Sensor placement management system and sensor placement management method

Abstract

To provide a sensor placement management system capable of achieving without installing sensors on electrical equipment that consumes general power.SOLUTION: The sensor placement management system includes: an arithmetic unit that performs arithmetic processing; and a storage unit that to which the arithmetic device is accessible. The sensor placement management system also includes: a power consumption target attribute calculation unit that calculates the attributes of the experimental power consumption target that have a high correlation with the electrical signal by correlation analysis between business information for experimental power consumption and electrical signals measured by the electrical sensor installed on the electrical equipment subject to power consumption in the experiment; a candidate power consumption target calculation unit that performs clustering of the experimental power consumption target and the general power consumption target using the attribute information and calculates the order of candidates for new experimental power consumption targets for general power consumption targets; and an output unit that outputs candidates for experimental power consumption targets with low ranks.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to power analysis, and more particularly to techniques for managing sensor placement in power data collection. [Background technology]

[0002] 2. Description of the Related Art Power analysis services are available that obtain information about the current and power consumed in buildings such as commercial stores, factories, and homes, and suggest improvements to power consumption within the buildings.

[0003] In other words, by utilizing time-series data on the power, current, and voltage generated or consumed by electrical equipment operating within a building (hereafter simply referred to as electrical signals), it is possible to understand the actual operation status of that electrical equipment, which can be used to plan measures for saving electricity and improving operations. For example, by collecting and analyzing the power consumption of lighting equipment in a home, it is possible to detect whether the lights have been left on. Similarly, by collecting and analyzing the power consumption of cooking equipment in a retail store, it is possible to estimate the time when products were cooked, which is information useful for inventory management.

[0004] Power analysis services need to be provided cheaply and quickly, and the challenge for this is to collect power data from the electrical equipment operating within each building.

[0005] One way to realize such a service is to first install a sensor for each electrical device operating in a building and collect electrical signals.However, if a sensor is installed for each electrical device to be analyzed, the number of sensors required increases depending on the number of electrical devices, which creates a problem of increasing costs for sensor installation work and the collection and storage of electrical signals.

[0006] The other method uses NILM sensors that measure the power of the main circuit at the entrance to the power building and a probabilistic generation model to estimate the operating status of the electrical equipment in that building. This method using NILM sensors is inexpensive because it requires only one or two power sensors per store, but in order to develop a probabilistic generation model, it is necessary to install an electrical sensor for each piece of electrical equipment and collect data.

[0007] Therefore, it is desirable to provide power analysis services to a small number of buildings by applying electrical sensors to each electrical device, collect data, and then use that data to create a probability generation model with reasonable accuracy, which can then be deployed to a large number of buildings using NILM sensors.

[0008] The following patent documents are included as background art in this technical field: Patent Document 1 (JP 2013-213825 A) describes a method for monitoring an electric device, including the steps of acquiring data representing a sum of electric signals of two or more electric devices including a first electric device, processing the data using a probabilistic generation model to generate an estimate of an operating state of the first electric device, and outputting the estimate of the electric signal of the first electric device, wherein the probabilistic generation model is a factor corresponding to the first electric device and has three or more states.

[0009] Furthermore, Patent Document 2 (JP 2013-218715 A) describes an electrical appliance estimation device that includes a data acquisition means for acquiring time series data of the total value of the current consumption of a plurality of electrical appliances, and a parameter estimation means for determining model parameters when the operating states of the plurality of electrical appliances are modeled using a probabilistic model based on the acquired time series data.

[0010] Non-Patent Document 1 discloses a method for managing the relationship between the model of an electrical device and a probability generation model as meta-information. [Prior art documents] [Patent documents]

[0011] [Patent Document 1] Japanese Patent Application Laid-Open No. 2013-213825 [Patent Document 2] Japanese Patent Application Laid-Open No. 2013-218715 [Non-patent literature]

[0012] [Non-Patent Document 1] J. Kelly and W. Knottenbelt, "Metadata for Energy Disaggregation," in 2014 IEEE 38th International Computer Software and Applications Conference Workshops, Vasteras, Sweden, Jul. 2014, pp. 578-583. Summary of the Invention [Problem to be solved by the invention]

[0013] In order to estimate parameters using the techniques disclosed in Patent Documents 1 and 2, it is necessary to install sensors in advance in individual electrical devices to collect electrical signals as learning data, and then use this learning data to create in advance a probability generation model according to the model of the electrical device.

[0014] Depending on the building where the distribution board is installed, even if the same model of electrical equipment is used, the shape of the electrical signal may vary significantly if the installation environment and usage pattern of the electrical equipment are significantly different. In such cases, it is not possible to use a probabilistic generation model created in another building with a significantly different installation environment and usage pattern to estimate the operating status of the electrical equipment in that building. Furthermore, while it is possible to accurately estimate the operating status of electrical equipment if the probabilistic generation model is created and used in the same building, this requires installing electrical sensors on the electrical equipment in all buildings to collect learning data, which poses the problem of increasing the number of sensors required.

[0015] Here, buildings such as commercial stores, factories, and homes will be referred to as power consumption targets as a broader concept for the remainder of the explanation. A building in which sensors are installed for each electrical device used inside to collect learning data for creating a probabilistic generation model and the electrical signals are collected will be referred to as an experimental power consumption target, and a building in which only electrical signals from the main circuit are basically collected to utilize the probabilistic generation model will be referred to as a general power consumption target. A general power consumption target basically collects only electrical signals from the main circuit, but it may also collect electrical signals from other circuits.

[0016] By dividing the installation environments and usage patterns of electrical equipment in general power consumption objects into several types and using a probabilistic generation model created with experimental power consumption objects of the same type, it is possible to accurately estimate operating conditions. Furthermore, if many types can be represented with a small number of experimental power consumption objects, the number of sensors required to collect learning data can be reduced.

[0017] Therefore, the present invention aims to realize a process for extracting general power consumption objects that have different electrical equipment installation environments and usage patterns from existing experimental power consumption objects as candidates to be added to a group of experimental power consumption objects, without installing sensors on the electrical equipment that are general power consumption objects. [Means for solving the problem]

[0018] A representative example of the invention disclosed in the present application is as follows: That is, a sensor placement management system includes a computing device that executes computational processing and a storage device that can be accessed by the computing device, wherein the computing device: Electricity consumption target Business information including at least one of the address, location, and business hours of The business information correlated with the electrical signal measured from the experimental power consumption object, which is the power consumption object, is used as attribute information of the experimental power consumption object, and the electrical signal is measured from an electrical sensor installed in an internal electrical device. The experimental power consumption object and the general power consumption object are clustered using the Within the cluster to which the general power consumption object belongs, The present invention is characterized by having a candidate power consumption target calculation unit that calculates the ranking of candidates for new experimental power consumption targets, and an output unit that outputs the candidate experimental power consumption target with the lowest ranking. [Effects of the Invention]

[0019] According to one aspect of the present invention, it is possible to collect the learning data necessary to create a probabilistic generation model that estimates the operating state of an electrical device from an electrical signal of a main circuit using a small number of sensors. Problems, configurations, and effects other than those described above will become clear from the description of the following examples. [Brief explanation of the drawings]

[0020] [Figure 1] 1 is a diagram illustrating an example of a network configuration including a sensor placement support system according to an embodiment of the present invention. [Figure 2] FIG. 2 is a diagram illustrating an example of the hardware configuration of a sensor placement assistance server according to an embodiment of the present invention. [Figure 3] FIG. 2 is a diagram illustrating an example of the configuration of business information according to an embodiment of the present invention. [Figure 4] FIG. 2 is a diagram illustrating an example of the configuration of an electrical signal according to an embodiment of the present invention. [Figure 5] FIG. 10 is a diagram illustrating an example of the configuration of correlation attribute information according to an embodiment of the present invention. [Figure 6] FIG. 2 is a diagram illustrating an example of the configuration of store attribute information according to an embodiment of the present invention. [Figure 7] FIG. 4 is a diagram showing an example of the configuration of candidate store information according to an embodiment of the present invention. [Figure 8] FIG. 10 is a diagram illustrating an example of the configuration of similarity information according to an embodiment of the present invention. [Figure 9] FIG. 2 is a diagram illustrating an example of the configuration of recommendation information according to an embodiment of the present invention. [Figure 10] FIG. 10 is a diagram illustrating an example of a flowchart of a sensor placement assistance server according to an embodiment of the present invention. [Figure 11(a)] FIG. 10 is a diagram showing an example of the configuration of a recommendation screen according to an embodiment of the present invention. [Figure 11(b)] FIG. 10 is a diagram showing an example of the configuration of a recommendation screen according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0021] Specific embodiments of the present invention will be described below with reference to the drawings.

[0022] In the sensor placement management system of the embodiment of the present invention, a store (building) such as a convenience store will be described as an example of a power consumption target to be supported. A store uses various electrical equipment, such as lighting equipment, air conditioning equipment, and cooking equipment. As mentioned above, the power consumption target may include various facilities that are units of power consumption, such as factories, houses (detached houses and apartment buildings), and office buildings, in addition to the commercial store exemplified in the embodiment.

[0023] <System configuration> A system configuration according to an embodiment of the present invention will be described with reference to an example. <Example>

[0024] An embodiment of the present invention will now be described.

[0025] An embodiment of the present invention may be a sensor deployment management system that includes a computing device 403 that executes calculation processing and a storage device accessible by the computing device 403, and that includes a store attribute calculation unit 110 that calculates attributes of experimental stores that have a high correlation with an electrical signal by performing a correlation analysis between business information 211 of the experimental store and an electrical signal 212 measured from an electrical sensor installed on electrical equipment in the experimental store, a candidate store calculation unit 120 that clusters the experimental store and general stores using the attribute information and calculates a ranking of the general stores as candidates for new experimental power consumption targets, and an output unit that outputs candidate experimental stores with low rankings. Note that a high correlation may mean that the correlation is relatively high.

[0026] Fig. 1 is a diagram showing an example of the configuration of a sensor placement management system according to one embodiment of the present invention. The sensor placement management support system shown in Fig. 1 includes a sensor placement support server 100, a store information management server 200, and a display unit 300 as an output unit, and further includes a calculation device 403 that executes calculation processing (not shown), and a storage device 401 that can be accessed by the calculation device 403. The sensor placement support server 100 is connected so as to be able to communicate with the store information management server 200 and the display unit 300 used by a user of the sensor placement support system.

[0027] The store information management server 200 stores business information 211 and electrical signals 212 for each experimental store and general store, which will be described later. The sensor placement support server 100 has a store attribute calculation unit 110, a candidate store calculation unit 120, an electrical signal acquisition unit 130, and an experimental store recommendation unit 140. An example of the hardware configuration of the sensor placement support server 100 is shown in FIG. 2, and will be described in the hardware configuration example, which will be described later. Examples of the business information 211 and electrical signals 212 are shown in FIGS. 3 and 4, and will be described in the data structure example, which will be described later.

[0028] In order to collect learning data for creating a probabilistic generation model, a store building where sensors are installed for each electrical device used in the store and the electrical signals are collected is called an experimental store.Furthermore, a store building where only electrical signals from the main circuit are collected to use the probabilistic generation model is called a general store.

[0029] The store attribute calculation unit 110 acquires business information 211 for each store and electrical signals 212 measured for each electrical device in the store from the store information management server 200. The correlation analysis unit 111 calculates correlation attributes between the business information 211 and the electrical signals 212 acquired by the store attribute calculation unit 110 and stores the calculated correlation attributes in the correlation attribute information 112. The correlation analysis unit 111 calculates correlation coefficients between the business information and the electrical signals of the electrical devices using a known method, such as Pearson's correlation coefficient or point biserial correlation coefficient, and records a predetermined number of pieces of business information for each electrical device in the correlation attribute information 112. An example of the correlation attribute information 112 is shown in FIG. 5 and will be described later in the data structure example. The business information 211 and electrical signals 212 for which the correlation analysis unit 111 calculates correlation attributes may be for all of the business information 211 and electrical signals 212 acquired by the store attribute calculation unit 110. Alternatively, the correlation analysis unit 111 may calculate correlation attributes for only a portion of the business information 211 and electrical signals 212 required for the correlation attribute information 112. Furthermore, the predetermined number of pieces of task information to be recorded in the correlation attribute information 112 may be recorded in descending order of correlation coefficient for each electrical appliance.

[0030] The candidate store calculation unit 120 selects general stores that are candidates for new experimental stores, i.e., candidate stores. The candidate store calculation unit 120 acquires business information 211 of general stores and experimental stores related to the attributes described in the correlation attribute information 112 using the attribute information acquisition unit 121, and records this information in store attribute information 122. Here, the business information 211 of general stores and experimental stores acquired by the attribute information acquisition unit 121 may be business information 211 of all general stores and experimental stores, or may be information required for the clustering performed by the clustering unit 123. The clustering unit 123 performs clustering based on the attributes described in the store attribute information 122 and the distances between stores, and records information on the clusters to which each store belongs, nearby experimental stores, and the distance from the experimental store in candidate store information 124. An example of the store attribute information 122 is shown in FIG. 6 and will be described later in the data structure example.

[0031] The clustering calculation method may be any clustering method such as K-Means clustering, Gaussian mixture model, etc. The distance may be defined in any way such as Euclidean distance or Mahalanobis distance.

[0032] As a result of clustering based on the attributes described in the store attribute information 122 and the distances between stores in the clustering unit 123 described above, stores are divided into several clusters, for example, depending on the correlation between store attributes and the correlation between the distances between stores.

[0033] Various methods can be used to select general stores as new candidate experimental stores. For example, in candidate calculation example 1, the sum of the distances between each general store and other general stores in each cluster is calculated, and a predetermined number of general stores with the largest sum of distances are recorded in the candidate store information 124 as new experimental store candidates. The candidates recorded in the candidate store information 124 are sent to the display unit 300, which is an output unit, and displayed and output on the display unit 300 for use by the sensor placement manager. In this way, by selecting a new experimental store as a candidate, it becomes easier to create a probabilistic generation model for a distinctive store that is relatively dissimilar to other general stores, facilitating power consumption prediction using NILM sensors. In this example, a distinctive store that is relatively dissimilar to other general stores is selected as a candidate. Therefore, if this candidate is selected as an experimental store, it is expected that a more suitable probabilistic generation model can be used compared to when other general stores are selected as experimental stores, making it a useful candidate for a new experimental store.

[0034] As a second example of candidate selection, the sum of the distances between each general store and other general stores within each cluster may be calculated, and a predetermined number of general stores may be selected in order of smallest sum of distances and recorded in the candidate store information 124 as new experimental store candidates. The candidates recorded in the candidate store information 124 are sent to the display unit 300, which serves as an output unit, and displayed and output on the display unit 300 for use by the sensor placement manager. By selecting a new experimental store as a candidate in this way, it becomes easier to create a probabilistic generation model for a distinctive store that is relatively similar to other general stores, facilitating power consumption prediction using NILM sensors. In this example, a distinctive store that is relatively similar to other general stores is selected as a candidate. Therefore, if this candidate is selected as an experimental store, it is expected that the probabilistic generation model can also be used for many general stores similar to the candidate, resulting in high utilization efficiency as a candidate.

[0035] Furthermore, as candidate selection example 3, instead of calculating the sum of the distances between each general store and other general stores in each cluster in candidate selection example 1, the average distance between each general store and other general stores may be calculated. A general store whose average distance is greater than a predetermined distance from the general store to its nearest experimental store may be recorded in candidate store information 124 as a new experimental store candidate, and displayed and output by display unit 300, which is an output unit. With this configuration, a general store whose average distance between itself and other general stores is relatively farther than the nearest existing experimental store may be selected as a new experimental store candidate. Therefore, it is expected that a probability generation model more suited to the candidate may be used for other general stores in the vicinity of the new experimental store, making it useful as a new experimental store candidate.

[0036] The new method for calculating experimental store candidates described above may calculate candidate stores based on distance from the results of clustering. In addition to the method described above, various other methods can be used to calculate new experimental store candidates. For example, while the method described here selects candidates from general stores within the same cluster, it is also possible to select candidates across multiple clusters.

[0037] The results stored in the candidate store information 124 can be sent to the display unit 300, which is an output unit, and displayed, that is, output.

[0038] With the above-described configuration, the learning data required to create a probability generation model for estimating the operating state of an electrical device from the electrical signal of the main circuit can be collected with a small number of sensors. <Hardware configuration example>

[0039] Next, an example of the hardware configuration of the sensor placement assistance server 100 will be described.

[0040] Fig. 2 is a configuration diagram showing an example of the hardware configuration of the sensor placement assistance server 100. As shown in Fig. 2, the sensor placement assistance server 100 has a storage device 401, a computing device 403, a memory 404, and a communication device 405, and each unit is interconnected via a bus.

[0041] The storage device 401 may be configured with a non-volatile storage element such as an SSD (Solid State Drive) or a hard disk drive. The storage device 401 may store a program 402 that defines the operation of the arithmetic device 403, and various information used or generated by the arithmetic device 403, such as correlation attribute information 112, store attribute information 122, candidate store information 124, similarity information 142, and recommendation information 144. The memory 404 may be configured with a volatile storage element such as a RAM (Random Access Memory).

[0042] The arithmetic device 403 may be configured with a processor such as a CPU (Central Processing Unit). The arithmetic device 403 executes a program 402 stored in a storage device 401 by, for example, reading the program 402 into a memory 404. This enables the implementation of the functions of the correlation analysis unit 111, the attribute information acquisition unit 121, the clustering unit 123, the electrical signal acquisition unit 131, the similarity calculation unit 141, and the recommendation unit 143 shown in FIG. 1. The communication device 405 can communicate with an external device such as the store information management server 200 shown in FIG. 1 via a network 406.

[0043] <Data structure example> Next, an example of a data structure used in the embodiment will be described. In the following description of the data structure, the data is expressed in a tabular format. In this tabular format, each column of the table is called a field. The reference number of the field and the reference number representing the name of the data written in that field are the same to the extent that confusion does not occur. FIG. 3 is a diagram illustrating an example of the configuration of business information 211. Business information 211 illustrated in FIG. 3 may have fields 211a to 211e. Field 211a may store a store name, which is identification information for identifying a store. This store name may include not only the experimental store collecting electrical signals 212 from sensors installed on electrical equipment, but also other general stores. Field 211b may store an address indicating the location of the store. Field 211c may store location, which is information indicating the environment surrounding the store. Field 211d may store equipment configuration, which is a list of identification information for electrical equipment operating in the store. Field 211e may store business hours, which is information indicating the hours when the store is open. Each field in FIG. 3 is an example of business information in the present disclosure and is not limited to this example and may include any information that can correlate with electrical signals generated by electrical equipment. Furthermore, the relationship between the information stored in FIG. 3 and the fields is not limited to this, and any information can be stored in any field.

[0044] FIG. 4 is a diagram illustrating an example of the configuration of the electrical signal 212. The electrical signal 212 illustrated in FIG. 4 may have fields 212a to 212d. Field 212a may store a store name, which is identification information for identifying the store. The store name may include not only the experimental store collecting the electrical signal 212 from sensors installed on electrical devices, but also other general stores. Field 212b may store an equipment name, which is identification information for identifying the electrical devices in the store. The equipment name may include a main circuit, which is the sum of the electrical signals of the electrical devices in the store. Furthermore, if the store has multiple distribution boards, the multiple main circuits may be distinguished and stored in a format such as main circuit 1, main circuit 2, etc. Field 212c may store the time when the electrical signal of the device was measured. Field 212d stores the measured value of the electrical signal of the device at the time. Specifically, this electrical signal may be a measured value of a signal related to an electrical circuit, such as voltage, current, or power. The unit of the measured value may be, for example, V for voltage, A for current, or W for power. This electrical signal may be of two or more types, such as voltage and current, etc. This electrical signal can be measured at any interval and for any period depending on the application, such as every minute for a period of one day, or every hour for a period of one month.

[0045] FIG. 5 is a diagram showing an example of the configuration of the correlation attribute information 112. The correlation attribute information 112 shown in FIG. 5 may have fields 112a to 112b. In this example, field 112a may store an equipment name, which is identification information for identifying an electrical device. In this example, this equipment name does not include the main circuit, and only information identifying the electrical device is stored. Field 112b may store an attribute correlated with the equipment as an "attribute name." This attribute is composed of information contained in the column names of each field of the business information 211. For example, in the second row of the correlation attribute information 112 shown in table format in FIG. 5, the equipment name column contains "air conditioning" and the attribute name column contains "{location, business hours}." This configuration may be used because, as a result of a correlation analysis between each electrical device and an electrical signal performed by the correlation analysis unit 111 of the store attribute calculation unit 110, the equipment name "air conditioning" has a relatively high correlation with "location" and "business hours." 5, the third line has "freezer" in the equipment name column and "{location, address}" in the attribute name column. This may also mean that the equipment name "freezer" has a relatively high correlation with "location" and "address." Here, the number of attributes stored in the attribute name field 112b can be any number as needed.

[0046] FIG. 6 is a diagram showing an example of the configuration of store attribute information 122. The store attribute information 122 shown in FIG. 6 may have fields 122a to 122d. Field 122a may store a store name, which is identification information for identifying a store. This store name may include not only experimental stores that collect electrical signals 212 from sensors installed on electrical equipment, but also other general stores. Field 122b may store an equipment name, which is identification information for identifying equipment in the store. Field 122c may store an attribute value indicating an attribute of the store. Field 122d may store a value indicating the purpose of the electrical signal. This attribute value may store a value of the store for the attribute name in field 112b of the correlation attribute information 112. This attribute value is obtained by extracting information corresponding to the column names described in the correlation attribute information 112 and the store names and the like that have this information from the business information 211 of the store information management server 200. For example, the second line of the store attribute information 122 shown in the table format in FIG. 6 has "A" in the store name column, "air conditioning" in the equipment name column, "{station building, 0-24 o'clock}" in the attribute value column, and "{power saving}" in the purpose column. This means that "air conditioning" is stored in the "equipment name" column in the second line of the store attribute information 122 corresponding to "air conditioning" as the "equipment name" in the second line of the correlation attribute information 112 shown in the table format in FIG. 5. From the business information 211 corresponding to this "air conditioning," "A" is stored as the "store name," and "{station building, 0-24 o'clock}" is stored in the "attribute value" column of the store attribute information 122 corresponding to the attribute name "{location, business hours}." The "purpose" column of the store attribute information 122 may be provided as necessary, and may contain a transcription and storage of the "purpose" targeted by the store attribute information 122. Information regarding the "purpose" may be included in the business information 211. This information regarding the use may be information indicating the use of the electrical signal, such as considering energy-saving measures based on the electrical signal of an air conditioner, or estimating cooking times based on the electrical signal of a microwave oven and using this information to help plan future cooking.

[0047] FIG. 7 is a diagram illustrating an example of the configuration of candidate store information 124. The candidate store information 124 illustrated in FIG. 7 may have fields 124a to 124e. Field 124a may store information for identifying the general store as a "general store name." Field 124b may store the name of the cluster to which the general store belongs as a "cluster number." Field 124c may store the name of an experimental store that belongs to the same cluster as the general store and is the shortest distance from the general store as a "neighboring experimental store name." Field 124d may store the distance between the general store and the experimental store that is located near the general store as a "neighboring experimental store name." Field 124e may store information indicating the order in which the general store should be added as an experimental store as a "candidate order." For example, in the second row of the candidate store information 124 shown in table format in Figure 7, the general store name column stores "A," the cluster number column stores "1," which is the number of the cluster to which store A belongs, the nearby experimental store name column stores "X," which is the name of the experimental store located closest to store A, the distance from experimental store column stores "12," which is the distance between store A and the experimental store in the nearby experimental store name, and the candidate ranking column stores "1," which is the candidate ranking.

[0048] FIG. 8 is a diagram illustrating an example of the configuration of similarity information 142. The similarity information 142 illustrated in FIG. 8 may include fields 142a to 142c. In this example, field 142a may store information for identifying a candidate store as a "candidate store name." Field 142b may store information for identifying an experimental store as an "experimental store name." Field 142c may store the similarity of the electrical signals between the candidate store and the experimental store as a "similarity." This similarity may be stored for all combinations of candidate stores and experimental stores. For example, the second row of candidate store information 124 illustrated in table format in FIG. 8 stores "A" in the "candidate store name" column, "X" in the "experimental store name" column, and "80%" in the "similarity" column. This is the result of the similarity calculation unit 141 receiving the electrical signal selected by the electrical signal acquisition unit 131 and calculating the similarity of the electrical signals between the candidate store and the experimental store. This calculation of similarity may be performed between the candidate store and all the experimental stores, or may be performed between a required range of experimental stores.

[0049] FIG. 9 is a diagram showing an example of the configuration of recommendation information 144. The recommendation information 144 shown in FIG. 9 may have fields 144a to 144c. Field 144a may store information for identifying a candidate store as a "candidate store name." Field 144b may store an electrical device whose electrical signal is to be measured in the candidate store as a "target device." Field 144c may store an order in which the candidate store is recommended as an experimental store for measuring the electrical signal of the target device as a "recommendation order." For example, in the second row of the recommendation information 144 shown in the table format in FIG. 9, "C" is stored in the candidate store name column, "{air conditioner, rice cooker}" is stored in the target device column, and "1" is stored in the recommendation order column.

[0050] <Flowchart example> FIG. 10 is a flowchart illustrating an example of the operation of the sensor placement assistance server 100. As shown in FIG.

[0051] First, the correlation analysis unit 111 of the sensor placement support server 100 may acquire the business information 211 of the experimental store and the electrical signals 212 of each electrical device in the experimental store from the store information management server 200 (step S101). Next, the correlation coefficient between the electrical signal 212 and the business information 211 for each electrical device may be calculated and stored in the correlation attribute information 112 (step S102). This correlation coefficient may be calculated using a known method such as Pearson's correlation coefficient or point biserial correlation coefficient to calculate the correlation coefficient between the business information and the electrical signal of the electrical device, and a predetermined number of pieces of business information are recorded in the correlation attribute information 112 for each electrical device. When recording, the business information can be recorded in descending order of correlation coefficient. Furthermore, the number of pieces of business information to be recorded may be a predetermined number set as appropriate.

[0052] The attribute information acquisition unit 121 can extract information corresponding to the column names listed in the correlation attribute information 112, as well as the store names that have that information, from the business information 211 of the store information management server 200, and store the extracted information as store attribute information 122 (step S103).

[0053] The clustering unit 123 performs clustering on the general stores and the experimental stores based on the attribute values ​​described in the store attribute information 122, and calculates the cluster number to which the general store belongs, the name of the nearest experimental store that belongs to the same cluster as the general store, and the distance between the general store and the nearest experimental store (step S104). Here, clustering can be performed on all general stores and experimental stores. Next, the candidate stores can be ranked in descending order of distance from the nearest experimental store, and the results can be stored in the candidate store information 124 together with the calculation results of step S104 (step S105).

[0054] As an example, the process may jump from step S105 to step S110, and the results stored in the candidate store information 124 may be sent to the display unit 300, which is an output unit, and displayed, that is, output.

[0055] The electrical signal acquisition unit 131 can select a predetermined number of general stores as candidate stores from each cluster listed in the candidate store information 124 in descending order of candidate rank (step S106). This predetermined number can be set as appropriate. Next, electrical signals of the trunk circuits of the candidate stores and the experimental store can be acquired (step S107). At this time, if electrical signals of electrical devices are also recorded in the candidate stores, the electrical signals of the electrical devices may be acquired instead of or together with the electrical signals of the trunk circuits.

[0056] The similarity calculation unit 141 receives the electrical signals acquired by the electrical signal acquisition unit 131, calculates the similarity of the electrical signals between the candidate store and the experimental store, and stores the calculated similarity in the similarity information 142 (step S108). This similarity calculation can be performed between the candidate store and all experimental stores. Alternatively, for example, experimental stores that are known to have a high degree of similarity can be excluded from this calculation. In this case, if the electrical signals of electrical devices are selected instead of the electrical signals of the main circuit, the similarity of the electrical signals may be calculated only for electrical devices with the same device name.

[0057] The recommendation unit 143 can set a recommendation order for the candidate stores in ascending order of similarity described in the similarity information 142 and store the order in the recommendation information 144 (step S109).

[0058] The display unit 300 displays the candidate stores and the electrical devices to be measured in descending order of recommendation rank described in the recommendation information 144, and can recommend installing a sensor in the electrical device to designate the electrical device as an experimental store (step S110). At this time, if the similarity calculation unit 141 calculates the similarity for the electrical signals of the main circuit, all electrical devices in the candidate store will be displayed as the electrical devices to be measured, but they can be displayed using the values ​​of the equipment configuration described in the business information 211. If the similarity is calculated for the electrical signals of an electrical device, the electrical device can be displayed as the measurement target. The display unit 300 may function as an output unit that outputs experimental store candidates with high recommendation ranks (low recommendation ranks). In this embodiment, the display unit 300 is used as an output unit, but depending on the application, the experimental store candidates can also be output as electronic data.

[0059] 11(a) and (b) show examples of recommendation screens 301. These recommendation screens 301 may be displayed and output on a display unit 300 of, for example, a personal computer.

[0060] The recommendation screen 301 shown in FIG. 11(a) may be an example in which, when recommending a new experimental store as a candidate, electrical devices on which electrical sensors are to be installed are specified and recommended along with the store. That is, this example may be an example in which electrical sensors are not installed on all electrical devices in the new experimental store. The recommendation screen 301 shown in FIG. 11(a) may have fields 301a to 301g. Field 301a may display, as a "recommendation order," the order in which the candidate stores are recommended as new experimental stores. Field 301c may display, as a "candidate store name," the name of the store recommended as a new experimental store. Field 301b may display, as a "cluster," the cluster to which the store displayed in field 301c belongs. Field 301d may display, as a "target device," the electrical device on which sensors are to be installed in the store displayed in field 301c. Field 301e may display, as an "operation," the handling operation selected by a user of the sensor placement management system according to the present invention for the candidate stores specified in the "recommendation order." On the recommendation screen 301, candidate experimental stores with low recommendation rankings are output and displayed, as shown in field 301a. In this example, candidate experimental stores with low recommendation rankings are output and displayed in ascending order of recommendation ranking. The number of candidate experimental stores with low recommendation rankings to display may be determined as needed. The user may select how to handle the recommended information displayed on the recommendation screen 301. For example, the user may select to adopt the recommended item by moving the pointer on a personal computer to an operation field for the information displayed in table format in FIG. 11(a) and clicking there on the personal computer. Before performing this adoption operation, the operation field may display "[Not Selected]," and when the user clicks to adopt, the corresponding operation field may display "[Adopt]." This click operation may cause the operation field to cycle through the following options: [Not Selected], [Adopt], ​​and [Exclude]. For example, [Adopt] can be configured to mean that the recommendation of the store etc. is adopted, [Exclude] means that the recommendation of the store etc. is not adopted, and [Not Selected] means that the recommendation of the store etc. is not adopted.Field 301f may be an icon that is clicked to confirm, for example, when selecting employment or the like for the recommendation of a store or the like and then deciding and confirming the information on employment or the like. The "Recalculate" icon in field 301g may be an icon used when recalculating by providing conditions different from the information currently displayed on the recommendation screen 301 and displaying information related to new recommendations. In other words, when recalculating by providing conditions different from the current conditions, the conditions may be provided, for example, from a computer keyboard, and the "Recalculate" icon may be clicked to perform recalculation according to the newly provided conditions.

[0061] The recommendation screen 301 shown in Fig. 11(b) may be an example in which, unlike the case of Fig. 11(a), when a new experimental store is recommended as a candidate, electrical sensors are installed on all electrical devices in the new experimental store. The recommendation screen 301 shown in Fig. 11(b) may have fields 301a to 301c and 301e to 301g. The explanation of each field is the same as that of Fig. 11(a), so the explanation will be omitted.

[0062] 11(a) and 11(b), it is possible to collect, with a small number of sensors, the learning data required to create a probability generation model that estimates the operating state of an electrical device from the electrical signal of the main circuit, in combination with the processing up to displaying this recommendation screen 301. In particular, by adopting a configuration like the recommendation screen 301 shown in Fig. 11(a) and 11(b), it is easy for users of the sensor placement management system according to the present invention to grasp information about recommended stores, etc., and operation is also easy.

[0063] In the embodiment described above, the candidate store calculation unit 120 may further cluster all store attribute information and calculate a predetermined number of experimental store candidates from the clusters to which the candidate stores belong in descending order of distance from the nearest experimental store to the candidate stores. Here, "distance" refers to the distance within the cluster resulting from clustering, and this applies throughout this specification and drawings unless otherwise specified.

[0064] In this case, the processing of the candidate store calculation unit 120 is the same as in the case described above, in that the clustering unit 123 performs clustering based on the attributes described in the store attribute information 122 and the distances between the stores, and records information on the cluster to which each store belongs, nearby experimental stores, and the distances from those experimental stores in the candidate store information 124. The clustering calculation method and the definition of distance are also the same. As in the case described above, the stores are divided into several clusters according to the results of clustering performed by the clustering unit 123 based on the attributes described in the store attribute information 122 and the distances between the stores, for example, the level of correlation between the store attributes and the distances between the stores.

[0065] Furthermore, after clustering all store attribute information 122 in the clustering unit 123, in each cluster to which the candidate store belongs, i.e., in the cluster in which general stores exist, a predetermined number of general stores are selected as candidates for a new experimental store in descending order of distance from the nearest experimental store. Here, in the case of a cluster in which only one general store exists, the predetermined number may be 1.

[0066] An example of information stored as candidate store information 124 through such processing is shown in Figure 7. Figure 7 shows that a general store with the general store name "A" belongs to a cluster with cluster number "1", its nearby experimental store is "X", the distance between store A and store X is "12", and its candidate ranking is "1".

[0067] In this example, for each general store belonging to a cluster, a predetermined number of general stores may be selected as candidates for the experimental store, i.e., candidate stores, in descending order of distance from the nearest experimental store among the experimental stores also belonging to that cluster. In other words, general stores that do not have any existing experimental stores nearby become candidates for new experimental stores, and a probabilistic generation model derived using this new experimental store is expected to reproduce the in-store electricity consumption with high accuracy. Furthermore, by using this probabilistic generation model, it is expected that the in-store electricity consumption of other general stores near the new experimental store will also be reproduced with high accuracy.

[0068] In FIG. 7, candidate rankings are assigned based on the distance between the general store and the nearest experimental store. Here, the ranking is not limited to the cluster, and the candidate ranking 124e may be changed to 1, 2, 3, etc., in order of the general store corresponding to the general store name 124a with the greatest distance 124d from the experimental store. The "distance from experimental store" in field 124d refers to the distance between the general store and the nearest experimental store. In the example shown in FIG. 7, the distances 124d from the experimental store to the nearest store are "12," "5," and "3," respectively. Therefore, the candidate ranking 124e may be set to "1" for general store A, "2" for general store C, and "3" for general store B.

[0069] In this way, when ranking is performed regardless of cluster, candidate rankings 124e are assigned to all general stores in descending order of distance 124d from the experimental store. Therefore, by selecting a general store with a high candidate ranking 124e as a new experimental store, it is possible to efficiently create a highly accurate probability generation model that can be applied to many general stores.

[0070] Alternatively, candidate rankings can be assigned to each cluster. This will be explained using an example in which the data from the general store name 124a to the distance 124d from the experimental store in FIG. 7 are the same. First, a cluster to which the general store corresponding to the general store name 124a with the longest distance 124d from the experimental store belongs may be selected. In this case, cluster 1 may be selected, to which general store A, whose general store name 124a has "A" in it and whose distance 124d from the experimental store is the longest at "12." Then, candidate rankings 124e may be assigned to cluster 1 first. That is, in this case, the general stores belonging to the cluster with cluster number 124b of "1" are general stores A and B, referring to the general store name 124a. Here, candidate rankings 124e for general stores A and B can be determined in descending order of distance 124d from the experimental store. That is, in this example, the distances 124d from the experimental store corresponding to general stores A and B are "12" and "3," respectively. Therefore, the candidate rankings 124e can be set to "1" and "2," respectively. These are the general stores belonging to cluster 1. Next, the cluster with the largest distance 124d from the experimental store may be selected. That is, for the general stores belonging to cluster 1, the cluster with the second largest distance 124d from the experimental store after the largest distance 124d from the experimental store, "12," is selected. That is, the cluster with cluster number 124b of "2" is represented as cluster 2, and cluster 2 to which general store C belongs can be selected. Then, the general stores in the general store name 124a belonging to cluster 2 can be re-assigned candidate rankings 124e in descending order of distance 124d from the experimental store. As a result of this processing, in this example, the candidate rankings 124e can be set as follows: general store A is set to "1," general store B is set to "2," and general store C is set to "1." Although the same candidate ranking may appear multiple times, they can be distinguished because the candidate rankings correspond to the cluster numbers.

[0071] A cluster may contain many stores with similar attributes. For example, one cluster may have many stores located in station buildings, while another cluster may have many stores located along national highways. In this case, by ranking candidates within each cluster, it is possible to identify new experimental stores as candidates based on the unique conditions of being located in a station building.

[0072] In the above example, after clustering all store attribute information 122, a predetermined number of general stores in each cluster are selected as candidates for new experimental stores in descending order of distance from their nearest experimental store. However, when deriving the distance between a general store in each cluster and its nearest experimental store, the present invention also includes a case in which the nearest experimental store does not belong to the same cluster as the general store. This has the advantage of being able to find new experimental store candidates even when a cluster contains general stores but not experimental stores. Furthermore, clustering can be performed on only some of the store attribute information 122 as needed, rather than on all store attribute information 122. For example, if there is a general store that is not a candidate for a new experimental store for some reason, that general store can be excluded from clustering. This can shorten processing time for clustering and other processes.

[0073] Next, a further modification will be described. The above-described embodiment may further include an electrical signal acquisition unit 130 that collects electrical signals of electrical devices used in the candidate store and the experimental store from electrical sensors installed in those stores, and an experimental store recommendation unit 140 that calculates the similarity of the collected electrical signals between the candidate store and the experimental store and recommends electrical devices in the candidate store with a low similarity as sensor installation locations where an electrical sensor should be added. Note that a low similarity may mean that the similarity is relatively low.

[0074] As described in the above embodiment, the clustering unit 123 performs clustering to obtain the candidate store information 124. Next, the electrical signal acquisition unit 131, which is the main function of the electrical signal acquisition unit 130, can select a predetermined number of general stores with high candidate ranks 124e within each cluster listed in the candidate store information 124 as candidate stores, i.e., stores that will be candidates for a new experimental store (see FIG. 10, S106). Next, for the selected candidate stores and the experimental stores associated with the nearby experimental store names 124c corresponding to the candidate stores, electrical signals from electrical devices used in those stores may be acquired and collected from electrical sensors installed in those stores (see FIG. 10, S107). In FIG. 10, step S107 is simply described as "acquire electrical signals from the candidate stores and the experimental stores."

[0075] Next, the similarity calculation unit 141 receives the electrical signals acquired and collected by the electrical signal acquisition unit 131, calculates the similarities of the electrical signals between the candidate store and all experimental stores, and stores the results in similarity information 142 (step S108). Here, when calculating the similarities, it is possible to select not all experimental stores, but only a portion of the experimental stores as appropriate, such as experimental stores corresponding to the candidate store. Next, the experimental store recommendation unit 140 may set a recommendation order for the candidate stores in the candidate store name 142a listed in the similarity information 142 in ascending order of similarity 142c, and store the order in recommendation information 144 (step S109).

[0076] Next, the display unit 300 may display the candidate stores and the electrical devices whose electrical signals are to be measured in descending order of the recommendation rank of the candidate stores stored in the recommendation information 144, and recommend that the electrical devices be equipped with electrical sensors to make them experimental stores (step S110). The display of the recommendation rank of the candidate stores may be adopted according to the purpose, and may not be in descending order of recommendation rank.

[0077] With this configuration, candidate stores that are not only highly ranked by clustering but also have electrical signals with low similarity to those of the experimental store are recommended as new experimental stores. Therefore, if the candidate store uses distinctive equipment not found in other stores, it is expected that a probabilistic generation model that is more suited to the candidate can be created, making it useful as a candidate for a new experimental store. In other words, the learning data required to create a probabilistic generation model can be collected with a small number of sensors.

[0078] Next, a further modification will be described. In the above-described embodiment, the system may include an electrical signal acquisition unit 130 that collects electrical signals from electrical devices used in candidate stores and experimental stores from electrical sensors installed in those stores, and an experimental store recommendation unit 140 that calculates the similarity between the collected electrical signals for the candidate stores and the experimental stores and recommends electrical devices in the candidate stores with low similarity as locations where electrical sensors should be installed. In this modification, the experimental store recommendation unit 140 may also be a sensor placement management system that calculates the similarity between the electrical signals from the main power supply for each candidate store and the experimental store. That is, when acquiring electrical signals from electrical devices used in the candidate stores selected in step S106 and the experimental stores associated with the nearby experimental store names 124c corresponding to the candidate stores from electrical sensors installed in those stores, the electrical sensors may be installed in the main circuits of those stores, thereby acquiring and collecting electrical signals from all electrical devices connected to the main circuits (see FIG. 10 , S107). Thereafter, the similarity calculation unit 141 may calculate the similarity, and the display unit 300 may recommend a new experimental store in the same manner as described above (step S110). In the description of the above embodiment, the experimental store recommendation unit 140 recommended electrical devices with low similarity as sensor installation locations where electrical sensors should be added, but in this modified example, these electrical devices with low similarity may mean all electrical devices connected to a main circuit. The experimental store recommendation unit 140 may also recommend all electrical devices with low similarity as sensor installation locations where electrical sensors should be added, that is, a group of electrical devices, in other words, a main circuit with low similarity. Note that "low similarity" may mean that the similarity is relatively low.

[0079] According to this modification, for a group of one or more electrical devices connected to the main circuit at each of the candidate store and the experimental store, the sum of the electrical signals of those electrical devices is obtained, the similarity of those sums is calculated, and the candidate store with a relatively low similarity is recommended as the new experimental store. This allows for the generation of a more accurate probabilistic generation model and better prediction of the store's electricity consumption. In other words, the learning data required to create the probabilistic generation model can be collected with a smaller number of sensors. Furthermore, the probabilistic generation model can be obtained by acquiring electrical signals at a single point on the main circuit, which also contributes to reducing data acquisition costs.

[0080] Next, a further modification will be described. In the above-described embodiment, the system may include an electrical signal acquisition unit 130 that collects electrical signals of electrical devices used in the candidate store and the experimental store from electrical sensors installed in those stores, and an experimental store recommendation unit 140 that calculates the similarity between the collected electrical signals for the candidate store and the experimental store and recommends electrical devices in the candidate store with low similarity as sensor installation locations where electrical sensors should be added. In this modification, the system may further calculate a second similarity for the sum of electrical signals of some of the electrical devices corresponding to the candidate store and the experimental store, and the experimental store recommendation unit 140 may be a sensor placement management system that recommends the installation of electrical sensors for the electrical devices with low similarity installed in the candidate store.

[0081] Here, the electrical appliances with low similarity to be recommended may be electrical appliances with a relatively low calculated second similarity. The number of electrical appliances to be recommended may be determined arbitrarily as needed. Furthermore, the electrical appliances recommended here may be those for which electrical sensors are to be installed only in electrical appliances installed in the candidate store with a relatively low similarity.

[0082] In this variant, the similarity between the electrical signals collected for the candidate store and the experimental store described in the above embodiment is calculated, and in addition to recommending electrical equipment in the candidate store with low similarity as a sensor installation location where an electrical sensor should be added, it may also be possible to recommend sensor installation locations where further electrical sensors should be added.

[0083] In a store, external power is drawn into a main circuit via a power line, branched into several circuits, and then further branched from the branches to distribute power to various electrical devices. An example of such a branch is a circuit breaker. By measuring electrical signals at such a branch location, the sum of the signals of one or more electrical devices connected downstream of the branch can be measured. That is, from the perspective of the store, the sum of the electrical signals of some of the electrical devices corresponding to the store can be measured and obtained. In this modification, for example, a similarity can be calculated for the sum of the electrical signals of some of the electrical devices corresponding to the candidate store and the experimental store, obtained in this manner, and this similarity can be used as a second similarity. The experimental store recommendation unit 140 may recommend the installation of an electrical sensor for electrical devices installed in the candidate store with low similarity. When recommending the installation of an electrical sensor using the sum of signals of electrical devices at a branch location such as a circuit breaker, the electrical device on which the recommended electrical sensor is installed may be, for example, the branch described above or a circuit breaker installed at the branch. Note that the degree of similarity is low may mean that the degree of similarity is relatively low.

[0084] In addition to the effects described in the above embodiment, this modification is expected to recommend the installation of electrical sensors on electrical devices that are effective when some of the electrical devices in a candidate store are distinctive, that is, when the devices are distinctive compared to other devices, for example, when the devices consume a lot of power or are used during specific time periods, and to obtain a highly accurate probabilistic generation model. In other words, the learning data required to create a probabilistic generation model can be collected with a small number of sensors.

[0085] The above-mentioned similarity is explained in more detail below. In the present invention, similarity refers to the degree of similarity between the electrical signal of a candidate store and the electrical signal of an experimental store. Even if the electrical signal of a candidate store is highly similar to the electrical signal of an existing experimental store, the electrical signal of the existing experimental store can be used as a substitute. Therefore, the candidate store does not necessarily need to be recommended in the recommendation information 144. When recommending candidate stores, the recommendation unit 143 can use similarity to recommend candidate stores, etc., by (1) filtering out stores with relatively high similarity to exclude them from the candidate stores, (2) selecting the candidate store with the lowest similarity among experimental stores with relatively low similarity, or (3) recommending the candidate store with the lowest similarity among the maximum similarity values ​​corresponding to each experimental store. Regarding "(1)" and "(2)," the efficiency of selection can be improved. Regarding "(3)," a more effective probability generation model can be obtained when selecting general stores with relatively similar electrical signals as candidate stores.

[0086] A further improved embodiment will be described below. In this embodiment, the candidate store calculation unit 120 in the above embodiment acquires the use of the electric signal described in the business information and uses it for clustering.

[0087] In the candidate store calculation unit 120, when the attribute information acquisition unit 121 creates store attribute information 122, it acquires information about the use of the electrical signals in the store and adds it to the store attribute information 122. This information about the use is output as the store attribute information 122 exemplified in Fig. 6 or in a form added to the items of the candidate store information 124, and can be displayed on the display unit 300, for example, for use.

[0088] For example, in FIG. 6, the use information for store A, whose name is A, is "power saving." This means, for example, that the "air conditioning" in the equipment name used in store A is controlled and used with the aim of power saving. Also, for example, in FIG. 6, the "microwave oven" in the equipment name used in store B is controlled and used with the aim of cooking plans. Here, when considering a new experimental store using this sensor placement management system, by knowing this use information, it can be used as reference information when planning a power saving plan for air conditioning in another store, referring to the control and use of air conditioning in store A. Also, it can be used as reference information when planning a cooking plan, such as considering cooking times and staff allocation, in another store, referring to the cooking plan for microwave ovens in store B, whose name is B.

[0089] In the above description of the embodiments, a commercial store such as a convenience store has been used as an example of an object to which the sensor placement management system of the present invention is applied. Therefore, the names of the matters specifying the invention are those corresponding to the commercial store. The sensor placement management system of the present invention is not limited to commercial stores, but can be applied to various facilities that are units of power consumption, such as factories, homes (detached houses and apartment buildings), and office buildings. Here, the names of the matters specifying the invention used in describing the embodiments will be replaced with names corresponding to these various facilities. The replacements are as follows: That is, the store attribute calculation unit described as store attribute calculation unit 110 can be read as a power consumption target attribute calculation unit, the candidate store calculation unit 120 described as a candidate store calculation unit can be read as a candidate power consumption target calculation unit, store attribute information described as store attribute information 122 can be read as attribute information, candidate store information described as candidate store information 124 can be read as candidate power consumption target information, the experimental store recommendation unit described as experimental store recommendation unit 140 can be read as an experimental power consumption target recommendation unit, the store information management server described as store information management server 200 can be read as a power consumption target information management server, and the display unit described as display unit 300 can be read as an output unit. Furthermore, the term "business information" used in the above description of the embodiments is not particularly reinterpreted, but it is understood to have the meaning of, for example, in-home usage information when the present invention is applied to a home. The same applies to other applications.

[0090] An embodiment of the present invention has been described above using examples of the present invention. In the above description, the electrical signal acquisition unit 130 acquires electrical signals from candidate stores and the like. This electrical signal acquisition can be performed by acquiring data that has already been measured and stored in the electrical signal 212 in the store information management server. Alternatively, electrical signal measurement sensors can be installed in candidate stores and the like, and electrical signal data can be acquired through online data communication. Furthermore, the diagrams used in the description of the examples do not completely represent the paths of data and signals; the necessary movements will occur even if the paths of data and signals are not explicitly shown in the diagrams.

[0091] The present invention is not limited to the above-described embodiments and includes various modifications. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to those including all of the described configurations. Furthermore, it is possible to replace part of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add the configuration of another embodiment to the configuration of one embodiment. Furthermore, with respect to part of the configuration of each embodiment, addition, deletion, or substitution of other configurations can be applied alone or in combination.

[0092] Furthermore, the above-described configurations, functions, processing units, and processing means may be partially or entirely implemented in hardware, for example, by designing them as integrated circuits. The above-described configurations and functions may also be implemented in software, with a processor interpreting and executing a program that implements each function. Information such as the programs, tables, and files that implement each function may be stored in a memory, a recording device such as a hard disk or SSD (Solid State Drive), or a recording medium such as an IC card, SD card, or DVD.

[0093] In addition, in the diagram, the control lines and information lines shown are those that are considered necessary for explanation, and not all control lines and information lines in the product are necessarily shown. In reality, all components may be considered to be connected to each other as necessary. [Explanation of symbols]

[0094] 100: Sensor placement support server 110: Store attribute calculation unit 111: Correlation analysis section 112: Correlation attribute information 120: Candidate store calculation section 121: Attribute information acquisition unit 122: Store attribute information 123: Clustering Department 124: Candidate store information 130: Electrical signal acquisition unit 131: Electrical signal acquisition unit 140: Experimental Store Recommendation Department 141: Similarity calculation part 142: Similarity information 143: Recommendation Department 144: Recommendation information 200: Store information management server 211: Business Information 212: Electrical signal 300: Display section 301: Recommendation screen

Claims

1. A sensor placement management system, A computing device that executes computational processing and a storage device that can be accessed by the computing device, a candidate power consumption target calculation unit configured to use, as attribute information of the experimental power consumption target, business information including at least one of the address, location, and business hours of the power consumption target, the business information correlating with an electrical signal measured from an experimental power consumption target, which is the power consumption target that measures an electrical signal from an electrical sensor installed in an internal electrical device, to cluster the experimental power consumption target and general power consumption targets, and to calculate a ranking of the general power consumption target as a candidate for a new experimental power consumption target within the cluster to which the general power consumption target belongs; The computing device has an output unit that outputs the candidate experimental power consumption target with the lowest ranking.

2. A sensor placement management system according to claim 1, The sensor placement management system according to claim 1, wherein the candidate power consumption target calculation unit calculates the ranking based on a distance between the general power consumption target and the experimental power consumption target.

3. The sensor placement management system according to claim 2, The candidate power consumption target calculation unit calculates the ranking in descending order of distance to the nearest experimental power consumption target within a cluster to which the candidate experimental power consumption target belongs, and calculates a predetermined number of candidate experimental power consumption targets from the cluster to which the candidate experimental power consumption target belongs based on the ranking.

4. A sensor placement management system according to claim 2, The sensor placement management system is characterized in that, when there is no experimental power consumption object in the cluster to which the candidate experimental power consumption object belongs, the candidate power consumption object calculation unit calculates the ranking in order of the greatest distance to the nearest experimental power consumption object among the experimental power consumption objects included in a cluster other than the cluster to which the candidate experimental power consumption object belongs.

5. A sensor placement management system according to any one of claims 1 to 4, A sensor placement management system characterized in that the calculation device further has a power consumption object attribute calculation unit that calculates attributes of the experimental power consumption object that are highly correlated with the electrical signal by correlation analysis between the business information of the experimental power consumption object and the electrical signal.

6. A sensor placement management system according to any one of claims 1 to 4, the computing device includes an electrical signal acquisition unit that collects electrical signals of electrical devices from electrical sensors installed on the candidate experimental power consumption objects and the experimental power consumption objects; The sensor placement management system is characterized in that the calculation device has an experimental power consumption object recommendation unit that calculates the similarity of the electrical signals between the candidate experimental power consumption object and the experimental power consumption object, and recommends the candidate experimental power consumption object electrical equipment with the low similarity as a sensor installation location where an electrical sensor should be added.

7. A sensor placement management system according to claim 6, The sensor placement management system is characterized in that the similarity is a similarity of the main power electrical signal for each power consumption object.

8. A sensor placement management system according to claim 6, The sensor arrangement management system is characterized in that the similarity is a similarity for a sum of electrical signals of some of the corresponding electrical devices.

9. A sensor placement management system according to any one of claims 1 to 4, the business information includes a use of the electrical signal; The candidate power consumption target calculation unit acquires the use of the electrical signal and uses the use of the electrical signal for clustering.

10. A sensor placement management method executed by a sensor placement management system, comprising: the sensor placement management system includes a computing device that executes computational processing and a storage device that is accessible by the computing device; The sensor placement management method includes: a candidate power consumption target calculation step in which the computing device uses, as attribute information of the experimental power consumption target, business information including at least one of the address, location, and business hours of the power consumption target, the business information correlating with an electrical signal measured from an experimental power consumption target, which is the power consumption target that measures an electrical signal from an electrical sensor installed in an internal electrical device, to cluster the experimental power consumption target and general power consumption targets, and calculate a ranking of the general power consumption target as a candidate for a new experimental power consumption target within the cluster to which the general power consumption target belongs; an output step in which the computing device outputs the candidate experimental power consumption target with the lowest ranking.

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