Data analyzing system and method

Proposed is a data analyzing system and method capable of performing highly reliable analytical processing which matches the actual situation. In a data analyzing device and method in which load data is classified into a plurality of clusters in consumer units based on the load data representing the power usage of each consumer for each unit time and the attribute information of each consumer, a diagnostic decision tree is generated for classifying the consumer into one of the clusters based on the attribute information of that consumer.

TECHNICAL FIELD

The present invention relates to a data analyzing system and method, and, for instance, can be suitably applied to an energy selling system.

BACKGROUND ART

Conventionally, in order to stably supply power to their customers (consumers), energy companies analyze the electrical power demand based on time series data of power usage for each unit time collected from the respective consumers, and adjust the power generation amount or adjust the power from the electric power exchange based on the analysis result.

In relation to this kind of analytical processing of the electrical power demand, for example, PTL 1 discloses a similarity analysis evaluation system which extracts the feature quantity focusing on the shape of the time series data, performs arbitrary classification based on the extracted feature quantity, and performs relevance evaluation based on the attribute of the time series data and the classification result.

Furthermore, PTL 2 discloses a load curve estimation system which categories a plurality of consumers into groups in which the consumption pattern of resources is similar, generates, for each group, a standard load curve representing the consumption pattern of resources of that group, identifies the group to which the consumer to be subject to estimation belongs, and estimates the resource consumption of the consumer to be subject to estimation for each unit time within an arbitrary period by using the standard load curve of the identified group.

CITATION LIST

Patent Literature

SUMMARY OF THE INVENTION

Problems to be Solved by the Invention

However, with the similarity analysis evaluation system disclosed in PTL 1, upon classifying the feature quantity focusing on the shape of the time series data into a plurality of clusters, since the cluster number is set manually, there is a problem in that the feature quantity cannot be classified into an appropriate number of clusters according to the actual situation.

Furthermore, even with the load curve estimation system disclosed in PTL 2, the groups of the consumption pattern are set in advance, and, similar to PTL 1, there is a problem in that the consumption pattern of resources of consumers cannot be classified into an appropriate number of groups according to the actual situation.

When it is not possible to classify the feature quantity or the consumption pattern into an appropriate number of clusters or groups according to the actual situation as in PTL 1 and PTL 2, the adjustment of the power generation amount and the procurement of electricity from the electric power exchange that are performed based on the analysis result will not match the actual situation, and there is a possibility that that there may be deficiency or excess in the required electrical energy.

Furthermore, according to the technologies disclosed in PTL 1 and PTL 2, there is a problem in that new consumers, from which the time series data and consumption pattern have not been acquired, and existing consumers, from which the time series data and consumption pattern cannot be acquired, cannot be classified into appropriate clusters and groups. Consequently, in the same manner as described above, the adjustment of the power generation amount and the procurement of electricity from the electric power exchange that are performed based on the analysis result will not match the actual situation, and there is a possibility that that there may be deficiency or excess in the required electrical energy.

The present invention was devised in view of the foregoing points, and an object of this invention is to propose a data analyzing system and method capable of performing highly reliable analytical processing which matches the actual situation.

Means to Solve the Problems

In order to achieve the foregoing object, the present invention provides a data analyzing system comprising a server device which collects load data representing power usage of each consumer for each unit time, and manages attribute information of each of the consumers, and a data analyzing device which classifies the load data into a plurality of clusters in consumers units based on the load data of each of the consumers for each unit time that is periodically notified from the server device and the attribute information of each of the consumers, wherein the data analyzing device generates a diagnostic decision tree for classifying the consumers into one of the clusters based on the attribute information of each of the consumers.

Moreover, according to the present invention, in the data analyzing system, the data analyzing device calculates an intra-cluster relevance representing a degree of unity of the load data in each of the clusters when assuming that a cluster number is respectively 1 to M (M is number of the consumers), and an inter-cluster average degree of separation representing a degree of separation of the clusters, and decides the cluster number to be used upon classifying the load data based on the calculation result.

The present invention additionally provides a data analyzing method executed in a data analyzing system comprising a server device which collects load data representing power usage of each consumer for each unit time, and manages attribute information of each of the consumers, and a data analyzing device which classifies the load data into a plurality of clusters in consumers units based on the load data of each of the consumers for each unit time that is periodically notified from the server device and the attribute information of each of the consumers, wherein the data analyzing device generates a diagnostic decision tree for classifying the consumers into one of the clusters based on the attribute information of each of the consumers.

Moreover, according to the present invention, the data analyzing method comprises a first step of the data analyzing device calculating an intra-cluster relevance representing a degree of unity of the load data in each of the clusters when assuming that a cluster number is respectively 1 to M (M is number of the consumers), and an inter-cluster average degree of separation representing a degree of separation of the clusters, and a second step of the data analyzing device deciding the cluster number to be used upon classifying the load data based on the calculation result.

According to the data analyzing system and the data analyzing method of the present invention, it is possible to also classify new consumers from which load data has not been acquired and existing consumers from which load data cannot be acquired into appropriate clusters.

Furthermore, according to the data analyzing system and the data analyzing method of the present invention, since the intra-cluster relevance and the inter-cluster average degree of separation when assuming that a cluster number is respectively 1 to M (M is number of the consumers) are actually calculated and the cluster number to be used upon classifying (clustering) the load data is decided based on the calculation result, it is possible to perform appropriate clustering which matches the actual situation.

Advantageous Effects of the Invention

According to the present invention, it is possible to realize a data analyzing system and method capable of performing highly reliable analytical processing which matches the actual situation.

DESCRIPTION OF EMBODIMENTS

An embodiment of the present invention is now explained in detail with reference to the appended drawings.

(1) Configuration of Electrical Power Demand Analyzing System According to this Embodiment

InFIG. 1, reference numeral1shows the overall energy selling system according to this embodiment. The energy selling system1is configured by a meter data collection server3, a consumer information providing server4, a sales information processing apparatus5, a traded product information processing apparatus6and a corporate information processing apparatus7of an energy company2, an equipment control terminal9and an information I/O terminal10respectively equipped in each consumer8receiving the supply of electricity from the energy company2, and a data analyzing device12and an information I/O terminal13of an analysis company11being connected via a network14.

The equipment control terminal9equipped in the consumer8is configured, for instance, from a smart meter. The equipment control terminal9measures the power usage of the consumer8, and sends the measurement result as load data to the meter data collection server3of the energy company2. Furthermore, the information I/O terminal10of each consumer8is configured, for instance, from a personal computer equipped with a browser. The information I/O terminal10is used for the consumer8to access the consumer information providing server4of the energy company2.

The meter data collection server3of the energy company2is a server device with a function of accumulating and managing the load data sent from the equipment control terminal9of each consumer8. The meter data collection server3manages, together with the load data of each consumer8, attribute information of each consumer8that is registered in advance such as the geographical location, contracted rate menu, system feeder number, contracted power reception and power reception equipment. The meter data collection server3periodically sends the accumulated attribute information and load data of each consumer8to the data analyzing device12of the analysis company11.

The consumer information providing server4is configured from a general-purpose server device, and presents an electrical power load curve (this is hereinafter referred to as the “load curve”) representing the transition of the future power usage of the consumer which is estimated by the data analyzing device12of the analysis company11as described later in response to a request from the consumer8, and provides information such as the rate menu and equipment (district heating and cooling equipment, heat pump water heater, regenerative heating equipment or the like) recommended for the consumer8based on the determination of the data analyzing device12of the analysis company11.

The sales information processing apparatus5is a computer device that is installed in the sales office of the energy company2, and is used for the energy company2to acquire analysis results related to sales from the data analyzing device12of the analysis company11. Furthermore, the traded product information processing apparatus6is a computer device that is installed in the power generation/procurement office of the energy company2, and, for instance, accesses the website for electricity trading provided by the electrical power exchange and performs buying bid of required electricity or selling bid of excess power. Furthermore, the corporate information processing apparatus7is a computer device that is installed in the management office of the energy company2.

The data analyzing device12of the analysis company11is a computer device with a function of analyzing the past electrical power demand based on the load data and attribution information of each consumer8sent from the meter data collection server3of the energy company2. Furthermore, the information I/O terminal13of the analysis company11is configured, for instance, from a personal computer equipped with a browser. The information I/O terminal13is used when the analysis company11is to perform the maintenance of the data analyzing device12.

FIG. 2shows a schematic configuration of the data analyzing device12of the analysis company. As shown inFIG. 2, the data analyzing device12is configured by comprising a CPU (Central Processing Unit)21, a memory22, a storage device23and a communication unit24which are mutually connected via an internal bus20.

The CPU21is a processor that governs the operational control of the overall data analyzing device12. Furthermore, the memory22is primarily used for temporarily storing various programs and data. The clustering processing program25, the standard load curve outline data generation processing program26, the diagnostic decision tree generation processing program27and the consumer data analytical processing program28described later are also stored and retained in the memory22.

The storage device23is configured, for instance, from a hard disk device, and is used for retaining programs and data for a long period of time. The energy company transmission data database29and the class and cluster information database30described later are stored and retained in the storage device23.

The communication unit24performs protocol control during communication with the meter data collection server3of the energy company2via the network14, or during communication with the consumer information providing server4, the sales information processing apparatus5, the traded product information processing apparatus6or the corporate information processing apparatus7, or during communication with the information I/O terminal13.

Meanwhile,FIG. 3shows a schematic configuration of the sales information processing apparatus5of the energy company2. As shown inFIG. 3, the sales information processing apparatus5is configured by comprising a CPU32, a memory33, a storage device34and a communication unit35which are mutually connected via an internal bus31. Since the CPU32, the memory33, the storage device34and the communication unit35have the same functions and configuration as the CPU21, the memory22, the storage device23and the communication unit24of the data analyzing device12described above with reference toFIG. 2, the detailed explanation thereof is omitted. Note that the memory33of the sales information processing apparatus5stores the unsigned consumer list creation program36and the contract conclusion processing program37described later.

(2) Flow of Data Analytical Processing in Energy Selling System

The data analyzing function equipped in the data analyzing device12of the analysis company11is now explained. In the case of this embodiment, the data analyzing device12is equipped with a data analyzing function of analyzing the past electrical power demand based on the load data and attribute information of each consumer8accumulated in the meter data collection server3of the energy company2, generating information that is useful for the energy company2and each consumer8, and providing the generated information to the energy company2.

FIG. 4shows the flow of the series of processing that is executed by the energy selling system1in relation to the data analyzing function. In the energy selling system1, the equipment control terminal9(FIG. 1) of each consumer8periodically sends, to the meter data collection server3(FIG. 1) of the energy company2, load data representing the power usage for each unit time (for example, 30 minutes) (SP1). Furthermore, the meter data collection server3periodically sends, to the data analyzing device12of the analysis company11, load data of a prescribed time period (for instance, this is one year, and hereinafter referred to as the “analyzing period”) among the load data of each consumer that is being stored and retained, and the attribute information of each consumer8(SP2).

When the data analyzing device12receives the load data and attribute information of each consumer8from the meter data collection server3, the data analyzing device12executes load data clustering processing of classifying the load data into a plurality of clusters in consumer units (SP3). Consequently, each consumer8is also classified into a group that is associated with the cluster to which the corresponding load data belongs (this is hereinafter referred to as the “consumer class”). Furthermore, the data analyzing device12thereafter generates, for each cluster, outline data representing the load curve which represents the transition of the power usage in a standard analyzing period of the consumer8belonging to the consumer class corresponding to that cluster (this is hereinafter referred to as the “standard load curve”) (SP4). Note that normalization processing in which the load data takes the values of average 0, distribution 1 may also be executed prior to the processing of the load data clustering. Consequently, it will be possible to obtain a group of consumers associated with a cluster to which load curves of similar figures belong, irrespective of the amount of power reception of the consumers. For example, it is possible to obtain a group of consumers in which the amount of power reception increased during a specific time frame during the evening.

Subsequently, the data analyzing device12executes consumer data analytical processing of analyzing the power usage status of each consumer8based on the load data and attribute information of each consumer8acquired in step SP2(SP5). Specifically, the data analyzing device12generates, for each consumer8, outline data of the load curve representing the transition of the power usage within the analyzing period, and sends the generated outline data, as the estimated value of the future power usage of the corresponding consumer8, to the consumer information providing server4of the energy company2. Furthermore, the data analyzing device12determines the recommended rate menu and equipment for each consumer8and executes other analytical processing based on the generated outline data of the load curve within the analyzing period for each consumer8, and sends the decision result and analysis result thereof to the consumer information providing server4of the energy company2.

Here, the data analyzing device12calculates, for each consumer8based on the load data and attribute information of each consumer8acquired in step SP2, supplementary information such as the total power usage within the analyzing period, the power usage during the tightening of electric power supply of the energy company2such as in times of a planned power outage, and increase in the power consumption relative to the temperature, and stores the supplementary information calculated for each consumer8as statistical information in the class attribute information table30B (FIG. 7) described later with reference toFIG. 7.

Subsequently, the data analyzing device12generates, based on the attribute information for each consumer8acquired in step SP2and the processing result of the load data clustering processing of step SP3, a diagnostic decision tree as shown inFIG. 14for classifying new consumers8from which load data has not yet been acquired and existing consumers8from which load data cannot be acquired since an equipment control terminal9(FIG. 1) has not been installed (these consumers8are hereinafter collectively referred to as the “new consumers8”) into one of the consumer classes (SP6). Note that, in the case of this embodiment, ID 3 (Iterative Dichotomiser 3) is used as the algorithm for creating the diagnostic decision tree as described above, but an algorithm other than ID 3 may also be used. Details regarding the diagnostic decision tree will be described later.

Meanwhile, when the data analyzing device12subsequently receives the attribute information of the new consumers8from the meter data collection server3(SP7), the data analyzing device12uses the diagnostic decision tree created in step SP6to make a diagnosis regarding to which consumer class the new consumer8belongs (more accurately, estimates to which cluster the load data of the new consumer8belongs) (SP8).

Subsequently, the data analyzing device12sends, to the consumer information providing server4of the energy company2, the outline data of the foregoing standard load curve corresponding to the consumer class diagnosed as being the consumer class to which that new consumer8belongs as the estimation result of the future power usage of that new consumer8. Furthermore, the data analyzing device12determines the rate menu and equipment recommended to that new consumer8and executes other analytical processing based on the outline data of the standard load curve, and sends the decision result and analysis result thereof to the consumer information providing server4of the energy company2. Furthermore, the data analyzing device12estimates the supplementary information of that new consumer8, and stores the estimated supplementary information as statistical information in the class attribute information table30B (FIG. 7) described later (SP9).

Consequently, when the existing or new consumer8thereafter uses one's own information I/O terminal10(FIG. 1) and accesses the consumer information providing server4of the energy company2and requests the provision of information related to that consumer8(SP10), the consumer information providing server4of the energy company2displays the load curve obtained based on the outline data of the standard load curve of that consumer8, which was provided by the data analyzing device12in step SP5or step SP9, as the estimation result of the future power usage, and provides information such as the recommended rate menu and equipment determined by the data analyzing device12to that consumer8(SP11).

Meanwhile, if the consumer8who acquired information such as the recommended rate menu and equipment in step SP11requests, for instance, the conclusion of a contract for the recommended rate menu based on the acquired information, or the new consumer8that has not yet concluded a contract with the energy company2operates one's own information I/O terminal and applies for the conclusion of a new energy sales contract with the energy company2, such request is sent from that information I/O terminal to the sales information processing apparatus5of the energy company2(SP12).

Subsequently, in response to the received request, the sales information processing apparatus5of the energy company2executes processing for concluding a new energy sales contract based on the requested rate menu regarding existing consumers8, and executes processing required for concluding the requested energy sales contract regarding new consumers8who have not yet concluded a contract (SP13).

As means for the data analyzing device12to execute the processing of step SP3to step SP6, step SP8and step SP9based on the data analyzing function described above, the memory22(FIG. 2) of the data analyzing device12stores, as shown inFIG. 2, a clustering processing program25, a standard load curve outline data generation processing program26, a diagnostic decision tree generation processing program27and a consumer data analytical processing program28, and the storage device23of the data analyzing device12stores an energy company transmission data database29, and a class and cluster information database30.

The clustering processing program25is a program with a function of classifying the load data for each consumer that is periodically sent from the meter data collection server3of the energy company2into a plurality of clusters in consumer units. Furthermore, the standard load curve outline data generation processing program26is a program with a function of generating the outline data of the standard load curve of each cluster based on the processing result of the clustering processing program25, and the diagnostic decision tree generation processing program27is a program with a function of generating the foregoing diagnostic decision tree (FIG. 14) based on the processing result of the clustering processing program25.

Furthermore, the consumer data analytical processing program28is a program with a function of determining the future power usage and the recommended rate menu and equipment of the existing consumers based on the load data and attribute information of those existing consumers8which are periodically provided by the meter data collection server3of the energy company2, determining the consumer class of new consumers8based on the diagnostic decision tree generated by the diagnostic decision tree generation processing program27, and determining the future power usage and the recommended rate menu and equipment of those new consumers8.

Meanwhile, the energy company transmission data database29is a database that is used for storing and retaining the load data and attribute information of each consumer8which are provided by the meter data collection server3of the energy company2. The energy company transmission data database29takes on a table configuration configured from a consumer ID column29A, an item column29B and a value column29C as shown inFIG. 5.

Moreover, the consumer ID column29A stores the identification number of each consumer8, and the item column29B stores the item name of the load data and attribute information of the corresponding consumer8(“load data” with regard to “load data”, and “monthly integral power usage”, “location (geography)” and “rate menu” with regard to attribute information). Furthermore, the value column stores the value of the corresponding item of the corresponding consumer.

Accordingly, in the case ofFIG. 5, with regard to the consumer8having an identification number of “1”, the power usage (“load data”) for each unit time is “100.0 kW, 150 kW, . . . ”, the monthly integral power usage is “10,000 kWh, 14,000 kWh, . . . ”, latitude and north latitude are located at the positions of “A+30.0, B+130.0” (A, B are predetermined constants), the current rate menu is “late night discount type A”, the system feeder number is “101”, the contracted power reception per hour is “500 kW”, “CHP: 10 kW” is owned as the equipment, the cluster number of the cluster of the load data classified based on the foregoing clustering is “170”, and the consumer's own consumer class is “70”.

Moreover, the class and cluster information database30(FIG. 2) is configured from the cluster attribute information table30A shown inFIG. 6and the class attribute information table30B shown inFIG. 7.

Among the above, the cluster attribute information table30A is a table that is used for managing the respective clusters obtained by performing clustering processing to the load data of the respective consumers8in step SP3ofFIG. 4, and is configured, as shown inFIG. 6, from a cluster ID column30AA, an item column30AB and a value column30AC.

The cluster ID column30AA stores the identification number that is assigned to each cluster obtained based on the clustering processing, and the item column30AB stores the item name of each piece of information related to the corresponding cluster (“number of sample consumers”, “sample consumer ID list”, “cluster center”, “total power share” and “total consumption share”). Furthermore, the value column30AC stores the value of the corresponding item of the corresponding consumer8.

Accordingly, in the case ofFIG. 6, with regard to the cluster assigned with the identification number of “1”, the number of consumers belonging to that cluster is “4000”, the ID of those consumers8is “1, 2, 4, . . . , 5780”, the coordinates of the cluster center of that cluster are “[0, 100, 100, 0, 0, 200, . . . , 0]”, the ratio of the total contracted power reception of the respective consumers8belonging to that cluster relative to the total contracted power reception of all consumers8is “10%”, and the ratio of the total power consumption of the respective consumers8belonging to that cluster relative to the total power consumption of all consumers8is “10%”.

Moreover, the class attribute information table30B is a table that is used for managing the respective consumer classes to which the consumers8were distributed, and is configured, as shown inFIG. 7, from a class ID column30BA, an item column30BB and a value column30BC.

The class ID column30BA stores the identification number assigned to each consumer class, and the item column30BB stores the item name of each piece of information related to the corresponding consumer class (“standard load curve”, “cluster number list”, “consumer ID list”, “statistical information list” and “valid energy saving investment list”). Furthermore, the value column30BC stores the value of the corresponding item of the corresponding consumer class.

Accordingly, in the case ofFIG. 7, with regard to the consumer class assigned with the identification number of “1”, the identification number of the corresponding cluster is “1”, and the standard load curve of that consumer class is shown as the product of performing inverse Fourier transformation to the cluster center “[0, 100, 100, 0, 0, 200, . . . , 0]” of the corresponding cluster. Furthermore, inFIG. 7, the consumer8having an ID of “4” is the only consumer belonging to that consumer class, and, as the overall statistical information (supplementary information) of that consumer8belonging to that consumer class which was obtained by analyzing the load data of that consumer8, the total power usage is “1000” MW, the power usage during the tightening of electric power supply of the energy company2such as in times of a planned power outage is “5000” MW, the increase in power usage relative to the temperature is “500” MW, the equipment recommended to the consumer8belonging to that consumer class is “CHP (Combined Heat Power)” of “100 kW”, and the recommended rate menu is “nighttime contract A”. Note that the statistical information (supplementary information) regarding the consumer class assigned with an identification number is not limited to the information described above, and information related to the increase in the power usage relative to the various diffusion indexes such as the final demand inventory index, new openings, unemployment rate, and corporate tax revenue.

(3) Various Types of Processing Related to Data Analyzing Function

The specific processing contents of the various types of processing to be executed by the data analyzing device12of the analysis company11and the sales information processing apparatus5of the energy company2in relation to the foregoing data analyzing function are now explained. Note that, in the ensuing explanation, while the processing entity of the various types of processing is explained as a “program” as needed, in effect, it goes without saying that the CPU21(FIG. 2) of the data analyzing device12and the CPU32(FIG. 3) of the sales information processing apparatus5executes the processing based on the “program”.

(3-1) Load Data Clustering Processing

FIG. 8shows the specific processing contents of the processing that is executed by the data analyzing device12of the analysis company11in step SP3of the series of processing described above with reference toFIG. 4(this processing is hereinafter referred to as the “load data clustering processing”). The load data clustering processing is processing for classifying the respective consumers8into several clusters in which the feature quantity, such as the outline of the load data, is substantially similar, and calculating the cluster center as information representing the respective clusters.

When the data analyzing device12receives the load data and attribute information of each consumer8from the meter data collection server3of the energy company2, the data analyzing device12starts the load data clustering processing shown inFIG. 8, and foremost obtains a obtains a cluster center set {Ck:k=1, 2 . . . , N} of each cluster when the acquired load data is classified into 1 to M (M is number of consumers8) clusters in consumer units (SP20).

Specifically, based on k-means clustering, the data analyzing device12distributes the load data to the respective clusters while sequentially changing the cluster number N from 1 to M and obtains the cluster center set {Ck} of each cluster at such point in time; for instance, cluster center set {C1} of that cluster when the load data is classified into one cluster in consumer units, cluster center set {C1, C2} of each cluster when the load data is classified into two clusters in consumer units, cluster center set {C1, C2, C3} of each cluster when the load data is classified into three clusters in consumer units, . . . .

Next, the data analyzing device12executes cluster number adequacy evaluation value calculation processing of calculating the index for evaluating which cluster number N would be adequate (this index is hereinafter referred to as the “adequacy evaluation value”) based on the processing result of the foregoing clustering processing (SP21). In the case of this embodiment, the data analyzing device12calculates, as the adequacy evaluation value, the intra-cluster relevance representing the degree of unity of the load data in each of the clusters, and the inter-cluster average degree of separation representing the degree of separation of the clusters.

Thereafter, the data analyzing device12decides the optimal cluster number based on the intra-cluster relevance and the inter-cluster average degree of separation calculated in step SP21(SP22).

Based on the foregoing processing, the load data of each consumer8is classified into the cluster of an appropriate cluster number in consumer units, and consequently each consumer8is also classified into the consumer class of an appropriate class number.

FIG. 9shows the specific processing contents of the clustering processing to be executed in step SP20of the load data clustering processing described above with reference toFIG. 8. The clustering processing is executed by the clustering processing program25(FIG. 2).

In effect, when the clustering processing program25receives the load data and attribute data for the analyzing period of each consumer8from the meter data collection server3(FIG. 1) of the energy company2, the clustering processing program25starts the clustering processing shown inFIG. 9, and foremost assumes the cluster number N {N=1, 2, . . . , M (M is number of all consumers)} of the consumer8to be one among 1 to M (SP30), and sets the initial value of the cluster center set {Ck: k=1, 2, . . . , N} of each cluster at that point in time (SP31). The initial value may be any value and, for instance, the execution result of the previous clustering processing may be used.

Next, the clustering processing program25calculates the feature quantity Si{si,1, si,2, . . . si,t} of the load data for the analyzing period of each consumer i {i=1, 2, . . . , M} (SP32). In this embodiment, since load data is clustered from the daily, weekly and annual electrical power demand periodicity, the result of performing Fourier transformation to the load data for the analyzing period of each consumer i is used as the feature quantity Siof that consumer i. Moreover, the foregoing feature quantity may be information other than the result of performing Fourier transformation so as long as it is information which indicates the features of the load data of each consumer i; for instance, the foregoing feature quantity may be the time series data of the load data, or statistical information of the load data such as the average value (average demand) or the maximum value (maximum demand) of the load data.

Next, the clustering processing program25selects one unprocessed consumer i among the respective consumers i for which the load data was received (SP33). The clustering processing program25subsequently calculates, with regard to that consumer i, the Euclidean distance of the cluster center set {Ck} of each cluster set in step SP31and the feature quantity Siof the load data of that consumer i acquired in step SP32, and distributes the load data of that consumer i to the cluster set Xkof the nearest cluster (SP34).

Next, the clustering processing program25determines whether the processing of step SP34has been executed for all consumers i (SP35), and returns to step SP33upon obtaining a negative result. The clustering processing program25thereafter repeats the processing of step SP33to step SP35while sequentially switching the consumer i selected in step SP33to another unprocessed consumer i.

When the clustering processing program25eventually completes distributing the load data of each consumer i to the cluster set {Xk} of one of the clusters (SP35: YES), the clustering processing program25updates the cluster center set {Ck} of each cluster to the average value Sk_ave={Σisj,1/M, Σisi,2/M, Σisi,3/M, . . . } (provided iϵXk) of the feature quantity in the set of the consumer i belonging to the corresponding cluster (this is hereinafter referred to as the “cluster set”) {Xk} (SP36).

Thereafter, the clustering processing program25determines whether the variation in step SP36of the cluster center set {Ck} of at least one cluster is equal to or not less than a predetermined threshold (SP37). When the clustering processing program25obtains a positive result in this determination, the clustering processing program25returns to step SP33and thereafter repeats step SP33to step SP37.

When the variation of the cluster center Ckof all clusters eventually becomes less than the threshold (SP37: YES), the clustering processing program25stores the cluster center set {Ck} of each cluster and the cluster set {Xk} of each cluster at that point in time memory22(FIG. 2) (SP38).

Subsequently, the clustering processing program25determines whether the processing of step SP31to step SP38has been executed for all cluster numbers N (SP39). When the clustering processing program25obtains a negative result in this determination, the clustering processing program25thereafter repeats the processing of step SP30to step SP39while changing the cluster number N selected in step SP30to another unprocessed value (1 to M).

When the clustering processing program25eventually completes acquiring the cluster center set {Ck} and the cluster set {Xi} of the individual clusters in cases of assuming that the cluster number is respectively 1 to M (SP39: YES), the clustering processing program25ends the clustering processing.

(3-1-2) Cluster Number Adequacy Evaluation Value Calculation Processing

FIG. 10shows the specific processing contents of the cluster number adequacy evaluation value calculation processing to be executed in step SP21of the load data clustering processing described above with reference toFIG. 8. The cluster number adequacy evaluation value calculation processing is executed by the clustering processing program25. The cluster number adequacy evaluation value calculation processing is processing for deciding the cluster number by evaluating the results of clustering of the respective cluster numbers 1 to M calculated in the foregoing load data clustering processing based on a plurality of distance indexes such as the distance between the load data and the cluster center and the distance between the respective clusters.

In effect, when the clustering processing program25completes the clustering processing described above with reference toFIG. 9, the clustering processing program25starts the cluster number adequacy evaluation value calculation processing shown inFIG. 10, and foremost selects one among 1 to M (M is number of all consumers) as the cluster number N (SP40), and calculates, for each cluster, an error (this error is hereinafter referred to as the “intra-cluster error”) Ekbetween the feature quantity Siof each load data belonging to that cluster and the cluster center Ckof that cluster in cases of assuming that the cluster number N is the number selected in step SP40(SP41). Specifically, the clustering processing program25calculates the intra-cluster error of one cluster as the total value of the distance between the feature quantity Siof the individual load data belonging to that cluster and the cluster center Ckof that cluster.

Subsequently, the clustering processing program25calculates, based on the intra-cluster error ENcalculated in step SP41, the intra-cluster relevance E(N) for each cluster based on the following formula with the penalty coefficient for inhibiting an excessively large cluster number as a, and the feature quantity dimension number as D (SP42).
[Math 1]
E(N)=1/(EN+a×N×D)  (1)
The intra-cluster relevance E(N) is an index representing the degree of unity of the individual load data in the clusters as described above, and as the intra-cluster relevance E(N) is larger, the load data in that cluster is in a unified state. Moreover, in k-means clustering, as the cluster number N is larger the ENwill be smaller, and becomes smallest when N=M (number of consumers). Therefore, a penalty term a×N×D which is proportional to the number of parameters in the k-means clustering is added.

Next, the clustering processing program25calculates an interface g where the clusters can be separated by using a multi class support vector machine (SP43), and thereafter calculates the inter-cluster average degree of separation B(N) based on the following formula with the total value of the margin (distance) between the respective clusters as MN(SP44).
[Math 2]
B(N)=MNINC2(2)
The inter-cluster average degree of separation B(N) is an index representing the degree of separation of the clusters as described above, and as the inter-cluster average degree of separation B(N) is larger, the clusters are more separated. Moreover, the inter-cluster average degree of separation may be any index so as long as it is an index which will increase as the average distance between the respective clusters is larger, and the average value of the distance between the respective cluster center sets {Ck} may also be used as the index.

Thereafter, the clustering processing program25determines whether the calculation of the intra-cluster relevance E(N) and the inter-cluster average degree of separation B(N) regarding all cluster numbers N (1 to M) is complete (SP45). When the clustering processing program25obtains a negative result in this determination, the clustering processing program25thereafter repeats the processing of step SP40to step SP45while changing the cluster number N selected in step SP40to another unprocessed value (1 to M).

When the clustering processing program25eventually completes the calculation of the intra-cluster relevance E(N) and the inter-cluster average degree of separation B(N) regarding all cluster numbers N (1 to M) (SP45: YES), the clustering processing program25ends the cluster number adequacy evaluation value calculation processing.

(3-1-3) Optimal Cluster Number Decision Processing

FIG. 11shows the specific processing contents of the optimal cluster number decision processing to be executed in step SP22of the load data clustering processing described above with reference toFIG. 8. The optimal cluster number decision processing is also executed by the clustering processing program25.

In effect, when the clustering processing program25ends the cluster number adequacy evaluation value calculation processing described above with reference toFIG. 10, the clustering processing program25starts the optimal cluster number decision processing shown inFIG. 11, and foremost calculates, as a relevance optimal cluster number CL1, a minimum cluster number in which the ratio of the variation of the intra-cluster relevance E(N) relative to the variation of the cluster number becomes equal to or not greater than a predetermined first threshold as shown inFIG. 12Bbased on the value of each intra-cluster relevance E(N) in cases of assuming the cluster number N calculated in the cluster number adequacy evaluation value calculation processing to be 1 to M (SP50).

Next, the clustering processing program25calculates, as a degree of separation optimal cluster number CL2, a minimum cluster number in which the ratio of the variation of the inter-cluster average degree of separation B(N) relative to the variation of the cluster number becomes equal to or not greater than a predetermined second threshold as shown inFIG. 12Abased on the value of each inter-cluster average degree of separation B(N) in cases of assuming the cluster number N calculated in the cluster number adequacy evaluation value calculation processing to be 1 to M (SP51).

Here, if a value that is smaller than both the relevance optimal cluster number CL1and the degree of separation optimal cluster number CL2is decided as the current cluster number, since this means that the degree of unity of the load data in the individual clusters is small and the degree of separation of the clusters is also small, as shown inFIG. 12B, the difference between the outline of the standard load curve of each cluster and the load curve of the consumers belonging to that cluster will become too great.

Moreover, if a value that is larger than both the relevance optimal cluster number CL1and the degree of separation optimal cluster number CL2is decided as the current cluster number, since this means that the degree of unity of the load data in the individual clusters is small and the degree of separation of the clusters is also small, as shown inFIG. 12D, the difference in the outline of the standard load curve of the respective clusters is small and, therefore, it is difficult to extract the feature (use of electricity during daytime on weekdays, use of electricity during nighttime, or use of electricity during specific periods, etc.) of the electricity usage pattern of the consumer8belonging to the class corresponding to the respective clusters.

Meanwhile, if the cluster number of either the class relevance optimal cluster number CL1or the degree of separation optimal cluster number CL2is decided as the current cluster number, since the degree of unity of the load data in the individual clusters and the degree of separation of the clusters will be appropriate, as shown inFIG. 12C, the difference in the outline of the standard load curve of the respective clusters is appropriate and, therefore, it is easy to extract the feature of the electricity usage pattern of the consumer8belonging to the class corresponding to the respective clusters.

Thus, the clustering processing program25thereafter decides, as the current cluster number, one cluster number among the cluster numbers between the relevance optimal cluster number CL1calculated in step SP50and the degree of separation optimal cluster number CL2calculated in step SP51(SP52).

Specifically, when at least one cluster number exists between the relevance optimal cluster number CL1and the degree of separation optimal cluster number CL2, the clustering processing program25decides, as the current cluster number, the cluster number that is closest to the median value thereof or a cluster number that is selected randomly therefrom. Furthermore, when no cluster number exists between the relevance optimal cluster number CL1and the degree of separation optimal cluster number CL2, the clustering processing program25decides, as the current cluster number, one of either the relevance optimal cluster number CL1or the degree of separation optimal cluster number CL2, which is selected arbitrary or determined in advance.

Next, the clustering processing program25creates the cluster attribute information table30A (FIG. 6) and the class attribute information table30B based on the decision result of step SP52(SP53), and thereafter ends the cluster number decision processing.

(3-2) Standard Load Curve Outline Data Generation Processing

FIG. 13shows the specific processing contents of the standard load curve outline data generation processing to be executed by the data analyzing device12in step SP4of the series of processing described above with reference toFIG. 4. The standard load curve outline data generation processing is executed by the standard load curve outline data generation processing program26(FIG. 2).

In effect, when the clustering processing program25ends the load data clustering processing described above with reference toFIG. 8toFIG. 12, the standard load curve outline data generation processing program26starts the standard load curve outline data generation processing shown inFIG. 13, and foremost selects one cluster among the respective clusters of the cluster number that was decided in the foregoing optimal cluster number decision processing (FIG. 11) (SP60).

Next, the standard load curve outline data generation processing program26generates the outline data of the standard load curve regarding the cluster selected in step SP60(SP61). Specifically, the standard load curve outline data generation processing program26acquires the cluster center of the cluster selected in step SP60from the cluster attribute information table30A (FIG. 6). If necessary, the outline data of the standard load curve of that cluster can be generated by correcting the cluster center based on the error or maximum value of the load curve of the consumers8belonging to the cluster. The standard load curve may be corrected, for instance, when the maximum demand is required, by selecting a load curve among the load curves of the consumers belong to the cluster with a large amount of excess (error in the upward direction) from the cluster center and adding such excess to the cluster center, or adding the distribution of the load curve of the consumers belonging to the cluster as an error of the standard load curve.

Next, the standard load curve outline data generation processing program26stores the outline data of the standard load curve generated in step SP61in the class attribute information table30B (FIG. 7) (SP62), and thereafter determines whether the generation of the outline data of the standard load curve regarding all clusters is complete (SP63).

When the standard load curve outline data generation processing program26obtains a negative result in this determination, the standard load curve outline data generation processing program26thereafter repeats the processing of step SP60to step SP63while sequentially switching the cluster selected in step SP60to another unprocessed cluster.

When the standard load curve outline data generation processing program26eventually ends the generation of the outline data of the standard load curve regarding all clusters (SP63: YES), the standard load curve outline data generation processing program26ends the standard load curve outline data generation processing.

(3-3) Diagnostic Decision Tree Generation Processing

FIG. 14shows the schematic configuration of the diagnostic decision tree that is generated in step SP6of the series of processing described above with reference toFIG. 4. The diagnostic decision tree generation processing is processing for comprehensively generating a diagnostic decision tree for estimating the cluster, to which consumers are related, from the information respectively held by the energy company and the analysis company. As shown inFIG. 14, the diagnostic decision tree is configured from a first diagnostic decision tree TR1shown inFIG. 14(A)and a second diagnostic decision tree TR2shown inFIG. 14(B).

Among the above, the first diagnostic decision tree TR1is a diagnostic tree that is created based on only the load data and attribute information for the analyzing period of the respective consumers8provided from the meter data collection server3(FIG. 1) of the energy company2to the data analyzing device12. In effect, with the first diagnostic decision tree TR1, the contents of each node ND1are limited to those related to the power usage status of the consumers8or the attribution information of the consumers8that are recognized based on the load data, and the consumers8are associated with one of the clusters based on only the attribute information of the consumers8.

Furthermore, the second diagnostic decision tree TR2is a diagnostic tree that is created based on the supplementary information of the consumers8(attribute information of the consumer class to which the consumers8belong) obtained by analyzing the load data for the analyzing period of the existing consumers8, which is distributed to a leaf LF1, with the leaf LF1among the respective leaves LF1of the first diagnostic decision tree TR1in which the cluster of the distribution destination has not been identified (cluster of the distribution destination has not been decided as a single cluster) as the route. In effect, with the second diagnostic decision tree TR2, the contents of each node ND2are limited to those related only to the supplementation information of the consumers8, and the consumers8are associated with one of the clusters based on only the foregoing supplementary information.

FIG. 15shows the specific processing contents of the diagnostic decision tree generation processing to be executed in step SP6of the series of processing described above with reference toFIG. 4for creating the first and second diagnostic decision trees TR1, TR2described above. The diagnostic decision tree generation processing is executed by the diagnostic decision tree generation processing program27(FIG. 2).

In effect, when the diagnostic decision tree generation processing program27proceeds to step SP6of the series of processing described above with reference toFIG. 4, the diagnostic decision tree generation processing program27starts the diagnostic decision tree generation processing shown inFIG. 15, and foremost refers to the energy company transmission data database29(FIG. 5) and the cluster attribute information table30A (FIG. 6), and generates the first diagnostic decision tree TR1(SP70).

Next, the diagnostic decision tree generation processing program27determines whether there is a leaf LF1in the first diagnostic decision tree TR1in which the cluster of the distribution destination has not been decided as a single cluster (SP71). When the diagnostic decision tree generation processing program27obtains a negative result in this determination, the diagnostic decision tree generation processing program27ends the diagnostic decision tree generation processing.

Meanwhile, when the diagnostic decision tree generation processing program27obtains a positive result in the determination of step SP71, the diagnostic decision tree generation processing program27refers to the class attribute information table30B and generates the second diagnostic decision tree TR2(SP72), and thereafter ends the diagnostic tree decision processing.

FIG. 16shows the specific processing contents of the diagnostic decision tree generation processing program27in step SP70of the diagnostic tree decision processing (FIG. 15). When the diagnostic decision tree generation processing program27proceeds to step SP70of the diagnostic tree decision processing, the diagnostic decision tree generation processing program27starts the first diagnostic decision tree creation processing shown inFIG. 16, and foremost acquires the cluster information {Xk} decided in the foregoing clustering processing and the optimal cluster number decision processing, and the attribute item {Ai} of the respective consumers (SP80), and starts the processing from the route (uppermost node) of the diagnostic decision tree to be generated (SP81).

Next, the diagnostic decision tree generation processing program27determines whether the attribute item {Ai} acquired in step SP80is an empty set (SP82). When the diagnostic decision tree generation processing program27obtains a negative result in this determination, the diagnostic decision tree generation processing program27causes that node to become a leaf (terminal node) and then proceeds to step SP92(SP83).

Meanwhile, when the diagnostic decision tree generation processing program27obtains a negative result in the determination of step SP82, the diagnostic decision tree generation processing program27calculates the average information volume H ({Xk}) of the cluster in all consumers included in the current cluster information based on the following formula (SP84).

[Math⁢⁢3]H⁡({Xk})=-∑k⁢(Xk/∑n⁢Xn)⁢log⁡(Xk/∑n⁢Xn)(3)
However, |Xk| shall be the number of consumers included in the cluster k.

The average information volume H ({Xk}) of the cluster will be large when the variation in the cluster to which the input consumer8belongs is great and take on a large value when the deviation is great. The average information volume H ({Xk}) will be 0 when all input consumers8only belong to one cluster.

Next, the diagnostic decision tree generation processing program27selects one attribute item Aifrom the input attribute item {Ai} (SP85), and calculates the cluster set {YK,j} in the subset of the consumers8having the values ai,1, ai,2, ai,3, . . . contained in the selected attribute item Aias their attribute values, and the number of consumers |YK,j| thereof (SP86).

Subsequently, the diagnostic decision tree generation processing program27calculates the information gain IG (Ai) regarding the selected attribute item based on the following formula (SP87).

⁢[Math⁢⁢4]IG⁡(Ai)=H⁡({Xk})-∑j⁢∑k⁢(Yk,j/∑n⁢Yn,j)⁢log((Yk,j/∑n⁢Yn,j)(4)
Note that the information gain IG (Ai) is a parameter which represents to what level the variation in the cluster to which the consumers8belong will decrease when the consumers8are divided into subsets based on the attribute values ai,1, ai,2, ai,3, . . . .

Thereafter, the diagnostic decision tree generation processing program27determines whether the calculation of the information gain IG (Ai) regarding all input attribute items {Ai} is complete (SP88), and returns to step SP85upon obtaining a negative result. the diagnostic decision tree generation processing program27thereafter repeats the processing of step SP85to step SP88.

When the diagnostic decision tree generation processing program27eventually obtains a positive result in step SP88as a result of completing the calculation of the information gain IG (Ai) regarding all attribute items {Ai}, the diagnostic decision tree generation processing program27sets the attribute item Ai* with the greatest information gain IG (Ai) among the information gains IG (Ai) obtained in the foregoing processing as the current node of the diagnostic decision tree (SP89), and creates child nodes regarding each of the attribute values ai,1*, ai,2*, ai,3*, . . . of the attribute item Ai* (SP90).

Next, the diagnostic decision tree generation processing program27records, as the new cluster set {Xk} of the corresponding child node, the subset {YK,j} of the cluster regarding the consumer8having the attribute value ai,j* among the attribute values ai,1*, ai,2*, ai,3*, . . . of the foregoing attribute item Ai*. Moreover, the diagnostic decision tree generation processing program27records, as the new attribute set {Ai} of each child node, the subset {Ai\Ai*} of the attribute items excluding the attribute item Ai* in which the information gain IG (Ai) is greatest (SP91).

Next, the diagnostic decision tree generation processing program27determines whether the processing of step SP81to step SP91has been executed for all nodes (SP92), and, upon obtaining a negative result, thereafter repeats the processing of step SP81to step SP92while sequentially switching the node selected in step SP81to another unprocessed node.

When the diagnostic decision tree generation processing program27obtains a positive result in step SP92as a result of deciding the attribute item Ai* corresponding to all nodes, the diagnostic decision tree generation processing program27ends the first diagnostic decision tree generation processing.

Meanwhile,FIG. 17shows the specific processing contents of the diagnostic decision tree generation processing program27in step SP71of the diagnostic tree decision processing (FIG. 15) described above with reference toFIG. 15. The second diagnostic decision tree generation processing shown inFIG. 17is executed by the diagnostic decision tree generation processing program27upon creating the second diagnostic decision tree TR2(FIG. 14(B)) with the supplementary information of the consumers8as the attribute item {Bi} regarding the leaf LF1among the respective leaves LF1of the first diagnostic decision tree TR1generated inFIG. 16in which the cluster of the distribution destination has not been identified (cluster of the distribution destination has not been decided as a single cluster). Since the processing contents of the second diagnostic decision tree generation processing are the same as the first diagnostic decision tree generation processing described above with reference toFIG. 16other than including the supplementary information as an input item, the detailed explanation thereof is omitted.

Note that, while the first diagnostic decision tree generation processing described above with reference toFIG. 16and the second diagnostic decision tree generation processing described above with reference toFIG. 17were based on ID 3, any method may be used so as long as it can generate a decision capable of diagnosing the clusters and, moreover, the generation methods of the first and second diagnostic decision trees TR1, TR2do not need to be the same.

(3-4) Base Load Sales Contract Conclusion Processing

FIG. 18shows the processing contents of the base load sales contract conclusion processing to be executed by the sales information processing apparatus5(FIG. 3) that received an application for an energy sales contract for the base load of the power consumption from an unsigned consumer (this consumer is hereinafter referred to as the “unsigned consumer”)8in step SP13of the series of processing described above with reference toFIG. 4. The base load sales contract conclusion processing is executed by the contract conclusion processing program37(FIG. 3) stored in the memory33of the sales information processing apparatus5.

In effect, when the contract conclusion processing program37receives the application for an energy sales contract from the unsigned consumer8via the network14, the contract conclusion processing program37starts the base load sales contract conclusion processing shown inFIG. 18, and foremost sends a request to the effect that the future power usage of the unsigned consumer8should be estimated to the data analyzing device12, together with the attribute information of the unsigned consumer8that was input at the time of application. Consequently, in response to the foregoing request, the data analyzing device12uses the diagnostic decision tree described with reference toFIG. 14and determines the estimated consumer class to which the unsigned consumer8belongs based on the attribute information of the unsigned consumer8, reads the outline data of the standard load curve of that consumer class from the class attribute information table30B (FIG. 7), and sends the read outline data to the sales information processing apparatus5as the outline data of the load curve representing the estimated future power usage of that unsigned consumer8(SP120).

Next, the contract conclusion processing program37estimates the amount of power reception that the unsigned consumer8is most likely currently receiving from another energy company based on the outline data of the load curve of the unsigned consumer8acquired in step SP120(SP121), and calculates the minimum amount of power reception (this is hereinafter referred to as the “minimum power reception”) e (FIG. 21) of the unsigned consumer8(SP122). Otherwise, the consumer may transmit the maximum power reception (kW) of a prescribed time period and the purchased electric power (kWh) of a prescribed time period for which a contract has been concluded with another energy company to the processing apparatus of the analysis company11and the energy company2via the information I/O terminal10, or the estimation may be performed by multiplying the building area or the number of power reception equipment of the consumer by a predetermined coefficient. Consequently, even in cases where the outline data of the load curve is obtained from normalized load data, it is possible to estimate the load data of the unsigned consumer from the outline data of the load curve by using the two values of the maximum power reception and the purchased electric power of a prescribed time period, and thus calculate the minimum power reception e of that unsigned consumer.

Subsequently, the contract conclusion processing program37determines whether the minimum power reception e calculated in step SP122is greater than a predetermined threshold (SP123). When the contract conclusion processing program37obtains a negative result in this determination, the contract conclusion processing program37sends a message to the effect that the energy sales contract will not be concluded to the unsigned consumer, and thereafter ends the base load sales contract conclusion processing. Accordingly, in the foregoing case, the energy sales contract is not concluded with the unsigned consumer.

Meanwhile, when the contract conclusion processing program37obtains a positive result in the determination of step SP123, the contract conclusion processing program37executes predetermined processing for concluding the energy sales contract with the unsigned consumer (SP124), instructs the traded product information processing apparatus6to procure the power of the minimum power reception e calculated in step SP122(SP125), and thereafter ends the base load sales contract conclusion processing.

(4) Unsigned Consumer List Presentation Processing

The unsigned consumer list presentation processing to be executed by the energy selling system1separate from the series of processing described above with reference toFIG. 4is now explained. Note that, in the ensuing explanation, let it be assumed that the data analyzing device12is retaining a list of all electricity consumers (this list is hereinafter referred to as the “list of all consumers”) receiving electric power supply from one of the plurality of energy companies2, and the attribute information of all registered electricity consumers is included in the list of all consumers.

InFIG. 20A, the area framed with a solid line L1shows the electrical energy in a certain period in the future which has been procured by the power generation/procurement office of the energy company2, and the area framed with a broken line L2shows the electrical power demand in that period which is estimated by the data analyzing device12of the analysis company11. Note that the term “procured electrical energy” as used herein refers not only to the electrical energy that was reserved for purchase by the power generation/procurement office through the electric power exchange, it also indicates the total value of the electrical energy which also includes the power generation amount instructed by the power generation/procurement office to the power generation facility in cases where the energy company2is a power generation facility. The same applies in the ensuing explanation. InFIG. 20A, the electrical energy of the areas AR1, AR2indicated with diagonal lines may become the excess electrical energy.

Thus, the sales information processing apparatus5of this embodiment is equipped with an unsigned consumer list presentation function for presenting a list (this list is hereinafter referred to as the “unsigned consumer list”) which includes, as candidates with whom an energy sales contract should be newly concluded, the unsigned consumers who use electricity in a pattern shown inFIG. 18Bwhich is the same as the foregoing excess electrical energy.

FIG. 21shows the processing routine of the unsigned consumer list presentation processing to be executed in the sales information processing apparatus5in relation to the foregoing unsigned consumer list presentation function. The unsigned consumer list presentation processing is executed by the unsigned consumer list creation program36stored in the memory33(FIG. 3) of the sales information processing apparatus5.

In effect, the unsigned consumer list creation program36starts the unsigned consumer list presentation processing shown inFIG. 21according to prescribed operations performed to the sales information processing apparatus5(FIG. 1), and foremost acquires data representing the procured electrical energy for a prescribed time from the traded product information processing apparatus6(FIG. 1) (SP130), and compares the procured electrical energy recognized based on the acquired data and the future demand electrical energy in the energy company2that is notified from the data analyzing device12as described above.

Subsequently, the unsigned consumer list creation program36calculates the difference between the procured electrical energy and the estimated value of the future demand electrical energy based on the comparative result, and thereby calculates the excess electrical energy that is expected to arise within the foregoing prescribed time period (SP131). Furthermore, the unsigned consumer list creation program36thereafter sends a request for creating the foregoing unsigned consumer list (this request is hereinafter referred to as the “unsigned consumer list creation request”) to the data analyzing device12of the analysis company2, together with the data of the excess electrical energy calculated in step SP131(SP132).

Consequently, the data analyzing device12that received the unsigned consumer list creation request detects a consumer class having a standard load curve that is similar to the temporal variation of the excess electrical energy (this consumer class is hereinafter referred to as the “specific consumer class”) by referring to the class attribute information table30B (FIG. 7). Furthermore, the data analyzing device12classifies the unsigned consumers8among the consumers8registered in the foregoing list of all consumers into one of the consumer classes by using the diagnostic decision tree described above with reference toFIG. 14. Furthermore, the data analyzing device12creates the foregoing unsigned consumer list including the respective unsigned consumers8classified into the specific consumer class as sales promotion destination candidates, and sends the data of the created unsigned consumer list (this data is hereinafter referred to as the “unsigned consumer list data”) to the sales information processing apparatus5. Moreover, a specific consumer class having a standard load curve that is similar to the temporal variation of the excess electrical energy may also be obtained based on identification processing using the data of the foregoing cluster center set {Ck: k=1, 2, . . . , N} and the k-means clustering.

The unsigned consumer list creation program36of the sales information processing apparatus5that received the unsigned consumer list data displays the unsigned consumer list based on the unsigned consumer list data (SP133), and thereafter ends the unsigned consumer list presentation processing.

(5) Summation of Electrical Energy Sold

The summation processing of the electrical energy sold to be executed in the energy selling system1separate from the series of processing described above is now explained. The sales information processing apparatus5may add, as the electrical energy sold, the generated outline data of the load curve within the analyzing period for each consumer8, calculate the power generation/procurement required in the respective time frames within the analyzing period, and perform processing of instructing the traded product information processing apparatus6to procure power.

(6) Effect of this Embodiment

With the energy selling system1of this embodiment described above, since the cluster number to be used upon clustering the load data of the respective consumers8is decided based on the intra-cluster relevance representing the degree of unity of the load data in the individual clusters and the inter-cluster average degree of separation representing the degree of separation of the clusters and the load data is clustered in the decided cluster number as described above with reference toFIG. 11, the load data of the respective consumers8can be clustered to a cluster number that matches the actual situation.

Consequently, according to the energy selling system1, it is possible to appropriately classify the consumers8into a plurality of consumers classes, and the subsequent data analytical processing can be performed reliably based on the classification result.

Furthermore, with the energy selling system1of this embodiment, since a diagnostic decision tree as shown inFIG. 14is created with the final unit of profiling of the individual consumers8as the segmentation result based on the attribute information and supplementary information of the consumers8, the new consumers8from which the load data has not been acquired and the existing consumer8(new consumers8) from which the load cannot be acquired because the equipment control terminal9(FIG. 1) has not been installed can also be classified into appropriate clusters.

(7) Effect of this Embodiment

Note that, while the foregoing embodiment explained a case of applying the present invention to the energy selling system1configured as shown inFIG. 1, the present invention is not limited thereto, and the present invention can be broadly applied to data analyzing systems of various types of configurations which perform various types of data analysis based on the load data and attribute information of the respective consumers who receive electric power supplied from an energy company.

Moreover, while the foregoing embodiment explained a case of applying the k-means clustering as the means upon obtaining the cluster center Ckof the respective clusters in cases of respectively classifying the load data in 1 to M (M is number of consumers8) clusters in consumer units, the present invention is not limited thereto, and various other means may be broadly applied.

INDUSTRIAL APPLICABILITY

The present invention can be broadly applied to a data analyzing system for performing various types of data analysis based on load data and attribute information of the respective consumers who receive electric power supply from an energy company.

REFERENCE SIGNS LIST

1: energy selling system2: energy company3: meter data collection server4: consumer information providing server5: sales information processing apparatus6: traded product information processing apparatus7: corporate information processing apparatus8: consumer9: equipment control terminal10: information I/O terminal11: analysis company12: data analyzing device21,32: CPU25: clustering processing program26: standard load curve outline data generation processing program27: diagnostic decision tree generation processing program28: consumer data analytical processing program29: energy company transmission data database30: class and cluster information database30A: cluster attribute information table30B: class attribute information table36: unsigned consumer list creation program37: contract conclusion processing programTR1, TR2: diagnostic decision tree