 1 acquires the attribute information ATTI2 from the customer CS2. In addition, the average power consumption providing device 1 acquires the attribute information ATTI3 from the customer CS3. Moreover, the average power usage providing device 1 acquires the attribute information ATTI4 from the customer CS4. In the following description, the attribute information ATTI1, the attribute information ATTI2, the attribute information ATTI3, and the attribute information ATTI4 are collectively referred to as attribute information ATTI unless otherwise distinguished.
For example, the customer CS accesses a site for requesting input of attribute information ATTI via an Internet browser or the like. The customer CS inputs attribute information ATTI through the site. Thereby, the acquisition means 110 with which the control part 100 is provided acquires attribute information ATTI from the consumer CS via a network.

In the above description, the average power usage providing device 1 has described the case where the customer CS acquires the attribute information ATTI from the customer CS by inputting the attribute information ATTI via the site on the network. It is not limited to this.
The customer CS may supply the attribute information ATTI when contracting the use of power, and the average power usage providing apparatus 1 acquires the attribute information ATTI of the customer CS from a server or the like in which the attribute information ATTI is stored. May be.

  In the above description, the case where the average power usage providing apparatus 1 has four customer CSs that acquire the attribute information ATTI has been described, but the present invention is not limited thereto. The average power usage providing device 1 may acquire the attribute information ATTI from at least one customer CS, and may acquire the attribute information ATTI from five or more consumers CS.

  In this example, the attribute information ATTI includes six attribute ATTs. Specifically, the attribute information ATTI includes an attribute (attribute ATTa) indicating the number of family members of the customer CS. Further, the attribute information ATTI includes an attribute (attribute ATTb) indicating the form of the house where the customer CS resides. Further, the attribute information ATTI includes an attribute (attribute ATTc) indicating whether or not a solar panel is installed, which indicates whether or not a solar panel is installed in a house where the customer CS lives. Further, the attribute information ATTI includes an attribute (attribute ATTd) indicating the contract form of the amount of power that the customer CS contracts with the power supply company. In addition, the attribute information ATTI includes an attribute (attribute ATTe) indicating an ordinary day corresponding to a day on which the amount of power used by the customer CS is measured. Further, the attribute information ATTI includes an attribute (attribute ATTf) indicating a residential area where the customer CS resides.

  As shown in FIG. 4, the customer CS1 has three family members (attribute ATTa: three people). Further, the customer CS1 is a detached house (attribute ATTb: detached house). Moreover, the solar panel is installed in the house where consumer CS1 resides (attribute ATTc: Yes). Further, the customer CS1 has the contract form 10A of the metered light B (attribute ATTd: metered light B 10A). In addition, the customer CS1 has 26 daily days (attribute ATTe: 26 daily days). Further, the customer CS1 resides in Kanto (attribute ATTf: Kanto).

  Further, as shown in FIG. 4, the customer CS2 has five family members (attribute ATTa: 5 people). Further, the consumer CS2 is a housing complex in the housing form (attribute ATTb: housing complex). Moreover, the solar panel is not installed in the house where customer CS2 resides (attribute ATTc: none). In addition, the customer CS2 has a contract form of 30A of the metered lamp B (attribute ATTd: metered lamp B 30A). In addition, the customer CS2 has 15 daily days (attribute ATTe: 15 daily days). Further, the customer CS2 resides in Kanto (attribute ATTf: Kanto).

  Further, as shown in FIG. 4, the customer CS3 has three family members (attribute ATTa: three people). Further, the consumer CS3 is a multi-family house in the housing form (attribute ATTb: multi-family house). Moreover, the solar panel is installed in the house where consumer CS3 resides (attribute ATTc: Yes). Further, the customer CS3 has a contract form of 50A of the metered lamp B (attribute ATTd: metered lamp B 50A). In addition, the customer CS3 has 8 daily days (attribute ATTe: 8 daily days). Further, the customer CS3 resides in Kanto (attribute ATTf: Kanto).

  Further, as shown in FIG. 4, the customer CS4 has eight family members (attribute ATTa: eight people). Further, the customer CS4 is a detached house (attribute ATTb: detached house). Moreover, the solar panel is not installed in the house where customer CS4 resides (attribute ATTc: none). Further, the customer CS4 has the contract form 20A of the metered lamp B (attribute ATTd: metered lamp B 20A). Further, the customer CS4 has three daily days (attribute ATTe: third daily day). Further, the customer CS4 resides in an area other than Kanto (attribute ATTf: other than Kanto).

Returning to FIG. 3, the acquiring unit 110 supplies the acquired attribute information ATTI to the feature amount calculating unit 120 (step S120).
The feature amount calculation unit 120 acquires the attribute information ATTI from the acquisition unit 110 (step S130). Further, the feature amount calculation unit 120 reads the feature amount information FAI from the storage unit 500 (step S140). The feature quantity calculation means 120 calculates a consumer feature quantity CSFA based on the acquired attribute information ATTI and feature quantity information FAI (step S150).
First, the feature amount information FAI will be described, and the customer feature amount CSFA will be described later.

The feature amount information FAI is information indicating the feature amount FA of the attribute ATT for each attribute ATT. The feature amount FA is a value used for classifying the attribute ATT according to the feature of the attribute ATT.
As described above, the storage unit 500 stores the feature amount information FAI. The feature amount information FAI includes the feature amount FAa to the feature amount FAf corresponding to each of the attribute ATTa to the attribute ATTf. The feature amount FAa is the feature amount FA of the attribute ATTa. The feature amount FAb is the feature amount FA of the attribute ATTb. In addition, the feature amount FAc to the feature amount FAf also correspond to the attribute ATTc to the attribute ATTf.

FIG. 5 is a first table showing an example of the correspondence between the attribute ATT and the feature amount FA of the present embodiment. Specifically, FIG. 5 is a table showing the correspondence between the value of the attribute ATTa and the feature amount (feature amount FAa) of the attribute ATTa.
As shown in FIG. 5, in this example, when the number of family members (attribute ATTa) of the customer CS is 1 to 3, the feature amount FAa is 1. When the attribute ATTa is from 4 to 6, the feature amount FAa is 2. When the attribute ATTa is from 7 to 9, the feature amount FAa is 3. When the attribute ATTa is 10 or more, the feature amount FAa is 4.

FIG. 6 is a second table showing an example of the correspondence between the attribute ATT and the feature amount FA of the present embodiment. Specifically, FIG. 6 is a table showing the correspondence between the value of the attribute ATTb and the feature amount (feature amount FAb) of the attribute ATTb.
As shown in FIG. 6, in this example, when the housing form (attribute ATTb) of the customer CS is a detached house, the feature amount FAb is 1. When the attribute ATTb is an apartment house, the feature amount FAb is 2. When the attribute ATTb is a multi-family house, the feature amount FAb is 3.

FIG. 7 is a third table showing an example of the correspondence between the attribute ATT and the feature amount FA of the present embodiment. Specifically, FIG. 7 is a table showing the correspondence between the value of the attribute ATTc and the feature amount (feature amount FAc) of the attribute ATTc.
As shown in FIG. 7, in this example, the feature amount FAc is 1 when there is presence / absence of installation of a solar panel (attribute ATTc) in the house where the customer CS lives. When the attribute ATTc is none, the feature amount FAc is 2.

FIG. 8 is a fourth table showing an example of the correspondence between the attribute ATT and the feature amount FA of the present embodiment. Specifically, FIG. 8 is a table showing the correspondence between the value of the attribute ATTd and the feature amount (feature amount FAd) of the attribute ATTd.
As shown in FIG. 8, in this example, when the contract form (attribute ATTd) of the customer CS is 10A of the metered light B, the feature amount FAd is 1. Further, when the attribute ATTd is 20A of the metered electric lamp B, the feature amount FAd is 2. In addition, when the attribute ATTd is 30A of the metered lamp B, the feature amount FAd is 3. Further, when the attribute ATTd is 40A of the metered electric lamp B, the feature amount FAd is 4. Further, when the attribute ATTd is 50A of the metered lamp B, the feature amount FAd is 5. Further, when the attribute ATTd is 60A of the metered electric lamp B, the feature amount FAd is 6. Further, when the attribute ATTd is the metered lamp C, the feature value FAd is 7.

FIG. 9 is a fifth table showing an example of the correspondence between the attribute ATT and the feature amount FA of the present embodiment. Specifically, FIG. 9 is a table showing the correspondence between the value of the attribute ATTe and the feature amount (feature amount FAe) of the attribute ATTe.
FIG. 9 shows the correspondence between the daily date (attribute ATTe) of the customer CS and the feature amount FAe in this example. As shown in FIG. 9, in this example, the feature amount FAe is 1 when the customer CS has an ordinary date (attribute ATTe). Further, as shown in FIG. 9, the daily dates from the first day to the 26th day correspond to the feature values FAe from 1 to 19.

FIG. 10 is a sixth table showing an example of the correspondence between the attribute ATT and the feature amount FA of the present embodiment. Specifically, FIG. 10 is a table showing the correspondence between the value of the attribute ATTf and the feature amount (feature amount FAf) of the attribute ATTf.
As shown in FIG. 10, in this example, when the residential area (attribute ATTf) where the customer CS resides is Kanto, the feature amount FAf is 1. When the attribute ATTf is other than Kanto, the feature amount FAf is 2.

Hereinafter, the customer feature quantity CSFA will be described with reference to FIG. FIG. 11 is a table showing an example of the customer feature quantity CSFA of the present embodiment.
The customer feature quantity CSFA is a feature quantity FA corresponding to the attribute information ATTI of the customer CS that receives power supply.
As described above, in this example, since the attribute ATTa included in the attribute information ATTI1 is three, the feature amount FAa of the customer CS1 is 1. Further, since the attribute ATTb included in the attribute information ATTI1 is a detached house, the feature value FAb of the customer CS1 is 1. Further, since the attribute ATTc included in the attribute information ATTI1 has a solar panel, the feature value FAc of the customer CS1 is 1. Further, since the attribute ATTd included in the attribute information ATTI1 is 10A of the metered lamp B, the feature amount FAd of the customer CS1 is 1. Further, since the attribute ATTe included in the attribute information ATTI1 is 26 days, the feature amount FAe of the customer CS1 is 19. Further, since the attribute ATTf included in the attribute information ATTI1 is Kanto, the feature amount FAf of the customer CS1 is 1. That is, the customer feature quantity CSFA of the customer CS1 (customer feature quantity CSFA1) has a feature quantity FAa of 1, a feature quantity FAb of 1, a feature quantity FAc of 1, a feature quantity FAd of 1, a feature quantity FAe of 19, The feature amount FAf is 1.

  In this example, since the attribute ATTa included in the attribute information ATTI2 is five, the feature amount FAa of the customer CS2 is 2. Further, since the attribute ATTb included in the attribute information ATTI2 is an apartment house, the feature value FAb of the customer CS2 is 2. Further, since the attribute ATTc included in the attribute information ATTI2 has no solar panel, the feature value FAc of the customer CS2 is 2. Further, since the attribute ATTd included in the attribute information ATTI2 is 30A of the metered lamp B, the feature amount FAd of the customer CS2 is 3. Further, since the attribute ATTe included in the attribute information ATTI2 is 15 days, the feature amount FAe of the customer CS2 is 10. Further, since the attribute ATTf included in the attribute information ATTI2 is Kanto, the feature amount FAf of the customer CS2 is 1. That is, the customer feature quantity CSFA of the customer CS2 (customer feature quantity CSFA2) has a feature quantity FAa of 2, a feature quantity FAb of 2, a feature quantity FAc of 2, a feature quantity FAd of 3, a feature quantity FAe of 10. The feature amount FAf is 1.

  In this example, since the attribute ATTa included in the attribute information ATTI3 is three, the feature amount FAa of the customer CS3 is 1. Further, since the attribute ATTb included in the attribute information ATTI3 is a multi-family house, the feature value FAb of the customer CS3 is 3. Further, since the attribute ATTc included in the attribute information ATTI3 is a solar panel, the feature value FAc of the customer CS3 is 1. Further, since the attribute ATTd included in the attribute information ATTI3 is 50A of the metered lamp B, the feature amount FAd of the customer CS3 is 5. Further, since the attribute ATTe included in the attribute information ATTI3 is 8 days, the feature amount FAe of the customer CS3 is 5. Further, since the attribute ATTf included in the attribute information ATTI3 is Kanto, the feature amount FAf of the customer CS3 is 1. That is, the customer feature quantity CSFA of the customer CS3 (customer feature quantity CSFA3) has a feature quantity FAa of 1, a feature quantity FAb of 3, a feature quantity FAc of 1, a feature quantity FAd of 5, a feature quantity FAe of 5. The feature amount FAf is 1.

  In this example, since the attribute ATTa included in the attribute information ATTI4 is eight, the feature amount FAa of the customer CS4 is three. Moreover, since the attribute ATTb included in the attribute information ATTI4 is a detached house, the feature value FAb of the customer CS4 is 1. Further, since the attribute ATTc included in the attribute information ATTI4 has no solar panel, the feature value FAc of the customer CS4 is 2. Further, since the attribute ATTd included in the attribute information ATTI4 is 20A of the metered amount lamp B, the feature amount FAd of the customer CS4 is 2. Further, since the attribute ATTe included in the attribute information ATTI4 is 3 days, the feature amount FAe of the customer CS4 is 3. Further, since the attribute ATTf included in the attribute information ATTI4 is other than Kanto, the feature amount FAf of the customer CS4 is 2. That is, the customer feature quantity CSFA of the customer CS4 (customer feature quantity CSFA4) has a feature quantity FAa of 3, a feature quantity FAb of 1, a feature quantity FAc of 2, a feature quantity FAd of 2, and a feature quantity FAe of 3. The feature amount FAf is 2.

  In the following description, the consumer feature quantity CSFA1, the customer feature quantity CSFA2, the customer feature quantity CSFA3, and the customer feature quantity CSFA4 are collectively referred to as a customer feature quantity CSFA when not particularly distinguished.

Returning to FIG. 2, the feature quantity calculation means 120 supplies the calculated customer feature quantity CSFA to the attribute classification means 130 (step S160 in FIG. 3). The attribute classification unit 130 acquires the customer feature amount CSFA from the feature amount calculation unit 120 (step S170 in FIG. 3). Further, the attribute classification unit 130 reads the cluster information CLI from the storage unit 500 (step S180 in FIG. 3).
The attribute classification unit 130 classifies the customer CS for each cluster CL based on the acquired customer feature amount CSFA and the cluster information CLI.

Hereinafter, the cluster information CLI will be described with reference to FIG. FIG. 12 is a table showing an example of the cluster information CLI of the present embodiment. The cluster information CLI is information including a plurality of clusters CL. In this example, the cluster information CLI includes clusters CL1 to CL8. The cluster CL is a management unit for classifying and managing the customer CS according to the combination of the feature amounts FA.
In the cluster CL1, the feature amount FAa is 1, the feature amount FAb is 1, the feature amount FAc is 1, the feature amount FAd is 1, the feature amount FAe is 19, and the feature amount FAf is 1. The cluster CL2 has a feature quantity FAa of 3, a feature quantity FAb of 1, a feature quantity FAc of 2, a feature quantity FAd of 2, a feature quantity FAe of 3, and a feature quantity FAf of 2. The cluster CL3 has a feature quantity FAa of 4, a feature quantity FAb of 3, a feature quantity FAc of 2, a feature quantity FAd of 6, a feature quantity FAe of 3, and a feature quantity FAf of 1. The cluster CL4 has a feature quantity FAa of 2, a feature quantity FAb of 2, a feature quantity FAc of 2, a feature quantity FAd of 3, a feature quantity FAe of 4, and a feature quantity FAf of 2.

  The cluster CL5 has a feature quantity FAa of 2, a feature quantity FAb of 2, a feature quantity FAc of 2, a feature quantity FAd of 3, a feature quantity FAe of 10, and a feature quantity FAf of 1. The cluster CL6 has a feature quantity FAa of 3, a feature quantity FAb of 1, a feature quantity FAc of 2, a feature quantity FAd of 3, a feature quantity FAe of 3, and a feature quantity FAf of 2. In the cluster CL7, the feature amount FAa is 1, the feature amount FAb is 3, the feature amount FAc is 1, the feature amount FAd is 5, the feature amount FAe is 5, and the feature amount FAf is 1. The cluster CL8 has a feature quantity FAa of 2, a feature quantity FAb of 2, a feature quantity FAc of 1, a feature quantity FAd of 4, a feature quantity FAe of 7, and a feature quantity FAf of 2.

  Returning to FIG. 2, the attribute classification unit 130 classifies the customer CS for each cluster CL based on the acquired customer feature amount CSFA and the cluster information CLI. Specifically, the attribute classification unit 130 classifies the customer CS into a cluster CL in which the customer feature quantity CSFA matches the cluster CL feature quantity FA included in the cluster information CLI. More specifically, the attribute classification unit 130 generates attribute classification information AC that classifies the customer CS for each cluster CL based on the customer feature amount CSFA and the cluster information CLI (step S190 in FIG. 3). . Hereinafter, the attribute classification information AC will be described with reference to FIG.

  FIG. 13 is a table showing an example of the attribute classification information AC of the present embodiment. As shown in FIG. 13, in this example, the customer CS1 is classified into the cluster CL1 based on the customer feature amount CSFA and the cluster information CLI. The consumer CS2 is classified into the cluster CL5 based on the consumer feature quantity CSFA and the cluster information CLI. Further, the customer CS3 is classified into the cluster CL7 based on the customer feature amount CSFA and the cluster information CLI. The consumer CS4 is classified into the cluster CL2 based on the consumer feature quantity CSFA and the cluster information CLI.

Returning to FIG. 2, the attribute classification unit 130 supplies the attribute classification information AC obtained by classifying the customer CS for each cluster CL to the average power usage calculation unit 150 and the average power usage provision unit 160 (step in FIG. 3). S200).
Further, as shown in FIG. 2, the power usage amount acquisition unit 140 acquires the power usage amount PU (step S210 in FIG. 3). The power usage amount PU is information including the amount of power used by the customer CS in the past. Hereinafter, the power usage amount PU will be described with reference to FIG.

FIG. 14 is a table showing an example of the power usage amount PU of the present embodiment.
As illustrated in FIG. 14, the power usage amount PU includes information indicating the amount of power used in the month in which the customer CS is present for each customer CS. In this example, the power usage amount PU includes information indicating the amount of power used by the customer CS in each month from August to November.
Specifically, the power usage amount PU includes information that the customer CS1 uses 55 kWh in August, 59 kWh in September, 53 kWh in October, and 59 kWh in November. In the following description, the amount of power used in the past by the customer CS1 included in the power usage amount PU will be referred to as power usage amount PU1.
The power usage amount PU includes information that the customer CS2 uses 54 kWh in August, 55 kWh in September, 55 kWh in October, and 53 kWh in November. In the following description, the amount of power used in the past by the customer CS2 included in the power usage amount PU will be referred to as power usage amount PU2.
The power usage amount PU includes information that the customer CS3 is using 59 kWh in August, 60 kWh in September, 61 kWh in October, and 61 kWh in November. In the following description, the amount of power used in the past by the customer CS3 included in the power usage amount PU will be referred to as power usage amount PU3.
The power usage amount PU includes information that the customer CS4 is using 78 kWh in August, 77 kWh in September, 78 kWh in October, and 77 kWh in November. In the following description, the amount of power used by the customer CS4 included in the power usage amount PU in the past is referred to as power usage amount PU4.
In the following description, when the power usage amount PU1, the power usage amount PU2, the power usage amount PU3, and the power usage amount PU4 are not particularly distinguished, they are collectively referred to as the power usage amount PU.

Returning to FIG. 2, the power usage amount acquisition unit 140 acquires the power usage amount PU via the network N from, for example, a server in which the power usage amount PU is stored.
Here, the amount of power used by the customer CS is acquired on a calendar day corresponding to an example date indicated by the attribute ATTe of the customer CS. In this example, the first day, the second day, the third day, the fourth day, the eighth day, the ninth day, the tenth day, the 11th day, the 12th day, the 15th day, the 16th day. It is indicated by 19 patterns of days, 17 days, 18 days, 19 days, 22 days, 23 days, 24 days, 25 days, and 26 cases. Each day corresponds to a specific calendar day. In this example, one example day corresponds to two calendar days. Two example days correspond to the second day of the calendar day. Three days correspond to the second day of the calendar day. The 4th day corresponds to the 3rd day of the calendar day. The 8th day corresponds to the 6th day of the calendar day. Nine cases correspond to the eighth calendar day. The tenth day corresponds to the ninth day of the calendar. The eleventh day corresponds to the tenth day of the calendar day. The 12th day corresponds to the 11th day of the calendar day. The 15th day corresponds to the 13th day of the calendar day. The 16th day corresponds to the 15th day of the calendar day. The 17th day corresponds to the 16th day of the calendar day. The 18th day corresponds to the 17th day of the calendar day. The 19th day corresponds to the 18th of the calendar day. The 22th day corresponds to the 20th of the calendar day. The 23rd day corresponds to the 22nd calendar day. The 24th day corresponds to the 23rd day of the calendar day. The 25th day corresponds to the 24th day of the calendar day. The 26th day corresponds to the 25th day of the calendar day.
Here, the acquisition method of the electric energy used by the customer CS includes a meter reading day system that is measured on a different calendar day every month and a measurement daily system that is measured on the same calendar day every month. In this example, the amount of electric power used by the customer CS is acquired by the measurement daily system. Therefore, the calendar day corresponding to the regular day is the measurement date of the measurement date system.
As described above, the amount of power used by the customer CS is acquired approximately every day from the 2nd to the 25th of the month. That is, the power usage amount PU is updated approximately every day. That is, the power usage amount acquisition unit 140 acquires the power usage amount PU every day.
The power usage amount acquiring unit 140 supplies the acquired power usage amount PU to the average power usage amount calculating unit 150 (step S220 in FIG. 3).

The average power consumption calculation unit 150 acquires the attribute classification information AC from the attribute classification unit 130 (step S230 in FIG. 3). Further, the average power usage calculation unit 150 acquires the power usage PU from the power usage acquisition unit 140 (step S240 in FIG. 3). The average power usage calculation means 150 calculates the average power usage APU based on the acquired attribute classification information AC and the power usage PU (step S250 in FIG. 3).
The average power consumption APU is an average of the amount of power used in a month in which there is a customer CS classified into the cluster CL among the amount of power used by the customer CS classified into the cluster CL. Value. In this example, the average power consumption calculation means 150 calculates the average power consumption used by the customer CS in November as the average power usage APU from August to November included in the power usage PU. The case where it does is demonstrated.

  In the above description, the case where the amount of power used by the customer CS from August to November is described as the power usage amount PU has been described, but is not limited thereto. The power usage amount PU only needs to include at least the power consumption for two months used by the customer CS in the past. Further, the power usage amount PU may include the power usage amount from the present to the past 24 months used by the customer CS in the past.

  In addition, in the above description, the average power usage APU is the average of the amount of power used in the month in which there is a customer CS classified into the cluster CL among the amount of power used by the customer CS classified into the cluster CL. Although the case where the value is calculated for each cluster CL has been described, the present invention is not limited to this. The average power consumption APU is an average of a plurality of months of the amount of electric power used by the customer CS classified into the cluster CL among the electric energy used by the customer CS classified into the cluster CL for each cluster CL. It may be a calculated value.

Hereinafter, the average power consumption APU will be described with reference to FIG. FIG. 15 is a table showing an example of the average power consumption APU of the present embodiment. As shown in FIG. 15, in this example, the average of the amount of electric power in a certain month (average power usage amount APU1) used by the customer CS classified into the cluster CL1 is 55 kWh. Moreover, the average of the amount of electric power in a certain month used by the customer CS classified into the cluster CL2 (average power usage amount APU2) is 66 kWh. The average of the monthly power consumption (average power usage APU3) used by the customer CS classified into the cluster CL3 is 79 kWh. The average of the monthly power consumption (average power usage APU4) used by the customer CS classified into the cluster CL4 is 59 kWh. The average (average power usage APU5) of a certain amount of electricity used by a customer CS classified into the cluster CL5 is 63 kWh. The average amount of electric power in a certain month used by the customer CS classified into the cluster CL6 (average power usage amount APU6) is 70 kWh. The average of the amount of electric power in a certain month used by the customer CS classified into the cluster CL7 (average power usage amount APU7) is 50 kWh. The average of the amount of electric power in a certain month used by the customer CS classified into the cluster CL8 (average power usage amount APU8) is 62 kWh.
In the following description, average power usage APU1, average power usage APU2, average power usage APU3, average power usage APU4, average power usage APU5, average power usage APU6, average power usage APU7 and average power usage The amount APU8 is collectively referred to as an average power consumption APU.

Returning to FIG. 2, the average power usage calculating means 150 supplies the calculated average power usage APU to the average power usage providing means 160 (step S260 in FIG. 3).
The average power consumption providing unit 160 acquires the attribute classification information AC from the attribute classification unit 130 (step S270 in FIG. 3). The average power usage providing unit 160 acquires the average power usage APU from the average power usage calculating unit 150 (step S280 in FIG. 3). The average power usage amount providing unit 160 generates the average power usage amount provision information PV based on the attribute classification information AC and the average power usage amount APU (step S290 in FIG. 3).

  Hereinafter, the average power consumption provision information PV will be described with reference to FIG. FIG. 16 is a table showing an example of the average power usage amount provision information PV of the present embodiment. As shown in FIG. 16, since the consumer CS1 is classified into the cluster CL1, the average amount of electricity in a certain month used by the consumer CS classified into the cluster CL1 is 55 kWh (average power usage APU1). Is shown. Further, since the customer CS2 is classified into the cluster CL5, it is indicated that the average amount of electric power in a certain month used by the customer CS classified into the cluster CL5 is 63 kWh (average power usage APU5). Further, since the customer CS3 is classified into the cluster CL7, it is indicated that the average amount of electric power in a certain month used by the customer CS classified into the cluster CL7 is 50 kWh (average power usage APU7). Further, since the customer CS4 is classified into the cluster CL2, it is indicated that the average of the amount of electric power in a certain month used by the customer CS classified into the cluster CL2 is 66 kWh (average power usage APU2).

Returning to FIG. 2, the average power usage amount providing means 160 supplies the generated average power usage amount provision information PV to the customer CS by a known method (step S <b> 300 in FIG. 3).
Specifically, the average power usage providing means 160 is based on the average power usage providing information PV, and the average (average) of the monthly power used by the customer CS classified into the cluster CL for each cluster CL. Electricity consumption APU) is provided to the customer CS.
More specifically, the average power usage amount providing means 160 provides the consumer CS classified from the cluster CL1 to the cluster CL8 with the average power usage amount APU1 to the average power usage amount APU8. That is, the average power usage APU1 of the cluster CL1 is provided to the consumer CS classified into the cluster CL1. Moreover, the average power consumption APU2 of the cluster CL2 is provided to the customer CS classified into the cluster CL2. Similarly, the average power usage APU8 from the average power usage APU3 corresponding to the cluster CL8 is provided to the customers CS classified into the other clusters CL3 to CL8.

In the above description, the average power usage providing means 160 determines the average power usage APU used by the customer CS classified into the cluster CL by a known method based on the average power usage providing information PV. Although the case where it provides for every consumer CS classified into (2) was demonstrated, it is not restricted to this.
For example, the average power usage amount providing unit 160 may output the average power usage amount APU by a known method so that the average power usage amount providing means 160 can be viewed on a site on the Internet via a network. Further, the average power usage providing means 160 may provide information indicating the cluster CL into which the customer CS is classified for each customer CS based on the attribute classification information AC.
Thereby, the average power usage providing device 1 provides the average power usage APU of the cluster CL in which the customer CS is classified to the customer CS.

As described above, the average power usage amount providing apparatus 1 includes the control unit 100 and the storage unit 500. The storage unit 500 stores feature amount information FAI and cluster information CLI. The control unit 100 includes an acquisition unit 110, a feature amount calculation unit 120, an attribute classification unit 130, a power usage amount acquisition unit 140, an average power usage amount calculation unit 150, and an average power usage amount provision unit 160. .
The acquisition unit 110 acquires attribute information ATTI indicating the attribute ATT of the customer CS that receives the supply of electric power.
The feature quantity calculation means 120 is a customer who is the feature quantity FA of the attribute information ATTI of the customer CS based on the attribute information ATTI acquired by the acquisition means 110 and the feature quantity FA predetermined according to the attribute ATT. A feature amount CSFA is calculated.
The attribute classification unit 130 classifies the customer CS into a predetermined cluster CL based on the customer feature amount CSFA calculated by the feature amount calculation unit 120.
The power usage amount acquisition unit 140 acquires a power usage amount PU that is a usage amount of a month in which the customer CS has used power in the past.

The average power consumption calculating unit 150 acquires attribute classification information AC indicating information that the attribute classification unit 130 classifies the customer CS into the cluster CL. Further, the average power usage calculation unit 150 acquires a power usage PU that is a usage of power used by the customer CS acquired by the power usage acquisition unit 140 in the past. The average power usage calculation means 150 is a cluster of the power usage PU that is the power usage used in the past by the customer CS classified into the cluster CL based on the attribute classification information AC and the power usage PU. An average of a certain month for each CL is calculated for each cluster CL. Specifically, the average power usage amount calculation unit 150 generates an average power usage amount APU based on the attribute classification information AC and the power usage amount PU.
The average power usage providing unit 160 provides the consumer CS with the average power usage APU calculated by the average power usage calculating unit 150.

Here, in the conventional technology, even though the customer CS can know the breakdown of the amount of power used by the customer CS, it cannot recognize whether the amount of power is using too much power. was there. Even if the amount of power used by the customer CS is simply compared with the amount of power used by another customer CS different from the customer CS, the customer CS uses too much power. It may be difficult to recognize whether or not.
That is, in order to recognize whether or not the customer CS is using too much power, the attribute ATT indicating the characteristics of the lifestyle of the customer CS such as the number of family members (attribute ATTa) and the contract form (attribute ATTd). In some cases, it is required to compare customer CSs having similarities.

According to the average power usage providing device 1 of the present embodiment, the consumer CS can be classified into the clusters CL based on the attribute ATT for each consumer CS. Further, a feature amount FA based on the attribute ATT is defined for the cluster CL. That is, when the customer CS is classified into the cluster CL based on the attribute ATT of the customer CS, customers CS having similar attributes ATT are collected in the cluster CL.
Thereby, the consumer CS can refer to the average power consumption APU of the cluster CL into which the consumer CS is classified. That is, the customer CS can refer to the average power usage APU of the power usage PU used by the customers CS having similar attributes ATT to the cluster CL into which the customer CS is classified. . That is, the customer CS can use the average power usage APU of the cluster CL into which the customer CS is classified as an index for recognizing whether or not the power is being used excessively.
That is, according to the average power usage providing device 1 of the present embodiment, it is possible to provide the average power usage APU that serves as an index for recognizing whether or not the customer CS is using too much power.

Here, even if the average power usage amount APU is the average power usage amount APU of the same cluster CL, the value thereof is greatly different every month. For example, in June and July, the value of the average power consumption APU is larger in July than in June. This is because in July, there are more midsummer days and intensely hot days than in June, and in July, the amount of electric power consumed by the customer CS increases with the use of air conditioning and the like from June. That is, it is preferable that the average power consumption APU, which serves as an index for recognizing whether or not the customer CS is using too much power, is calculated every month.
According to the average power usage providing device 1 of the present embodiment, the average power usage APU is an index for recognizing whether or not the customer CS is using too much power, and is calculated every month. An average power usage APU can be provided.

[Second Embodiment]
The second embodiment of the present invention will be described below with reference to the drawings. FIG. 17 is a configuration diagram illustrating an example of a configuration of the average power usage providing device 2 according to the second embodiment.
As shown in FIG. 17, the average power usage providing device 2 of the present embodiment includes a storage unit 500 and a control unit 200. The control unit 200 includes an acquisition unit 110, a feature amount calculation unit 120, an attribute classification unit 130, a power usage amount acquisition unit 140, an average power usage amount calculation unit 150, an average power usage amount provision unit 160, a lower limit Value calculation means 210, minority cluster extraction means 220, and minority cluster merging means 230 are further provided.
In addition, about the structure and operation | movement similar to 1st Embodiment mentioned above, the same code | symbol is attached | subjected and the description is abbreviate | omitted.

The lower limit calculation unit 210 acquires the attribute classification information AC from the attribute classification unit 130. Here, when the total number of consumers CS corresponds to the number of attribute information ATTI, the total number of consumers CS is indicated by the number of attribute information ATTI acquired by the acquisition unit 110. In this case, the lower limit calculator 210 calculates the lower limit LL by calculating the total number of consumers CS based on the acquired attribute classification information AC.
The lower limit value LL is a value indicating the minimum value of the number of customers CS included in the cluster CL. For example, the number of customers CS classified into a certain cluster CL may be small. In this case, even if the average of the amount of electric power for a certain month used by the customer CS classified into the cluster CL is calculated, it may not be possible to use it as a guide for the amount of electric power used by the customer CS. The lower limit calculation means 210 calculates the minimum value of the number of consumers CS classified into the cluster CL by calculating the lower limit LL based on the attribute classification information AC.

Here, the lower limit calculation means 210 dynamically calculates the lower limit LL of the number of consumers CS classified into the cluster CL at the timing of classifying the consumers CS.
Here, the number of customer CSs may change day by day due to a new power supply contract, expiration of the contract, or the like. That is, the number of consumers CS changes dynamically. The lower limit value LL of the customer CS classified into the cluster CL is calculated based on the number of customers CS. Therefore, when the number of consumers CS changes, the lower limit value LL of the consumer CS in which the consumer CS is classified into the cluster CL must be recalculated.
Therefore, the lower limit calculation means 210 dynamically calculates the lower limit LL of the number of consumers CS classified into the cluster CL as the number of consumers CS dynamically changes.

  For example, the lower limit calculation means 210 calculates the number obtained by multiplying the number of consumers CS included in the attribute classification information AC by a predetermined ratio as the lower limit LL. Specifically, a value obtained by multiplying the number of customers CS included in the attribute classification information AC by 0.25% is calculated as the lower limit value LL.

In the above description, the lower limit calculation unit 210 has been described with respect to the case where the number obtained by multiplying the number of customers CS included in the attribute classification information AC by a predetermined ratio is calculated as the lower limit LL. Absent. The average power usage providing device 2 may acquire the total number of consumers CS that receive power supply by a known method. Moreover, the lower limit calculation means 210 may calculate the number obtained by multiplying the total number of consumers CS that receive the supplied power by a predetermined ratio as the lower limit LL.
Moreover, although the case where the attribute classification information AC indicates the total number of customers CS has been described above, the present invention is not limited to this. The total number of customers CS can also be indicated by the power usage amount PU acquired by the power usage amount acquisition means 140. When the power usage amount PU acquired by the power usage amount acquisition unit 140 indicates the total number of consumers CS, the lower limit value calculation unit 210 determines the demand indicated by the power usage amount PU acquired by the power usage amount acquisition unit 140. The lower limit LL may be calculated based on the total number of houses CS.

  In the above description, the case where the lower limit calculation unit 210 calculates the number obtained by multiplying the number of customers CS by 0.25% as the lower limit LL has been described, but the present invention is not limited to this. The lower limit value LL is calculated based on the lower limit value calculation means 210 according to the number of consumers CS and the number of clusters CL to be classified by the average power usage providing device 2. Any value may be calculated as the lower limit value LL as long as an average that can be obtained can be provided.

As shown in FIG. 17, the lower limit calculation unit 210 supplies the calculated lower limit LL to the minority cluster extraction unit 220. The minority cluster extraction unit 220 acquires the attribute classification information AC from the attribute classification unit 130. The minority cluster extraction unit 220 acquires the lower limit value LL from the lower limit value calculation unit 210.
The minority cluster extraction unit 220 extracts an inappropriate cluster UCL from the clusters CL indicated by the attribute classification information AC based on the acquired attribute classification information AC and the lower limit value LL. The unsuitable cluster UCL is a cluster CL that cannot calculate a value that is a measure of the amount of power used by the customer CS. In this example, the unsuitable cluster UCL is a cluster CL in which the number of customers CS classified into the cluster CL is smaller than the lower limit value LL.
The minority cluster extracting unit 220 supplies the extracted inappropriate cluster UCL to the minority cluster merging unit 230.

  Here, as described above, the lower limit calculator 210 calculates the lower limit LL at the timing of classifying the customer CS. The lower limit value LL is used when the minority cluster extraction unit 220 extracts an inappropriate cluster UCL. Therefore, the lower limit calculation unit 210 calculates the lower limit LL before the minority cluster extraction unit 220 extracts the unsuitable cluster UCL. That is, the timing at which the customer CS, which is the timing at which the lower limit calculation unit 210 calculates the lower limit LL, is the timing before the minority cluster extraction unit 220 extracts the unsuitable cluster UCL. That is, the lower limit calculation unit 210 calculates the lower limit LL before the minority cluster extraction unit 220 extracts the unsuitable cluster UCL, and supplies the calculated lower limit LL to the minority cluster extraction unit 220.

The minority cluster merging unit 230 acquires the attribute classification information AC from the attribute classification unit 130. Further, the minority cluster merging unit 230 acquires the inappropriate cluster UCL from the minority cluster extracting unit 220. The minority cluster merging means 230 merges the cluster CL, which is an unsuitable cluster UCL among the attribute classification information AC, into another cluster CL included in the attribute classification information AC.
As an example, the unsuitable cluster UCL is the cluster CL2. In this case, the minority cluster merging means 230 merges the customer CS classified into the cluster CL2 included in the attribute classification information AC into a cluster CL other than the cluster CL2.

  Specifically, the minority cluster merging means 230 changes the feature value FA included in the customer feature value CSFA of the customer CS belonging to the unsuitable cluster UCL to an arbitrary value. Hereinafter, an example in which the minority cluster merging unit 230 changes the feature value FA included in the customer feature value CSFA to an arbitrary value will be described.

  As described above, in this example, the inappropriate cluster UCL is the cluster CL2. Further, as described above, the feature amount FA of the cluster CL2 is that the feature amount FAa is 3, the feature amount FAb is 1, the feature amount FAc is 2, the feature amount FAd is 2, the feature amount FAe is 3, and the feature amount FAf is 2. It is. Further, as described above, the feature amount FA of the cluster CL6 is the feature amount FAa is 3, the feature amount FAb is 1, the feature amount FAc is 2, the feature amount FAd is 3, the feature amount FAe is 3, and the feature amount FAf is 2. is there. That is, the cluster CL2 and the cluster CL6 are identical except that the feature amount FAd is 2 and 3, respectively, and the feature amount FAd is different.

  In this example, the minority cluster merging means 230 changes the feature value FAd to an arbitrary value among the customer feature values CSFA of the customer CS4. In this example, the minority cluster merging means 230 changes the feature value FAd of the customer CS4 included in the customer feature value CSFA from 2 to 3. Thereby, the minority cluster merging means 230 merges the customer CS4 classified into the cluster CL2 into the cluster CL6 when the feature amount FAd of the customer CS4 classified into the cluster CL2 is changed from 2 to 3. To do. That is, the minority cluster merging means 230 selects the customer CS classified into the unsuitable cluster UCL based on the customer feature quantity CSFA after the feature quantity FA included in the customer feature quantity CSFA is changed to an arbitrary value. Merge with a cluster CL other than the unsuitable cluster UCL.

Hereinafter, an example of the attribute classification information AC after the minority cluster merging unit 230 has merged will be described with reference to FIG. FIG. 18 is a table showing an example of the attribute classification information AC after the minority cluster merging unit 230 of the present embodiment merges. As shown in FIG. 18, the minority cluster merging means 230 merges the customer CS classified into the cluster CL2 included in the attribute classification information AC into another cluster CL. As shown in FIG. 18, in this example, the minority cluster merging means 230 merges the customer CS4 classified into the cluster CL2 into the cluster CL6.
The minority cluster merging means 230 supplies the attribute classification information AC obtained by merging the inappropriate cluster UCL to another cluster CL other than the inappropriate cluster UCL to the average power usage calculating means 150.
The average power usage amount providing unit 160 calculates the average power usage amount APU based on the attribute classification information AC acquired from the minority cluster merging unit 230.

  In the above description, the case where the minority cluster merging unit 230 changes only the feature value FAd to an arbitrary value among the customer feature values CSFA of the customer CS has been described, but the present invention is not limited thereto. The minority cluster merging means 230 may change a plurality of feature amounts FA to arbitrary values among the customer feature amounts CSFA of the customer CS. Along with this, the minority cluster merging means 230 changes the customer CS in which the plurality of feature values FA among the customer feature values CSFA of the customer CS are changed to arbitrary values to other clusters CL other than the unsuitable cluster UCL. You may merge.

As described above, the average power usage providing device 2 of this embodiment includes the storage unit 500 and the control unit 200. The control unit 200 includes an acquisition unit 110, a feature amount calculation unit 120, an attribute classification unit 130, a power usage amount acquisition unit 140, an average power usage amount calculation unit 150, an average power usage amount provision unit 160, a lower limit A value calculating unit 210, a minority cluster extracting unit 220, and a minority cluster merging unit 230 are provided.
The lower limit calculation unit 210 calculates the lower limit LL of the number of customers CS classified into the cluster CL after the number of dynamically changing customers CS is acquired. The minority cluster extraction unit 220 is configured to determine the cluster CL in which the number of consumers CS is lower than the lower limit value LL based on the lower limit value LL calculated by the lower limit value calculation unit 210 and the number of consumers CS classified into the cluster CL. Are extracted as an unsuitable cluster UCL.

  The lower limit calculation unit 210 calculates the lower limit LL of the customer CS classified into the cluster CL, so that the minority cluster extraction unit 220 determines whether or not the number of customers CS classified into the cluster CL is appropriate. Can be determined. Specifically, the minority cluster extraction unit 220 can extract the inappropriate cluster UCL based on the lower limit value LL calculated by the lower limit value calculation unit 210 and the attribute classification information AC.

Here, a case where the number of customers CS classified into the cluster CL is small will be described. In this example, a case where the number of customers CS classified into a certain cluster CL is two will be described. In this case, when one customer CS among the customers CS classified into a certain cluster CL is a customer CS having a particularly large amount of power usage PU, the average power usage APU of a certain cluster CL is the customer. This is an average power consumption APU that is not suitable as an index for recognizing whether CS is using too much power.
As described above, when the number of consumers CS classified into the cluster CL does not exist more than a certain number with respect to the total number of consumers CS to which power is supplied, the average power usage APU of the cluster CL is It is not suitable as an index for recognizing whether or not the customer CS is using too much power.

According to the average power usage providing device 2 of the present embodiment, a cluster CL having a small number of customers CS classified into the cluster CL can be extracted as an inappropriate cluster UCL.
Thereby, the average power usage APU different from the average power usage APU of the unsuitable cluster UCL can be provided to the customers CS classified into the clusters CL included in the unsuitable cluster UCL. In other words, for the customer CS classified into the cluster CL included in the unsuitable cluster UCL, instead of the average power usage APU that is not suitable for the index for recognizing whether the customer CS is using too much power, the index is used as the index. A suitable average power usage APU can be provided.
That is, according to the average power usage providing device 2 of the present embodiment, the average power usage APU suitable for an index for recognizing whether or not the power is excessively used can be provided to the customer CS.

Further, as described above, the control unit 200 of this embodiment includes the minority cluster merging unit 230.
The minority cluster merging means 230 uses the inappropriate cluster UCL extracted by the minority cluster extraction means 220 as one or more arbitrary feature quantities among the feature quantities FA included in the consumer feature quantity CSFA of the customer CS belonging to the inappropriate cluster UCL. Based on the customer feature amount CSFA in which FA is changed to an arbitrary value, the unsuitable cluster UCL is merged with another cluster CL other than the unsuitable cluster UCL.
Along with this, the average power usage providing means 160 includes a customer CS classified into the other cluster CL merged by the minority cluster merging means 230 with a customer CS classified into the other cluster CL in the past. An average power usage amount APU for a certain month indicating an average of the power usage amount PU that is the usage amount of the used power for a certain month is provided.

For example, the minority cluster merging means 230 is the customer feature quantity CSFA of the customer CS classified into the cluster CL that is the inappropriate cluster UCL, and one of the feature quantities FA included in the customer feature quantity CSFA. The feature amount FA is changed. Thereby, the customer CS classified into the cluster CL which is an unsuitable cluster UCL can be classified into the cluster CL which does not correspond to the unsuitable cluster UCL.
In addition, among the feature quantities FA of the customer CS classified as the unsuitable cluster UCL, a customer CS whose one feature quantity FA is classified into a different cluster CL and a demand classified into the cluster CL which is an unsuitable cluster UCL. The house CS is a customer CS having a similar attribute ATT of the cluster CL.

That is, according to the average power usage providing device 2 of the present embodiment, the customer CS classified into the cluster CL that is the inappropriate cluster UCL is classified into the customer CS similar to the attribute ATT of the customer CS. It can be classified into CL.
That is, according to the average power usage providing device 2 of the present embodiment, the customer CS classified into the cluster CL that is the unsuitable cluster UCL is another cluster CL into which the customer CS having a similar attribute ATT is classified. Average power consumption APU can be provided.
Thereby, according to the average power usage providing device 2 of the present embodiment, the customer CS classified into the cluster CL included in the inappropriate cluster UCL recognizes whether the customer CS is using too much power. Instead of the average power usage APU that is not suitable for the index to be used, another average power usage APU that is suitable for the index can be provided.
That is, according to the average power usage providing device 2 of the present embodiment, the average power usage APU suitable for an index for recognizing whether or not the power is excessively used can be provided to the customer CS.

  Moreover, the lower limit calculation means 210 of this embodiment calculates the number which multiplied the predetermined ratio with respect to the total number of the consumers CS which receive supply of electric power as a lower limit LL.

  When the lower limit LL is predetermined as a predetermined value, an appropriate average power usage amount APU may not be provided due to an increase or decrease in the number of consumers CS. For example, in order to provide an appropriate average power usage APU to the consumer CS, at least the number of the consumer CS classified into the cluster CL is 5% of the total number of the consumer CSs to which power is supplied. Suppose that it is required to be classified only. In this case, if the lower limit LL is set as a predetermined value in advance, the value that is 5% of the total number of consumers CS that receive power supply varies according to the increase or decrease of the consumers CS. That is, when the lower limit value LL is set as a predetermined value, the inappropriate cluster UCL cannot be appropriately extracted.

On the other hand, the lower limit calculation means 210 of this embodiment calculates the lower limit LL by multiplying a predetermined ratio with the total number of consumers CS that receive power supply.
That is, the lower limit calculation unit 210 of the present embodiment can calculate the lower limit LL according to the increase or decrease of the customer CS. That is, according to the lower limit calculation unit 210 of the present embodiment, the lower limit LL can be calculated even when the total number of consumers CS that receive power supply changes.
Thereby, the average electric power consumption providing apparatus 2 of this embodiment can extract the unsuitable cluster UCL appropriately according to the total number of the customer CS. That is, according to the average power usage providing device 2 of the present embodiment, the customer CS classified into the cluster CL included in the inappropriate cluster UCL recognizes whether the customer CS is using too much power. Instead of the average power usage APU not suitable for the index, another average power usage APU suitable for the index can be provided.
That is, according to the average power usage providing device 2 of the present embodiment, the average power usage APU suitable for an index for recognizing whether or not the power is excessively used can be provided to the customer CS.

[Third Embodiment]
The third embodiment of the present invention will be described below with reference to the drawings. FIG. 19 is a configuration diagram illustrating an example of the configuration of the average power usage providing device 3 according to the third embodiment.
As illustrated in FIG. 19, the average power usage providing device 3 according to the present embodiment includes a storage unit 600 and a control unit 300.
The storage unit 600 stores feature amount information FAI, cluster information CLI, and distribution reference information DI. The distribution reference information DI will be described later.
The control unit 300 includes an acquisition unit 110, a feature amount calculation unit 120, an attribute classification unit 130, a power usage amount acquisition unit 140, an average power usage amount calculation unit 150, and an average power usage amount provision unit 160-1. , A usage variance calculation unit 310, a variance cluster extraction unit 320, a customer extraction unit 330, and an overall average power usage calculation unit 340.

As illustrated in FIG. 19, the usage amount variance calculation unit 310 acquires attribute classification information AC from the attribute classification unit 130. Further, the usage amount variance calculation unit 310 acquires the power usage amount PU from the power usage amount acquisition unit 140. Based on the power usage amount PU and the attribute classification information AC, the usage amount distribution calculating unit 310 calculates the distributed power usage amount DPU for each cluster CL indicated in the attribute classification information AC. The distributed power usage amount DPU is the distribution of the power amount used by the customer CS classified into the cluster CL for each cluster CL.
The usage variance calculation unit 310 supplies the calculated distributed power usage DPU to the distributed cluster extraction unit 320.

The distributed cluster extracting unit 320 acquires the distributed power usage amount DPU from the usage amount variance calculating unit 310. Further, the distributed cluster extraction unit 320 reads the distribution reference information DI from the storage unit 600. The distribution reference information DI is a value serving as a reference for the distribution of the electric energy used by the customer CS classified into the clusters CL for each cluster CL.
Based on the distributed power usage amount DPU and the distribution reference information DI, the distributed cluster extraction unit 320 distributes larger than the distribution reference information DI among the distributed power usage amounts DPU for each cluster CL indicated in the distributed power usage amount DPU. A cluster CL indicating the power usage amount DPU is extracted as an inappropriate cluster UCL.
The distributed cluster extracting unit 320 supplies the extracted inappropriate cluster UCL to the average power usage providing unit 160-1.

The customer extraction unit 330 acquires the power usage amount PU from the power usage amount acquisition unit 140. The customer extracting means 330 extracts a plurality of customers CS at random from the customers CS included in the power usage amount PU. Here, the customer CS randomly extracted by the customer extracting means 330 is referred to as an extracted customer ECS.
The customer extracting unit 330 supplies the extracted extracted customer ECS to the overall average power consumption calculating unit 340.

The total average power consumption calculating unit 340 acquires the extracted consumer ECS from the customer extracting unit 330. Further, the overall average power usage calculation unit 340 acquires the power usage PU from the power usage acquisition unit 140. The total average power usage calculating means 340 calculates the average of the monthly power consumption used by the extracted consumer ECS in the past based on the power usage PU and the extracted consumer ECS. Here, the average of the power consumption of a certain month used by the extracted consumer ECS calculated by the total average power usage calculation means 340 is described as the total average power usage TAPU.
The total average power usage amount calculating unit 340 supplies the calculated total average power usage amount TAPU to the average power usage amount providing unit 160-1.

  Based on the attribute classification information AC and the unsuitable cluster UCL, the average power usage amount providing unit 160-1 of the present embodiment determines whether a certain cluster CL included in the attribute classification information AC is an unsuitable cluster UCL. . When the average power usage amount providing unit 160-1 determines that a certain cluster CL included in the attribute classification information AC is an inappropriate cluster UCL, the average power usage amount TAPU is assigned to the consumer CS classified into the cluster CL. Supply. Further, when the average power usage providing unit 160-1 determines that a certain cluster CL included in the attribute classification information AC is not an unsuitable cluster UCL, the average power usage providing unit 160-1 assigns the customer CL classified as the cluster CL to the cluster CL. The corresponding average power consumption APU is supplied.

As described above, the average power usage amount providing device 3 of the present embodiment includes the control unit 300 and the storage unit 600. The storage unit 600 stores feature amount information FAI, cluster information CLI, and distribution reference information DI.
The control unit 300 includes an acquisition unit 110, a feature amount calculation unit 120, an attribute classification unit 130, a power usage amount acquisition unit 140, an average power usage amount calculation unit 150, and an average power usage amount provision unit 160-1. , A usage variance calculation unit 310, a variance cluster extraction unit 320, a customer extraction unit 330, and an overall average power usage calculation unit 340.

The usage amount variance calculating unit 310 is a variance that is a variance of the usage amount of power used in the past by the customer CS classified into the cluster CL for each cluster CL based on the power usage amount PU and the attribute classification information AC. The power usage amount DPU is calculated.
Based on the distributed power usage DPU calculated by the usage variance calculation unit 310 and the distribution reference information DI stored in the storage unit 600, the distributed cluster extraction unit 320 determines that the distributed power usage DPU is based on the distribution reference information DI. A large cluster CL is extracted as an inappropriate cluster UCL.
That is, the distributed cluster extracting unit 320 has a cluster in which the variance of the power usage amount PU, which is the usage amount of power used in the past by the customer CS classified as the cluster CL, is larger than the variance reference information DI that is a predetermined reference value. CL is extracted as an unsuitable cluster UCL.

  For example, it is assumed that a customer CS whose power usage amount PU is significantly different from another customer CS is classified into a certain cluster CL. In this case, the average power usage APU of the cluster CL is raised by the power usage PU of the customer CS whose power usage PU is significantly different. That is, the average power usage APU of the cluster CL is not suitable as an index for recognizing whether or not the customer CS classified into the cluster CL is using too much power.

That is, according to the average power usage providing device 3 of the present embodiment, based on the distributed power usage DPU of the customer CS classified as the cluster CL, the cluster CL in which the power usage PU is not appropriately distributed is selected. It can be extracted as an unsuitable cluster UCL.
Here, the cluster CL which is not appropriately distributed is a cluster CL in which the power usage amount PU of the customer CS classified as the cluster CL is biased.
According to the average power usage providing device 3 of the present embodiment, the consumer CS classified into the cluster CL included in the inappropriate cluster UCL is an index for recognizing whether or not the consumer CS is using too much power. Instead of the unsuitable average power usage APU, another average power usage APU suitable for the index can be provided.
That is, according to the average power usage providing device 3 of the present embodiment, the average power usage APU suitable for the index for recognizing whether or not the power is excessively used can be provided to the customer CS.

Further, as described above, the average power usage amount providing device 3 of the present embodiment includes the customer extracting unit 330 and the overall average power usage amount calculating unit 340.
The consumer extraction means 330 extracts the extraction consumer ECS at random based on a predetermined number or ratio.
The total average power usage calculating means 340 calculates the average of the power usage PU, which is the power usage of a certain month used by the extracted consumer ECS extracted by the consumer extracting means 330 in the past. Is calculated as the overall average power consumption TAPU.
The average power usage amount providing unit 160-1 provides the total average power usage amount TAPU to the consumer CS classified into the inappropriate cluster UCL.

For example, the average of the extracted customer ECS randomly extracted based on a predetermined number or ratio is substantially the same as the average of the total number of consumers CS that are supplied with power. That is, the total average power usage amount TAPU is substantially the same as the average of the total number of consumers CS that receive power supply. That is, the total average power usage amount TAPU is information suitable for an index for recognizing whether or not the customer CS classified into any cluster CL is using too much power.
According to the average power usage providing device 3 of the present embodiment, the consumer CS classified into the cluster CL included in the inappropriate cluster UCL is an index for recognizing whether or not the consumer CS is using too much power. Instead of the unsuitable average power usage APU, the overall average power usage TAPU suitable for the index is provided. According to the average power usage providing device 3 of the present embodiment, the average power usage APU suitable for an index for recognizing whether or not the power is excessively used can be provided to the customer CS.

In the above description, the average power usage providing unit 160-1 included in the average power usage providing device 3 is classified into the cluster CL which is the inappropriate cluster UCL based on the inappropriate cluster UCL extracted by the distributed cluster extraction unit 320. However, the present invention is not limited to this.
The average power usage amount providing device 3 may supply the overall average power usage amount TAPU to the consumers CS classified as the inappropriate cluster UCL based on the inappropriate cluster UCL extracted by the minority cluster extraction unit 220.

  Note that the units included in the average power usage providing device 1, the average power usage providing device 2 and the average power usage providing device 3 in each of the above embodiments may be realized by dedicated hardware. Further, it may be realized by a memory and a microprocessor.

  Each unit included in the average power usage providing device 1, the average power usage providing device 2 and the average power usage providing device 3 includes a memory and a CPU (central processing unit), and the average power usage providing device 1, You may implement | achieve the function by loading the program for implement | achieving the function of each part with which the average power usage providing apparatus 2 and the average power usage providing apparatus 3 are equipped, and executing it.

  Further, a program for realizing the function of each unit included in the average power usage providing device 1, the average power usage providing device 2 and the average power usage providing device 3 is recorded on a computer-readable recording medium, and this recording is performed. Processing may be performed by causing a computer system to read and execute a program recorded on a medium. Here, the “computer system” includes an OS and hardware such as peripheral devices.

Further, the “computer system” includes a homepage providing environment (or display environment) if a WWW system is used.
The “computer-readable recording medium” refers to a storage device such as a flexible medium, a magneto-optical disk, a portable medium such as a ROM and a CD-ROM, and a hard disk incorporated in a computer system. Furthermore, the “computer-readable recording medium” dynamically holds a program for a short time like a communication line when transmitting a program via a network such as the Internet or a communication line such as a telephone line. In this case, a volatile memory in a computer system serving as a server or a client in that case, and a program that holds a program for a certain period of time are also included. The program may be a program for realizing a part of the functions described above, and may be a program capable of realizing the functions described above in combination with a program already recorded in a computer system.

  The embodiment of the present invention has been described in detail with reference to the drawings. However, the specific configuration is not limited to this embodiment, and appropriate modifications may be made without departing from the spirit of the present invention. it can. You may combine the structure as described in each embodiment mentioned above.

1, 2, 3, average power usage providing device, 100, 200, 300, control unit, 500, 600, storage unit, 110, acquisition unit, 120, feature amount calculation unit, 130, attribute classification unit, 140, power Usage amount acquisition means, 150 ... average power usage amount calculation means, 160, 160-1 ... average power usage amount provision means, 210 ... lower limit value calculation means, 220 ... minority cluster extraction means, 230 ... minority cluster merge means, 310 ... Usage variance calculation means, 320 ... Distributed cluster extraction means, 330 ... Consumer extraction means, 340 ... Overall average power usage calculation means, AC ... Attribute classification information, APU, APU1, APU2, APU3, APU4, APU5, APU6, APU7, APU8 ... average power consumption, ATT, ATTa, ATTb, ATTc, ATTd, ATTe, ATTf ... attributes, ATT ATTI1, ATTI2, ATTI3, ATTI4 ... attribute information, CL, CL1, CL2, CL3, CL4, CL5, CL6, CL7, CL8 ... cluster, CLI ... cluster information, CS, CS1, CS2, CS3, CS4 ... consumer, CSFA, CSFA1, CSFA2, CSFA3, CSFA4 ... consumer feature, DI ... distributed reference information, DPU ... distributed power usage, ECS ... extracted consumer, FA, FAa, FAb, FAc, FAd, FAe, FAf ... feature , FAI ... feature information, LL ... lower limit, N ... network, PU, PU1, PU2, PU3, PU4 ... power usage, PV ... average power usage provision information, TAPU ... overall average power usage, UCL ... unsuitable cluster

Claims (8)

An acquisition means for acquiring attribute information indicating an attribute of a consumer receiving power supply;
Feature quantity calculation means for calculating a consumer feature quantity that is a feature quantity of the attribute information of the consumer based on the attribute information acquired by the acquisition means and a feature quantity predetermined according to the attribute. When,
Attribute classification means for classifying the consumer into a predetermined cluster based on the consumer feature quantity calculated by the feature quantity calculation means;
Power usage acquisition means for acquiring the amount of power used by the consumer in the past;
Based on the cluster in which the attribute classification unit classifies the consumer and the usage amount of power used in the past by the consumer acquired by the power usage amount acquisition unit, the consumer classified into the cluster is Average power usage calculating means for calculating the average of the usage of power used in the past for each cluster;
The average power usage calculating means provides the consumer with information indicating the average of the usage of power used by the consumer in the past, calculated by the consumer for each cluster. A characteristic average power consumption providing device. A lower limit calculating means for dynamically calculating a lower limit value of the number of consumers classified into the cluster at a timing of classifying the consumers;
Based on the lower limit value calculated by the lower limit calculation means and the number of consumers classified into the cluster, a minority cluster extraction means for extracting a cluster having the number of consumers below the lower limit value as an inappropriate cluster. The average power usage providing device according to claim 1, further comprising: The demand in which one or more arbitrary customer feature quantities are changed to an arbitrary value among the consumer feature quantities of the consumers belonging to the inappropriate cluster, as the inappropriate clusters extracted by the minority cluster extracting means. A minority cluster merging means for merging the unsuitable cluster with another cluster other than the unsuitable cluster based on the house feature amount;
The average power consumption providing means is:
The consumer classified into the other cluster merged by the minority cluster merging means is provided with an average of the amount of power used in the past by the consumer classified into the other cluster. The average power consumption providing device according to claim 2. The lower limit calculating means includes
The average power usage providing device according to claim 2 or 3, wherein the lower limit value is calculated by multiplying a total number of consumers who receive power supply by a predetermined ratio. The distributed cluster extraction means which extracts the cluster in which the usage-amount of the electric power used by the said consumer classified into the said cluster in the past is larger than a predetermined reference value as an unsuitable cluster is further provided. The average power usage providing device according to any one of claims 1 to 4. Consumer extracting means for randomly extracting the consumers based on a predetermined number or ratio;
Further comprising: an overall average power usage calculating means for obtaining an average of the power consumption used by the consumer extracted by the consumer in the past as an overall average power usage;
The average power consumption providing means is:
The average power usage providing apparatus according to any one of claims 2 to 5, wherein the overall average power usage is provided to the consumer classified into the inappropriate cluster. An acquisition step of acquiring attribute information indicating an attribute of a consumer receiving power supply;
A feature amount calculating step of calculating a consumer feature amount that is a feature amount of the attribute information of the consumer based on the attribute information acquired by the acquisition step and a feature amount predetermined according to the attribute. When,
An attribute classification step for classifying the customer into a predetermined cluster based on the customer feature amount calculated by the feature amount calculation step;
A power usage acquisition step of acquiring the usage of power used by the consumer in the past;
Based on the cluster in which the attribute classification step classifies the consumer and the usage amount of power used in the past by the consumer acquired in the power usage amount acquisition step, the consumer classified into the cluster is An average power usage calculating step for calculating the average of the amount of power used in the past for each cluster;
An average power usage providing step of providing the consumer with information indicating an average of usage of power used by the customer in the past, calculated by the consumer in the average power usage calculating step. A characteristic average power consumption providing method. An acquisition step of acquiring attribute information indicating an attribute of a consumer receiving power supply to the computer;
A feature amount calculating step of calculating a consumer feature amount that is a feature amount of the attribute information of the consumer based on the attribute information acquired by the acquisition step and a feature amount predetermined according to the attribute. When,
An attribute classification step for classifying the customer into a predetermined cluster based on the customer feature amount calculated by the feature amount calculation step;
A power usage acquisition step of acquiring the usage of power used by the consumer in the past;
Based on the cluster in which the attribute classification step classifies the consumer and the usage amount of power used in the past by the consumer acquired in the power usage amount acquisition step, the consumer classified into the cluster is An average power usage calculating step for calculating the average of the amount of power used in the past for each cluster;
In order to cause the average power usage calculating step to provide the consumer with information indicating an average of the power usage used in the past by the consumer calculated by the average power usage calculating step for each cluster. Average power usage program.

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