WO2016031065A1 - Power consumption estimation device, appliance management system, power consumption estimation method, and program - Google Patents

Abstract

An appliance management device (106) is provided with a correlation calculation unit (114), a parameter selection unit (115), an estimation equation determination unit (116), and an estimation unit (117). The correlation calculation unit (114) calculates correlation indices which each indicate the strength of the correlation between past power consumption in a residence and one of a plurality of parameter candidates, which are candidates for parameters to be employed in a model equation for estimating power consumption. The parameter selection unit (115) compares the calculated correlation indices with a predetermined threshold value, and selects parameters strongly correlated with the past power consumption from among the parameter candidates on the basis of the comparison results. On the basis of a model equation which employs the selected parameters, the estimation equation determination unit (116) determines an estimation equation for estimating power consumption. The estimation unit (117) determines parameter values for the selected parameters, applies these parameter values to the determined estimation equation, and thereby estimates power consumption.

Description

Power consumption estimation device, device management system, power consumption estimation method and program

The present invention relates to a power consumption estimation device, a device management system, a power consumption estimation method, and a program.

The HEMS (Home Energy Management System) that manages energy consumption in a home can manage electrical equipment based on the estimated value of power consumption in the home in order to reduce energy consumption and power costs. is there.

For example, the control panel described in Patent Document 1 sets a target charging rate based on an estimated load value related to estimated demand power during the daytime of the load, and installs an RF (redox flow) battery so as to achieve the set target charging rate. Charge at night.

In Patent Document 1, the predicted load value is, for example, “100” for the power consumption of the full load and “50” for the case where the power consumption of the load to be operated is half the power consumption of the full load. It is described that it can be specified according to the power consumption. According to this description, the predicted load value represents the ratio of the power consumption of the load expected to be operated to the power consumption of the entire load, and therefore can be understood to correspond to the estimated value of power consumption.

Patent Document 1 describes that an expected load value can be determined from calendar information such as weekdays or weekends, can be determined from past usage conditions, or can be determined from schedule information for operating each load. Has been.

JP 2004-249481 A

However, the cited document 1 does not describe how to determine a load expected value from calendar information such as weekdays or weekends, past usage conditions, and schedule information for operating each load. Techniques for estimating power consumption with high accuracy are generally desired, for example, to reduce energy consumption and power costs, but it is difficult to find such techniques at present.

The present invention has been made to solve the above-described problems, and an object of the present invention is to provide a power consumption estimation apparatus that can accurately estimate power consumption.

To achieve the above object, a power consumption estimation apparatus according to the present invention includes a correlation calculation unit, a parameter selection unit, an estimation formula determination unit, and an estimation unit. The correlation calculation unit calculates a correlation index indicating the strength of correlation between the actual value of power consumption at the demand place and each parameter candidate that is a parameter candidate used in the model formula for estimating the power consumption. To do. The parameter selection unit compares the calculated correlation index with a predetermined threshold, and selects a parameter having a strong correlation with the actual power consumption value from the parameter candidates based on the comparison result. The estimation formula determination unit determines an estimation formula for estimating the power consumption based on the model formula that employs the selected parameter. The estimation unit estimates the power consumption by applying a parameter value corresponding to the selected parameter to the determined estimation formula.

According to the present invention, a parameter having a strong correlation with the actual value of power consumption at the demand place is selected from the parameter candidates, and a model formula using the selected parameter is created. As a result, a model formula that accurately matches the actual power consumption value can be created, and the power consumption can be estimated using this model formula. Therefore, power consumption can be estimated with high accuracy.

It is a figure which shows the structure of the apparatus management system which concerns on Embodiment 1 of this invention. 1 is a diagram illustrating a configuration of a device management apparatus according to a first embodiment. It is a figure which shows an example of schedule data. 3 is a diagram illustrating a configuration of an estimation unit according to Embodiment 1. FIG. 3 is a flowchart showing a flow of estimation formula determination processing according to the first embodiment. 3 is a flowchart showing a flow of power consumption estimation processing according to the first embodiment. It is a figure which shows the structure of the apparatus management system which concerns on Embodiment 2 of this invention. It is a figure which shows the structure of the apparatus management apparatus which concerns on Embodiment 2. FIG. 6 is a diagram illustrating a configuration of an estimation unit according to Embodiment 2. FIG. 6 is a flowchart showing a flow of estimation formula determination processing according to the second embodiment. 6 is a flowchart showing a flow of power consumption estimation processing according to the second embodiment. It is a figure which shows the structure of the apparatus management system which concerns on Embodiment 3 of this invention. FIG. 10 is a diagram illustrating a configuration of a cloud server according to a third embodiment. FIG. 10 is a diagram illustrating a configuration of a device management apparatus according to a third embodiment.

Embodiments of the present invention will be described with reference to the drawings. The same elements are denoted by the same reference symbols throughout the drawings.

Embodiment 1 FIG.
Device management system 100 according to Embodiment 1 of the present invention is a HEMS (Home Energy Management System) for managing energy consumption in a house.

In addition, a house is an example of a demand place predetermined as a place used by a consumer of electric power. The demand place may be, for example, a facility or a building, and may be one or a plurality of areas partitioned in the building. A resident of a house is an example of a user who uses a demand place, and in the present embodiment, it is assumed that the resident is a family composed of three persons: a father, a mother, and a child. The number of users may be one or more.

As shown in FIG. 1, the device management system 100 includes a plurality of home appliances 101, a power generation system 102, and a power storage system 103 as electric devices installed in a house, and a power measurement device 104 that measures power consumption in the house. A weather server 105 that provides weather information, a device management device 106 that manages energy consumption in a house, a cloud server 107 that manages information used by the device management device 106, and a user operates the device management device 106. Operation terminal 108 and a portable terminal 109 carried by the user.

Each of the plurality of home appliances 101, the power generation system 102, the power storage system 103, the power measurement device 104, and the operation terminal 108 are connected to the device management device 106 via the home network 110 as shown in FIG. It is connected so that it can communicate. The weather server 105, the cloud server 107, and the mobile terminal 109 are communicably connected to the device management apparatus 106 via a wide area network 111 such as the Internet. Each of the home network 110 and the wide area network 111 may be constructed by wire, wireless, or a combination thereof.

As shown in FIG. 1, there are three household electrical appliances 101 according to this embodiment, which are a television receiver (hereinafter referred to as “TV”), an air conditioner, and a refrigerator.

In the present embodiment, the device management system 100 will be described with an example in which each of the above-described three types of home appliances 101 is provided. However, as long as the device management system 100 is one or more, how many home appliances are provided. A device 101 may be provided. The type of household electrical appliance 101 provided in the device management system 100 is not limited to a television, an air conditioner, and a refrigerator, and may be one or more of an electric water heater, an IH (Induction Heating) cooking heater, lighting, and the like. The device management system 100 may include a plurality of home appliances 101 of the same type. In addition, the device management system 100 may include, for example, a sensor that measures temperature, humidity, illuminance, and the like for the device management apparatus 106 to control an air conditioner, lighting, and the like as home appliances.

The power generation system 102 is, for example, a solar power generation system that generates power by receiving sunlight.

It should be noted that the power generation system 102 may be a wind power generation system that generates power using wind power. The device management system 100 may include a plurality of power generation systems 102 or may not include the power generation systems 102.

The power storage system 103 is a system including a stationary storage battery, and is charged or discharged under the control of the device management apparatus 106, for example.

The power storage system 103 may be an electric vehicle charging / discharging system. The device management system 100 may include a plurality of power storage systems 103 or may not include the power storage systems 103.

As shown in FIG. 1, all of the electrical devices (each of the home appliances 101, the power generation system 102, and the power storage system 103) are connected to the electrical wiring in the house. The electrical wiring in the house is also connected to the commercial power source 112. Each of the household electrical appliances 101 operates with electric power supplied from any one or more of the commercial power source 112, the power generation system 102, and the power storage system 103 through the electrical wiring in the house. In addition, the storage battery included in the power storage system 103 is charged by power supplied from one or both of the commercial power source 112 and the power generation system 102 through the electrical wiring in the house.

As shown in FIG. 1, the power measuring device 104 acquires current data indicating the value of the current flowing through each branch line from the current sensor CT provided in each branch line of the electrical wiring in the house. As shown in FIG. 1, the current sensor CT is provided on each branch line of the electrical wiring in the house, whereby the current flowing to each of the home appliances 101, the current flowing from the power generation system 102, and the power storage system 103 And the current flowing from the power storage system 103 can be individually measured. Based on the current data acquired from the current sensor CT and the voltage value of the electrical wiring in the house, the power measuring device 104 generates power in the power generation system 102, charge / discharge amounts in the power storage system 103, and power consumption in the home appliance 101. Measure the amount.

The weather server 105 is, for example, a server that provides weather information so that weather information can be generally used via the wide area network 111. The weather information includes temperature, weather, and wind power. Note that the weather information is not limited to the above-described example, and may include, for example, one or more of wind direction, sunshine duration, and the like.

The device management apparatus 106 is an example of a power consumption estimation apparatus that estimates power consumption during a predetermined period in the house. The device management apparatus 106 according to the present embodiment estimates the power consumption during a predetermined estimation period in the house using a model formula represented by the following formula (1).

Estimated value of power consumption PE = α1 × X1 + α2 × X2 +... + Αn × Xn + C
... Formula (1)
In Formula (1), n is an integer. X1, X2,..., Xn represent parameters. α1, α2,..., αn represent coefficients of parameters X1, X2,. C represents a constant term.

Suppose that the estimation period is one day (for example, from 0:00:00 to 23:59:59) in this embodiment. That is, in the present embodiment, an example will be described in which the power consumption per day in the house is estimated based on the model expression represented by Expression (1).

As shown in the equation (1), the model equation according to the present embodiment includes a term that is a product of each of n coefficients and n parameters that are associated one-to-one, and a constant term. Expressed in sum. The model formula is not limited to the formula represented by formula (1), and an appropriately selected formula may be adopted.

The parameter is selected by the device management apparatus 106 from predetermined parameter candidates. The parameter candidates according to the present embodiment include temperature, weather, wind power, number of people at home, and time at home. As the parameters adopted in the model formula, those having strong correlation with the actual power consumption value are adopted from the parameter candidates. The number of people at home and the time at home are respectively the number of people at home and the time at home when the demand place is a house. The constant term corresponds to the consumed power regardless of the change of the parameter, and is considered to correspond to the sum of the standby power of the refrigerator and various electric devices, for example.

Functionally, as shown in FIG. 2, the device management apparatus 106 is based on a data selection unit 113 that selects data for selecting a parameter to be adopted in a model formula from parameter candidates, and the selected data. Then, a correlation calculation unit 114 that calculates a correlation index indicating the strength of correlation between the actual value of power consumption in the home and each of the parameter candidates, and the actual power consumption in the home based on the calculated correlation index A parameter selection unit 115 that selects one or more parameters having a strong correlation with a value from parameter candidates, and a model expression that employs the selected one or more parameters to estimate power consumption in the home By applying an estimation formula determination unit 116 that determines an estimation formula and a parameter value corresponding to the selected one or more parameters to the determined estimation formula, An estimation unit 117 that estimates power consumption, a planning unit 118 that creates an operation plan for one or more of the electrical devices installed in the house, based on the estimated power consumption, and In accordance with the operation plan, a device management unit 119 that manages one or a plurality of electric devices, a communication unit 120 that transmits and receives data via the wide area network 111 and the home network 110, and a storage unit 121 that stores various data are provided. .

For example, as shown in FIG. 2, the storage unit 121 includes history data 122 including history information for selecting parameters, schedule data 123 indicating each resident's schedule, and parameter candidates (which are employed in the model formula). The model data 124 for determining the method of determining the parameter value corresponding to each of the parameters), the estimation formula data 125 for specifying the determined estimation formula, and the plan data 126 indicating the created operation plan Is remembered.

The history data 122 includes, for example, the actual value of each day of power consumption in the house, the weather information of each past day, the operation history of each of the home appliances 101, the history of generated power in the power generation system 102, the power storage system 103 It includes a part or all of the charge / discharge history, the remaining capacity history of the storage battery of the power storage system 103, and the like. The operation history of each home appliance 101 includes, for example, a part or all of the time zone in which each home appliance 101 operates, the power consumption of each home appliance 101, and the like.

The schedule data 123 is data in which a user ID (Identification Data), an event type, and a time are associated as illustrated in FIG. The user ID is information for specifying each user, and indicates a planned subject. The event type indicates the type of an event scheduled such as going out, company, shopping, drinking party, visitor, school, or travel. The time indicates the time when the schedule is made, and typically includes the start and end indicated by the date and time. Note that the time may be only one of the start and end, and the method for indicating the start and end may be, for example, only the date.

Referring to FIG. 2 again, the model data 124 defines a method for determining parameter values corresponding to temperature, weather, wind power, number of people at home, and time at home, which are parameter candidates according to the present embodiment. Hereinafter, an example of a method for determining the parameter value included in the model data 124 will be described.

The temperature is, for example, the average daily temperature, and the temperature of each day included in the weather information of the history data 122 is adopted as the temperature. When the weather information includes the temperature for each time zone, for example, the average value may be the daily average temperature. In addition, when the weather information includes the temperature for each time zone, the temperature for each time zone may be set as a parameter candidate.

Weather is a numerical value obtained by expressing each of sunny, cloudy, and rain as 3, 2, 1, for example. When the weather changes in one day, an average value weighted by the time of sunny, cloudy, and rainy in the day may be adopted as the weather. In addition, when the weather information includes weather for each time zone, the weather for each time zone may be used as a parameter candidate.

Wind power is, for example, an average of the daily wind speed, and in this case is determined in the same manner as the above-described temperature. That is, for example, the average wind speed of each day included in the weather information of the history data 122 is adopted as the wind force. When the wind speed for each time zone is included in the weather information, for example, the average value may be the wind force. When weather information includes wind speed by time zone, wind power by time zone is a parameter candidate, and the wind speed of the corresponding time zone is adopted as the parameter value corresponding to wind power by time zone. May be.

The number of people at home is, for example, the average value of the number of family members in the home per day, and is determined based on the number of users in the home and the length of time the user is in the home. Specifically, for example, the number of people at home is calculated by dividing the sum of the product of the number of users at home and the length of time at which users are at home by 24 (hours).

The staying home time is, for example, the time during which one or more users are in a house during one day.

Specifically, there are three people in the house from 0:00:00 to 7:59:59, no one is in the house from 8:00:00 to 17:59:59, 17 It is assumed that there are two people in the house from 0:00 to 19:59:59, and three people from 20:00 to 23:59:59. In this case, the number of people at home is (3 × 8 + 0 × 9 + 2 × 3 + 3 × 4) /24=1.75 (person). The staying home time is 8 + 3 + 4 = 15 (hours).

The estimation formula data 125 includes information for specifying a parameter candidate selected as a parameter to be adopted in the model formula, a coefficient value multiplied by the selected parameter, and a constant term value.

The data selection unit 113 acquires the history data 122 and the schedule data 123 for a predetermined period (for example, the latest 30 days in the present embodiment) from the storage unit 121. The data selection unit 113 selects data (estimation formula determination data) for determining the estimation formula from the acquired history data 122.

In detail, for example, when the data selection unit 113 includes a predetermined event type in the scheduled data 123 acquired from the storage unit 121, the data selection unit 113 selects from the acquired history data 122. The estimation formula determination data is selected by excluding the history data 122 corresponding to the day indicated by the time included in the scheduled data. Here, the event types excluded from the history data 122 by the data selection unit 113 are, for example, event types of non-periodic events such as trips, business trips, and visitors.

The correlation calculation unit 114 calculates a correlation index between the actual power consumption value included in the estimation formula determination data selected by the data selection unit 113 and each of the parameter candidates.

Specifically, for example, the correlation calculation unit 114 determines the parameter value corresponding to each parameter candidate from the history data 122 selected by the data selection unit 113 according to the determination method indicated by the model data 124. The correlation calculation unit 114 calculates a correlation index between the determined parameter value and the actual power consumption value for each parameter candidate. Here, the correlation index according to the present embodiment is a correlation coefficient between the determined parameter value and the actual value of power consumption.

The parameter selection unit 115 compares the correlation index calculated by the correlation calculation unit 114 with a predetermined threshold value. The parameter selection unit 115 selects, from the parameter candidates, a parameter that has a strong correlation with the actual power consumption value in the home based on the comparison result. Since the correlation coefficient indicates that the larger the value, the stronger the correlation between the two variables, the parameter selection unit 115 according to the present embodiment selects a parameter whose correlation index is greater than the threshold as a parameter to be adopted in the model formula. To do. The parameter selection unit 115 causes the storage unit 121 to store information for specifying the selected parameter as the estimation formula data 125.

Note that the correlation calculation unit 114 may calculate the t value and the p value of the multiple regression analysis as a correlation index, or may calculate the correlation index using a self-organizing map (SOM), a neural network, or the like. . When the t value and the p value of the multiple regression analysis are employed as the correlation index, the parameter selection unit 115 corresponds to, for example, a case where the t value is equal to or greater than the first threshold and the p value is equal to or less than the second threshold. Parameter candidates may be adopted as parameters of the model formula.

Based on the model data 124, the estimation formula data 125, and the estimation formula determination data selected by the data selection unit 113, the estimation formula determination unit 116 sets parameter values corresponding to the parameters selected by the parameter selection unit 115. To decide. The estimation formula determination unit 116 applies the parameter value determined by itself to the model formula that adopts the parameter selected by the parameter selection unit 115. Then, the estimation formula determination unit 116 determines a coefficient value and a constant term value to be multiplied by the selected parameter, for example, by executing a multiple regression analysis. At this time, for example, the estimation formula determination unit 116 determines the coefficient and the constant term value that maximizes the determination coefficient, so that the actual power consumption included in the estimation formula determination data selected by the data selection unit 113 is determined. Determine the coefficient and constant term values that best match the values and estimates. Thereby, the estimation formula determination part 116 determines the estimation formula for estimating the power consumption in a house. The estimation formula determination unit 116 associates the determined coefficient value with the information for specifying the parameter of the estimation formula data 125 stored in the storage unit 121 by the parameter selection unit 115 and includes the determined constant term value. The estimated equation data 125 is stored in the storage unit 121.

Specifically, as shown in FIG. 4, the estimation unit 117 includes an estimation acquisition unit 127 that acquires data necessary to determine parameter values corresponding to parameters included in the estimation formula, and an estimation formula determination unit 116. The parameter value determining unit 128 that determines a parameter value to be applied to the parameter included in the estimation formula determined by the parameter value, and the parameter value determined by the parameter value determining unit 128 to the estimation formula determined by the estimation formula determining unit 116 And an estimated power calculation unit 129 that calculates an estimated value of power consumption.

More specifically, the estimation acquisition unit 127 acquires the model data 124 and the estimation formula data 125 of the storage unit 121, and specifies estimation data based on the acquired model data 124 and the estimation formula data 125. . Here, the estimation data is data necessary for determining a parameter specified by the estimation formula data 125, that is, a parameter value of a parameter employed in the model formula. For example, when any one or more of temperature, weather, and wind power is selected as the parameter of the model formula, the weather for a predetermined period (for example, the latest 30 days) included in the history data 122 of the storage unit 121 Any one or more of the temperature, weather, and wind power of the information is specified as the estimation data. For example, when one or more of the number of people at home and the time at home are selected as the parameters of the model formula, the schedule data 123 for a predetermined period (for example, the latest 30 days) stored in the storage unit 121 is stored. Specified as estimation data. The estimation acquisition unit 127 acquires the specified estimation data from the storage unit 121.

The parameter value determination unit 128 determines a parameter value corresponding to the parameter employed in the model formula based on the model data 124, the estimation formula data 125, and the estimation data acquired by the estimation acquisition unit 127.

The estimated power calculation unit 129 calculates an estimated value of power consumption based on the estimation formula data 125 acquired by the estimation acquisition unit 127 and the parameter value determined by the parameter value determination unit 128. Specifically, the estimated power calculation unit 129 calculates a product of a coefficient value and a parameter value corresponding to each of one or more parameters specified by the estimation formula data 125. The estimated power calculation unit 129 calculates the sum of the product of the calculated coefficient value and the parameter value and the value of the constant term included in the estimation formula data 125. Thus, the estimated power calculation unit 129 calculates an estimated value of power consumption.

Referring again to FIG. 2, the planning unit 118 is based on the estimated power consumption calculated by the estimated power calculating unit 129, for example, an operation plan for setting the power consumption in the home as a preset target value, Create an operation plan to keep electricity costs low.

For example, the planning unit 118 has the power storage system 103 based on the remaining capacity of the storage battery included in the power storage system 103 and the estimated power consumption calculated by the estimated power calculation unit 129 in order to keep the electricity bill low. Create a storage battery operation plan.

The device management unit 119 transmits a control signal to each of the electric devices via the home network 110 according to the operation plan created by the planning unit 118, for example. As a result, the device management unit 119 manages the electrical device according to the operation plan created by the planning unit 118.

The communication unit 120 communicates with the cloud server 107 via the wide area network 111 in order to synchronize part or all of the data 122 to 126 stored in the storage unit 121 with the cloud server 107, for example. For example, the communication unit 120 periodically acquires weather information from the weather server 105 via the wide area network 111 and stores the weather information in the storage unit 121 as the history data 122. For example, when receiving a request from the estimation unit 117, the communication unit 120 acquires weather information from the weather server 105 via the wide area network 111 and delivers it to the estimation unit 117. For example, when acquiring the schedule data 123 from the mobile terminal 109 via the wide area network 111, the communication unit 120 stores the schedule data 123 in the storage unit 121. The communication unit 120, for example, displays information indicating the amount of power generated by the power generation system 102, the amount of charge / discharge of the power storage system 103 and the remaining capacity of the storage battery, whether or not each home appliance 101 is operating, etc. via the home network 110. The information is periodically acquired, and the acquired information is stored in the storage unit 121 as history data 122. For example, the communication unit 120 periodically acquires power information from the power measurement device 104 via the home network 110 and stores the acquired information in the storage unit 121 as history data 122. This power information includes, for example, part or all of the generated power of the power generation system 102, the power charged or discharged in the power storage system, the power consumption of each of the home appliances 101, and the like.

Note that the communication unit 120 may acquire life log information or the like from a server (not shown) via the wide area network 111.

The device management apparatus 106 is physically composed of a CPU (Central Processing Unit), a RAM (Random Access Memory), a ROM (Read Only Memory), a flash memory, a communication interface, and the like. For example, the device management apparatus 106 may execute a program stored in the storage unit 121 and thereby exhibit the above-described functions.

The operation terminal 108 is a terminal device that a user inputs to the device management apparatus 106 via the home network 110 and outputs a screen showing the setting contents of the device management apparatus 106 and the like. The operation terminal 108 is, for example, a tablet terminal or a smartphone installed with a software program for functioning as a user interface of the device management apparatus 106. For example, the user sets the schedule data 123 in the device management apparatus 106 via the operation terminal 108. Further, for example, the user may select a model formula used for power consumption estimation from a plurality of model formulas prepared in advance via the operation terminal 108.

The cloud server 107 periodically communicates with the device management apparatus 106 via the wide area network 111, for example, so that the cloud server 107 is the same as part or all of the data 122 to 126 stored in the storage unit 121 of the device management apparatus 106. Data is stored in a storage unit (not shown).

The portable terminal 109 is a terminal device that a user inputs to the device management apparatus 106 via the wide area network 111 and outputs a screen showing the setting contents of the device management apparatus 106. The portable terminal 109 is, for example, a smartphone in which a software program for causing it to function as a user interface of the device management apparatus 106 is installed. The portable terminal 109 and the operation terminal 108 typically have the same functions except that they communicate with the device management apparatus 106 via either the wide area network 111 or the home network 110.

So far, the configuration of the device management system 100 according to Embodiment 1 of the present invention has been described. From here, operation | movement of the apparatus management system 100 which concerns on this Embodiment is demonstrated.

The device management apparatus 106 executes an estimation formula determination process for determining an estimation formula as shown in FIG. 5 at a predetermined time, for example, once every two weeks.

The data selection unit 113 acquires the history data 122, the schedule data 123, and the model data 124 from the storage unit 121 (step S101). Specifically, the data selection unit 113 acquires the history data 122 and the schedule data 123 for the latest 30 days and the model data 124 from the storage unit 121.

The data selection unit 113 selects estimation formula determination data from the history data 122 acquired in step S101 based on the scheduled data 123 acquired in step S101 (step S102). For example, the history data 122 on the day when an event belonging to a predetermined event type is performed is excluded from the estimation formula determination data. Thereby, the power consumption of the day when non-periodic events, such as a trip, a business trip, and a visitor are performed, can be excluded from the estimation formula determination data. As a result, it is possible to select an appropriate parameter for power consumption estimation, and it is possible to accurately estimate power consumption.

The correlation calculation unit 114 calculates a correlation index between the actual value of power consumption included in the estimation formula determination data selected by the data selection unit 113 and each of the parameter candidates (step S103). As described above, the correlation index according to the present embodiment is a correlation coefficient.

Specifically, the correlation calculation unit 114 corresponds to each parameter candidate from the history data 122 included in the estimation formula determination data selected by the data selection unit 113 based on the model data 124 acquired in step S101. Determine the parameter value. The correlation calculation unit 114 calculates a correlation index between the determined parameter value and the actual power consumption value of the history data 122 included in the estimation formula determination data selected by the data selection unit 113 for each parameter candidate. .

The parameter selection unit 115 repeats steps S105 to S106, thereby selecting a parameter to be adopted for the model formula (loop A; step S104).

The parameter selection unit 115 compares the correlation index calculated in step S103 with a predetermined threshold (step S105). When the correlation index is larger than the threshold (step S105; Yes), the parameter selection unit 115 ends the process of loop A (step S104) regarding the parameter candidate to be processed. Then, the parameter selection unit 115 repeats the process of loop A (step S104) until the process of loop A (step S104) is completed for all parameter candidates.

When the correlation index is not greater than the threshold (step S105; No), the parameter selection unit 115 excludes the parameter candidate that is the processing target from the parameters that are employed in the model formula (step S106), and the loop related to the parameter candidate that is the processing target. The process of A (step S104) ends. Then, the parameter selection unit 115 repeats the process of loop A (step S104) until the process of loop A (step S104) is completed for all parameter candidates.

When the processing of loop A (step S104) is completed for all parameter candidates, parameter candidates that are not excluded are selected as parameters to be adopted in the model formula. Information for specifying the selected parameter is delivered from the parameter selection unit 115 to the estimation formula determination unit 116.

The estimation formula determination unit 116 determines the values of the coefficient and the constant term included in the model formula that adopts the parameter selected by executing the processing of step S104 to step S106, for example, by multiple regression analysis (step S107). .

Specifically, the estimation formula determination unit 116 executes the processing of steps S104 to S106 based on the model data 124 acquired in step S101 and the estimation formula determination data selected in step S102. The parameter value corresponding to the selected parameter is determined.

For example, when the temperature is selected as a parameter adopted for the model formula, the temperature indicated by the weather information of the history data 122 included in the estimation formula determination data is determined as the parameter value. When the weather is selected as a parameter employed in the model formula, a value associated with the weather indicated by the weather information of the history data 122 included in the estimation formula determination data is determined as the parameter value. When wind power is selected as a parameter employed in the model formula, the wind speed indicated by the weather information of the history data 122 included in the estimation formula determination data is determined as a parameter value. When the number of people at home is selected as a parameter employed in the model formula, the number of people at home for each day is calculated based on the schedule data 123 included in the estimation formula determination data, and the calculated number of people at home is determined as a parameter value. . When at-home time is selected as a parameter adopted in the model formula, based on the schedule data 123 included in the estimation formula determination data, for each day, calculate the length of time that one or more users are in the house, The calculated time length is determined as a parameter value.

The estimation formula determination unit 116 determines the coefficient value and the constant term value corresponding to each parameter by applying the determined parameter value to the parameter employed in the model formula and performing multiple regression analysis. The estimation formula determination unit 116 stores the determined coefficient and constant term values as estimation formula data 125 in the storage unit 121 together with information for specifying the parameter selected by executing the processing of steps S104 to S106. Remember.

Thereby, the estimation formula determination unit 116 ends the estimation formula determination process. In the estimation formula determination process, an estimation formula is selected based on a model formula that includes parameters that have a strong correlation with power consumption, selected from parameter candidates that are considered to affect power consumption, such as weather information, the number of people at home, and time at home. Is determined. Therefore, it is possible to determine an estimation formula that can accurately estimate power consumption in a house.

In addition, since the parameters adopted in the model formula are selected from among the parameter candidates, rather than determining the estimation formula using all of the parameter candidates, the parameter, the constant term, and the estimated power consumption are determined. The amount of processing can be reduced. Therefore, it is possible to reduce the processing load for estimating the power consumption.

Furthermore, since the estimation formula determination process is executed at any time at a predetermined time, the estimation formula is reviewed at any time. The tendency of power consumption may vary depending on the season and time. By revising the estimation formula as needed, it is possible to maintain an estimation formula that can accurately estimate the power consumption in a house.

Note that the device management system 100 may include a server of a posting site on the Internet posted by a user, a server providing an SNS (Social Networking Service), and the like. In this case, the device management apparatus 106 may acquire information provided from these servers via the wide area network 111, and may acquire life log information indicating the activity status of the user based on the information. Thereby, when the user is acting differently from the schedule, it can be determined whether the user is actually in the house or outside the house based on the life log information. Then, the number of people at home and the time at home based on the schedule data can be corrected. This makes it possible to improve the accuracy of the estimation formula. In addition, the device management apparatus 106 acquires information indicating the operation status of a predetermined home appliance (for example, a television) 101 via the home network 110, and based on the information, the user actually You may decide where you were outside. This also makes it possible to correct the number of people at home and the time at home based on the schedule data, so that the accuracy of the estimation formula can be improved.

The device management apparatus 106 uses the estimation formula determined in the estimation formula determination process to execute the power consumption estimation process for estimating the power consumption in a predetermined estimation period in the house as shown in FIG. To do. The power consumption estimation process is executed at a predetermined time such as 1 am every day.

The acquisition unit for estimation 127 acquires the model data 124, the estimation formula data 125, and the estimation data (step S111).

More specifically, the estimation acquisition unit 127 acquires the estimation formula data 125 from the storage unit 121. The estimation acquisition unit 127 identifies parameters to be employed in the model formula based on the acquired estimation formula data 125. The estimation acquisition unit 127 further acquires the model data 124 from the storage unit 121. The estimation acquisition unit 127 specifies a method for determining a parameter value corresponding to a parameter employed in the model formula based on the acquired model data 124, and estimation data necessary for determining the parameter value To get.

The parameter value determination unit 128 determines a parameter value corresponding to the parameter employed in the model formula based on the estimation data acquired in step S111 and the parameter value determination method specified in step S111. (Step S112).

The estimated power calculation unit 129 is based on the value of the coefficient and the constant corresponding to the parameter specified based on the estimation formula data 125 acquired in step S111, and the parameter value determined in step S112. Then, an estimated value of power consumption is calculated (step S113).

Specifically, the estimated power calculation unit 129 calculates the product of the coefficient value and the parameter value of the corresponding parameter. When a plurality of parameters are employed in the model formula, a plurality of such products are calculated, and the sum thereof is calculated. Further, the estimated power calculation unit 129 adds the value of the constant term to the product of the coefficient value and the parameter value or the sum of the products, thereby calculating the estimated value of power consumption.

Thereby, the estimated power calculation unit 129 ends the power consumption estimation process.

In the power consumption estimation process, since the estimated value of power consumption is calculated based on the estimation formula that can accurately estimate the power consumption in the house as described above, an accurate estimated value of power consumption can be obtained. Further, as described above, an estimated value of power consumption can be obtained with a relatively low processing load.

Based on the estimated value of power consumption calculated by the power consumption estimation process, the planning unit 118 creates an operation plan for an electrical device such as a storage battery included in the power storage system 103 as described above. Since an operation plan is created based on a highly accurate estimated value of power consumption, an appropriate operation plan can be created.

For example, the planning unit 118 may create an operation plan of a storage battery that stores electricity in a time zone where the electricity rate is low and discharges it in a time zone where the electricity rate is high.

In this case, in detail, for example, the planning unit 118 performs the night (for example, from 1:00:00 to 4:59:59) based on the estimated power consumption calculated by the estimated power calculation unit 129. The estimated value of power consumption during the time period when electricity charges are high compared to (for example, from 5:00:00 to 23:59:59, from 0:00:00 to 0:59:59) calculate. Specifically, for example, the planning unit 118 refers to the history data 122 of the storage unit 121, calculates the ratio of the power consumption in the time period when the electricity rate is high to the power consumption of the day, and calculates the ratio for the day. By multiplying the estimated value of power consumption, an estimated value of power consumption in a time zone when the electricity rate is high is calculated. And the plan part 118 is the estimated value of the power consumption of the time zone when the remaining capacity of the storage battery which the electrical storage system 103 has is the time period when the electricity rate is high (for example, 5:00:00) by the beginning of the time zone when the electricity rate is high. As described above, an operation plan for a storage battery included in the power storage system 103 is created.

Here, night is an example of the first time zone, and a time zone where the electricity rate is higher than that of the night is an example of the second time zone. Electricity charges are often determined by a contract between an electric power company and a customer. For example, each of a first time zone and a second time zone may be determined according to the contents of such a contract.

Note that the estimation unit 117 may calculate an estimated value of power consumption for each time period, and the planning unit 118 may create an operation plan for the electric device based on the estimated value of power consumption for each time period. .

By managing the storage battery of the power storage system 103 according to the created operation plan, the device management unit 119 stores the storage battery in a time zone where the electricity rate is low, and discharges the storage battery in a time zone where the electricity rate is high. it can. Thereby, it becomes possible to aim at reduction of the electricity bill in a house.

As mentioned above, although Embodiment 1 of this invention was demonstrated, this Embodiment may be deform | transformed as follows.

For example, the estimation period is not limited to one day, and may be appropriately determined such as a time zone such as 1 hour or 3 hours, a week, or a month. For example, a storage battery operation plan may be created based on an estimated value of power consumption for each time period.

For example, for the model formula, different estimation formulas may be determined for weekdays (Monday to Friday excluding holidays) and weekends and holidays. In that case, the estimation formula for weekdays may be determined based on the weekday history data 122, and the estimation formula for weekends and holidays may be determined based on the history data 122 for weekends and holidays.

Modification 1
In general, a television operates when a user is viewed when the user is at home. Therefore, it is considered that the time length (operation time) during which the television operates is strongly correlated with whether or not the user is in the house. Therefore, for example, the operation time of the television may be included in the parameter candidates. In this case, when the estimation formula is determined, the past television operation time included in the history data 122 may be adopted as the parameter value corresponding to the television operation time. When calculating the estimated value of power consumption, the home time described in the first embodiment is preferably adopted as the parameter value corresponding to the operating time of the television, and the home time is excluded from the parameter candidates. Good.

An electric device that includes such an operation time as a parameter candidate may be selected as appropriate from electric devices installed in a house, but preferably has a strong correlation with whether or not the user is in the house. For example, the data selection unit 113 is an electrical device in which the correlation index indicating the strength of the correlation between whether or not there is a user in the home and the operating time of the electrical device among the electrical devices installed in the home is greater than or equal to a threshold value. A device may be specified, and the operation time of the electric device may be set as a parameter candidate. Also in this case, when adopted as a parameter of the model formula, an estimation formula can be determined and an estimated value of power consumption can be calculated by a method similar to the case where the above-described television operating time is adopted as a parameter candidate. .

That is, in this case, when the parameter value determination unit 128 selects the operation time of the electric device by the parameter selection unit 115, the parameter value determination unit 128 determines the operation time of the selected electric device based on the schedule data indicating the user's schedule. The estimated value may be determined as a parameter value corresponding to the operating time of the selected electrical device. The estimated power calculation unit 129 may estimate power consumption in the home by applying the determined parameter value to the determined estimation formula.

Here, when estimating the power consumption, the relationship between the operating time of the television and the home time may be considered. In this case, for example, the estimation formula determination unit 116 may obtain the ratio of the operating time of the television to the at-home time based on the history data 122 and include the estimation formula data 125 in the storage unit 121 for storage. The parameter value determination unit 128 determines an estimated value of the operation time of the selected electrical device based on the schedule data 123 indicating the user's schedule when the operation time of the electrical device is selected by the parameter selection unit 115. Good. Then, the parameter value determination unit 128 multiplies the determined estimated value of the operating time by the ratio included in the estimation formula data 125, and uses the obtained value as a parameter value corresponding to the selected operating time of the electrical device. It is good to decide as.

According to this modification, it is possible to select an appropriate parameter from various parameter candidates and determine the estimation formula. Therefore, it is possible to improve the accuracy of the estimated value of power consumption.

Embodiment 2. FIG.
In the present embodiment, a method for estimating power consumption in an estimation period in a house based on a model formula different from that in the first embodiment shown in the following formula (2) is described.

Estimated value of power consumption PE = α1 × X1 + α2 × X2 +... + Αn × Xn + E1 + E2 + C
... Formula (2)
Formula (2) is different from Formula (1) in that it includes E1 as the first correction term and E2 as the second correction term, and is otherwise the same as Formula (1).

The first correction term is a term in which the value is determined based on the number of users in the house and the length of time the user is in the house. For example, in a television or the like, a plurality of users may view one television, and power consumption does not necessarily increase in proportion to the number of users in a house. As described above, the first correction term is preferably employed for power consumption that may not be proportional to the number of users in the house. The first correction value, which is a value applied to the first correction term, is determined from, for example, the number of users at each day calculated based on the number of users in the house and the length of time that the user is in the house. The As a result, the power consumption can be estimated more accurately.

The second correction term is a term in which an applied value (second correction value) is determined according to the event type scheduled by the user. Depending on the event type, an event belonging to the event type is performed, and the day when the event is not performed may cause power consumption to vary greatly. Examples of such event types include event types of events that are irregular or relatively infrequent, such as travel, business trips, visitors, and birthday parties. During travel and business trips, the number of people at home decreases, so power consumption often decreases, and at birthday parties held at guests and homes, it is considered that power consumption often increases according to the number of people invited. By setting such an event type in advance, it becomes possible to estimate the power consumption on the day when an event belonging to the event type is performed more accurately.

In this embodiment, an example in which the model formula includes both the first correction term and the second correction term will be described. However, either one of the first correction term and the second correction term is included in the model formula. Only may be included. When the model formula includes the first correction term, the parameter candidates may not include the number of people at home and the time at home among the parameter candidates exemplified in the first embodiment.

The device management system 200 according to the present embodiment is configured in substantially the same manner as the device management system 100 according to the first embodiment, as shown in FIG. In the present embodiment, the configuration of the device management apparatus 206 is different from the configuration of the device management apparatus 106 according to the first embodiment.

As illustrated in FIG. 8, the device management apparatus 206 includes a storage unit 221, an estimation formula determination unit 116, and an estimation formula determination unit 216 and an estimation unit 217 instead of the storage unit 121, the estimation formula determination unit 116, and the estimation unit 117 of the first embodiment. Other configurations are the same as those of the device management apparatus 106 according to the first embodiment.

The storage unit 221 stores history data 122, schedule data 123, and plan data 126 similar to those in the first embodiment, and model data 224 that replaces the model data 124 and the estimation formula data 125 according to the first embodiment, respectively. And estimation formula data 225 are stored.

The model data 224 includes a parameter value determination method in the same manner as the model data 124 according to the first embodiment. In addition, the model data 224 includes a determination method for each of the first correction value and the second correction value.

Like the estimation formula data 125 according to the first embodiment, the estimation formula data 225 includes information for specifying what is selected as a parameter to be used in the model formula, a value of a coefficient to be multiplied by the selected parameter, and a constant. Contains the value of the term. In addition to this, the estimation formula data 225 includes a first correction table for determining a value (first correction value) to be applied to the first correction term, and a value (second correction value) to be applied to the second correction term. And a second correction table for determining.

The estimation formula determination unit 216 determines a coefficient, a constant term, a first correction table, and a second correction table, and thereby, in the same way as the estimation formula determination unit 116 according to Embodiment 1, power consumption in the home An estimation formula for estimating is determined. The estimation formula determination unit 216 stores the determined coefficient, constant term, first correction table, and second correction table in the storage unit 221 as the estimation formula data 225.

The method by which the estimation formula determination unit 216 determines the values of the coefficient and the constant term is the same as that of the estimation formula determination unit 116 according to the first embodiment. Here, a method in which the estimation formula determination unit 216 determines the first correction table and the second correction table will be described.

The estimation formula determination unit 216 determines the first correction table based on the schedule data 123 and the estimation formula determination data selected by the data selection unit 113.

Specifically, the estimation formula determination unit 216 calculates the number of people staying at home on each day based on the schedule data 123 as in the first embodiment. The estimation formula determination unit 216 calculates the estimated value for each day based on the model formula by applying the determined coefficient and constant term values and the parameter value based on the selected estimation formula determination data to the model formula. To do. The estimation formula determination unit 216 calculates the difference between the calculated estimated value and the actual value of power consumption for each corresponding day. The estimation formula determination unit 216 creates a first correction table that associates the calculated number of people at home with the calculated difference (first correction value) for each corresponding day. The estimation formula determination unit 216 includes the created first correction table in the estimation formula data 225 and stores it in the storage unit 221.

Note that the first correction value can be either positive or negative. In addition, the first correction table may be appropriately modified or set by the user.

The estimation formula determination unit 216 acquires the schedule data 123 and the history data 122 excluded by the data selection unit 113 as correction data, and determines a second correction table based on the acquired correction data.

Specifically, the estimation formula determination unit 216 specifies the event type included in the scheduled data 123 for the day indicated by the correction data. The estimation formula determination unit 216 applies a parameter value based on the determined coefficient and constant term and the parameter value based on the selected estimation formula determination data to the model formula. Based on the estimated value. The estimation formula determination unit 216 calculates the difference between the calculated estimated value and the actual power consumption value for the corresponding day. The estimation formula determination unit 216 creates a second correction table in which the identified event type is associated with the calculated difference (second correction value). The estimation formula determination unit 216 includes the created second correction table in the estimation formula data 225 and stores it in the storage unit 221. When there are a plurality of days when events of the same event type are performed, for example, an average difference value may be associated with the event type as the second correction value.

The second correction value can be either positive or negative. In addition, the estimation formula determination unit 216 calculates the identified event type and the calculated difference (second correction value) when the difference between the calculated estimated value and the actual power consumption value is equal to or greater than a predetermined threshold. A correlated second correction table may be created. The second correction table may be appropriately modified or set by the user.

The estimation unit 217 estimates the power consumption in the home by applying the parameter values corresponding to one or more selected parameters to the determined estimation formula, as in the first embodiment.

The estimation unit 217 according to the present embodiment is different from the estimation unit 117 according to the first embodiment in that the first correction term and the second correction term are included in the estimation formula. Therefore, as illustrated in FIG. The estimated power calculation unit 229 is provided instead of the estimated power calculation unit 129 according to the first embodiment. The estimation acquisition unit 127 and the parameter value determination unit 128 included in the estimation unit 217 have the same functions as those in the first embodiment.

Based on the estimation formula data 225 acquired by the estimation acquisition unit 127 and the parameter value determined by the parameter value determination unit 128, the estimated power calculation unit 229 includes the estimated power calculation unit 129 according to the first embodiment, Similarly, the sum of the product of the coefficient value and the parameter value and the value of the constant term included in the estimation formula data 225 is calculated.

Furthermore, the estimated power calculation unit 229 calculates the number of people staying at home on the day on which power consumption is estimated based on the scheduled data 123. The estimated power calculation unit 229 determines the first correction value based on the calculated number of people at home and the first correction table included in the estimation formula data 225. If the first correction value corresponding to the calculated number of people at home is not included in the first correction table, the estimated power calculation unit 229 corresponds to, for example, two at-home numbers near the calculated number of people at home. It is preferable to determine the first correction value corresponding to the calculated number of people at home by apportioning the first correction value and performing interpolation interpolation or extrapolation complementation.

Furthermore, the estimated power calculation unit 229 specifies the event type of the day on which power consumption is estimated based on the schedule data 123. When the identified event type is included in the second correction table included in the estimation formula data 225, the estimated power calculation unit 229 determines a second correction value associated with the event type.

The estimated power calculation unit 229 adds each of the determined first correction value and second correction value to the sum of the product of the coefficient value calculated as described above and the parameter value and the value of the constant term, thereby Then, an estimated value of power consumption is calculated.

So far, the configuration of the device management system 200 according to Embodiment 2 of the present invention has been described. From here, the operation of the device management system 200 according to the present embodiment will be described.

In the present embodiment, the device management apparatus 206 executes an estimation formula determination process whose process flow is shown in FIG. The estimation formula determination process according to the present embodiment includes the process of step S208 in addition to the respective processes (step S101 to step S107) of the estimation formula determination process according to the first embodiment, as shown in FIG.

When the process of step S107 is executed, the estimation formula determination unit 216 determines the first correction table and the second correction table (step S208).

Specifically, the estimation formula determination unit 216 creates the first correction table as described above based on the scheduled data 123 acquired in step S101 and the estimation formula determination data selected in step S102. decide.

Further, the estimation formula determination unit 216 acquires from the data selection unit 113 correction data that has been excluded in step S102 out of the history data 122 acquired in step S101. The estimation formula determination unit 216 determines the second correction table as described above based on the acquired correction data and the scheduled data 123 acquired in step S101.

In the present embodiment, the device management apparatus 206 executes a power consumption estimation process whose process flow is shown in FIG. As shown in the figure, the power consumption estimation process according to the present embodiment includes the same processes of steps S111 and S112 as the power consumption estimation process according to the first embodiment, and the process of step S213 instead of the process of step S113. Including.

When the process of step S112 is executed, the estimated power calculation unit 229, as described above, calculates the product of the coefficient value and the parameter value included in the estimation formula data 125, and the value of the constant term included in the estimation data. The sum of the first correction value and the second correction value determined as described above is calculated. Thus, the estimated power calculation unit 229 calculates an estimated value of power consumption.

As described above, the model formula according to this embodiment includes the first correction term. Thereby, even if power consumption does not increase in proportion to the number of people at home, the estimated value of power consumption can be corrected according to the number of people at home. Therefore, it is possible to estimate the power consumption more accurately.

Further, the model formula according to the present embodiment includes a second correction term. As a result, even when an event of a predetermined event type such as an irregular event is performed, the estimated value of power consumption can be corrected according to the event type. Therefore, it is possible to estimate the power consumption more accurately.

Embodiment 3 FIG.
In the first and second embodiments, all of the data selection unit 113, the correlation calculation unit 114, the parameter selection unit 115, the estimation formula determination unit 116 or 216, the estimation unit 117 or 217, and the planning unit 118 are all managed by the device management apparatus 106 or 206. The example provided is described. The cloud server may include some or all of the data selection unit 113, the correlation calculation unit 114, the parameter selection unit 115, the estimation formula determination units 116 and 216, the estimation units 117 and 217, and the planning unit 118. In the present embodiment, an example will be described in which the cloud server includes a part of the functions of the device management apparatus 106 according to the first embodiment.

The device management system 300 according to the present embodiment is configured in substantially the same manner as the device management system 100 according to the first embodiment, as shown in FIG. In the present embodiment, the configurations of the cloud server 307 and the device management apparatus 306 are different from the configurations of the cloud server 107 and the device management apparatus 106 according to the first embodiment.

In the present embodiment, as shown in FIG. 13, the same data selection unit 113, correlation calculation unit 114, parameter selection unit 115, estimation formula determination unit 116 and estimation unit 117 as those in the first embodiment, history data 122, A storage unit 321a that stores the schedule data 123, the model data 124, and the estimation formula data 125, and a communication unit 320a that communicates with the device management apparatus 306, the weather server 105, and the like via the wide area network 111 are provided. The communication unit 320a causes the storage unit 321a to store history data 122 and schedule data 123 that are appropriately acquired via the wide area network 111.

By providing such a configuration, the cloud server 307 according to the present embodiment can execute the estimation formula determination process and the power consumption estimation process according to the first embodiment.

As shown in FIG. 14, the device management apparatus 306 according to the present embodiment includes a planning unit 118 and a device management unit 119 similar to those of the first embodiment, a communication unit 320b that communicates via the wide area network 111, and a plan. A storage unit 321b for storing data 126. For example, the communication unit 320b acquires data indicating the estimated value of power consumption calculated by the cloud server 307 via the wide area network 111. Thereby, similarly to Embodiment 1, the device management apparatus 306 can create an operation plan for the electric device based on the estimated value of power consumption, and manage the electric device based on the created operation plan.

Also according to the present embodiment, the same effects as in the first embodiment can be obtained.

In the present invention, by installing the computer in the computer, for example, the data selection unit 113, the correlation calculation unit 114, the parameter selection unit 115, the estimation formula determination unit 116 or 216, and the estimation unit 117 according to the first to third embodiments. Or you may implement | achieve as a program for functioning as a part or all of 217 and the plan part 118. FIG. The present invention may be realized as a storage medium on which such a program is recorded instead of temporarily.

As mentioned above, although embodiment and the modified example (including what was written in the description. The same is true hereafter) of this invention were demonstrated, this invention is not limited to these. The present invention includes a combination of the embodiments and modifications as appropriate, and a modification appropriately added thereto.

The present invention can be suitably used for a power consumption estimation system, a power consumption estimation device, a power consumption estimation method, a program therefor, and the like for estimating power consumption at a place where power is demanded.

100, 200, 300 device management system, 101 home appliance, 102 power generation system, 103 power storage system, 104 power measurement device, 105 weather server, 106, 206, 306 device management device, 107, 307 cloud server, 113 data selection unit, 114 correlation calculation unit, 115 parameter selection unit, 116, 216 estimation formula determination unit, 117, 217 estimation unit, 118 planning unit, 119 device management unit, 120, 320a, 320b communication unit, 121, 221 storage unit, 122 history data , 123 schedule data, 124, 224 model data, 125, 225 estimation formula data, 126 plan data, 127 estimation acquisition unit, 128 parameter value determination unit, 129, 229 estimation power calculation unit.

Claims (13)

1. A correlation calculation unit for calculating a correlation index indicating the strength of correlation between the actual value of the power consumption at the demand place and each of the parameter candidates that are parameter candidates employed in the model formula for estimating the power consumption; ,
   A parameter selection unit that compares the calculated correlation index with a predetermined threshold, and selects a parameter having a strong correlation with the actual value of the power consumption from the parameter candidates based on the comparison result;
   An estimation formula determining unit that determines an estimation formula for estimating the power consumption based on the model formula that employs the selected parameter;
   A power consumption estimation apparatus comprising: an estimation unit configured to estimate the power consumption by applying a parameter value corresponding to the selected parameter to the determined estimation formula.
2. The power consumption estimation apparatus according to claim 1, wherein the parameter candidates include a number of users in the demand place and a number of people in the place determined based on a length of time that the user is in the demand place.
3. The estimation unit includes
   A parameter value determining unit that determines a parameter value corresponding to the number of people on the basis of schedule data indicating the schedule of the user when the number of people on the location is selected by the parameter selection unit;
   The power consumption estimation apparatus according to claim 2, further comprising: an estimated power calculation unit that estimates the power consumption by applying the determined parameter value to the determined estimation formula.
4. The plurality of parameter candidates include an operation time that is a length of time during which each of one or more electrical devices installed in the demand place operates,
   The estimation unit includes
   When a predetermined operation time of the electric device is selected by the parameter selection unit, a parameter value corresponding to the operation time of the selected electric device is determined based on schedule data indicating the user's schedule A parameter value determination unit to perform,
   The power consumption estimation apparatus according to claim 1, further comprising: an estimated power calculation unit configured to estimate the power consumption by applying the determined parameter value to the determined estimation formula.
5. The model formula includes a first correction term determined based on the number of users in the demand place and the length of time the user is in the demand place,
   The estimation formula determination unit determines the estimation formula including the first correction term based on the model formula adopting the selected parameter,
   The estimation unit determines the length of time that the user is in the demand place and the number of users in the demand place based on the schedule data indicating the schedule of the user, and the determined length of time and The first correction amount is determined based on an estimated value of the number of people, a parameter value corresponding to the selected parameter is applied to the determined estimation formula, and the first estimation formula included in the determined estimation formula is used. The power consumption estimation apparatus according to claim 1, wherein the power consumption is estimated by applying the determined first correction amount to one correction term.
6. The model formula includes a second correction term determined according to the event type scheduled by the user,
   The estimation formula determination unit determines the estimation formula including the second correction term based on the model formula adopting the selected parameter,
   The estimation unit determines the second correction amount based on an event type included in the schedule data indicating the user's schedule, and sets a parameter value corresponding to the selected parameter to the determined estimation formula The estimated power calculating unit that estimates the power consumption by applying the determined correction amount to the second correction term included in the determined estimation formula. The power consumption estimation apparatus according to any one of claims.
7. The power consumption estimation apparatus according to any one of claims 1 to 6, wherein the plurality of parameter candidates include weather information.
8. 8. The system according to claim 1, further comprising: a planning unit that creates an operation plan for one or more of the electrical devices installed in the demand location based on the estimated power consumption. The power consumption estimation apparatus described in 1.
9. The power consumption estimation apparatus according to claim 8, wherein the planning unit creates an operation plan for the storage battery based on a remaining capacity of the storage battery and the estimated power consumption.
10. The storage unit stores the storage battery so that the remaining capacity of the storage battery becomes equal to or more than the estimated power consumption of the second time period before the second time period when the electricity rate is higher than that of the first time period. The power consumption estimation apparatus according to claim 9, wherein an operation plan is created.
11. A correlation calculation unit for calculating a correlation index indicating the strength of correlation between the actual value of the power consumption at the demand place and each of the parameter candidates that are parameter candidates employed in the model formula for estimating the power consumption; ,
    A parameter selection unit that compares the calculated correlation index with a predetermined threshold, and selects a parameter having a strong correlation with the actual value of the power consumption from the parameter candidates based on the comparison result;
    An estimation formula determining unit that determines an estimation formula for estimating the power consumption based on the model formula that employs the selected parameter;
    An estimation unit that estimates the power consumption by applying a parameter value corresponding to the selected parameter to the determined estimation equation;
    A planning unit that creates an operation plan for one or more of the one or more electrical devices installed in the demand location based on the estimated power consumption;
    A device management system comprising: a device management unit that manages the one or more electrical devices according to the created operation plan.
12. The power consumption estimation device provides a correlation index indicating the strength of correlation between the actual value of power consumption at a demand location and each parameter candidate that is a parameter candidate employed in the model formula for estimating the power consumption. Calculating,
    The power consumption estimation device compares the calculated correlation index with a predetermined threshold, and selects a parameter having a strong correlation with the actual power consumption value from the parameter candidates based on the comparison result To do
    The power consumption estimation device determines an estimation formula for estimating the power consumption based on the model formula adopting the selected parameter;
    The power consumption estimation method includes: estimating the power consumption by applying a parameter value corresponding to the selected parameter to the determined estimation formula.
13. Computer
    A correlation calculation unit that calculates a correlation index indicating the strength of correlation between the actual value of power consumption at a demand place and each parameter candidate that is a parameter candidate employed in the model formula for estimating the power consumption;
    A parameter selection unit that compares the calculated correlation index with a predetermined threshold, and selects a parameter having a strong correlation with the actual value of the power consumption from the parameter candidates based on the comparison result;
    An estimation formula determination unit that determines an estimation formula for estimating the power consumption based on the model formula adopting the selected parameter;
    The program for functioning as an estimation part which estimates the said power consumption by applying the parameter value corresponding to the said selected parameter to the determined estimation formula.

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