JP5394430B2 - Life cycle use system and life cycle use method - Google Patents

Description

  The present invention relates to a system for grasping and using a person's life cycle using photovoltaic power generation data.

  Conventionally, various methods for making a determination regarding a human life cycle have been proposed. For example, Patent Document 1 discloses a “life monitoring method and system using electricity consumption” for determining a life cycle abnormality from a change in the amount of power used.

JP 2008-112267 A

  However, since the method and system of Patent Document 1 do not take into account changes in the external environment such as weather and temperature, it is unlikely that a highly accurate determination can be expected. For example, in order to provide a service for the elderly living alone, it is necessary to grasp different life cycles depending on the weather, temperature, and the like.

  This invention is made | formed in view of the said subject, The main objective is to grasp | ascertain a human life cycle accurately using the data of photovoltaic power generation.

In order to solve the above-described problems, the present invention is a life cycle utilization system that periodically acquires generated power of a solar power generation apparatus installed in a customer's house and stores management data including the generated power Means for storing reference data indicating a standard for estimating the weather from the power output value and output differential value of the generated power; means for storing characteristic data indicating the generated power for each weather and season; and the reference Means for estimating the weather in the customer's house from the power output value and output differential value of the generated power included in the management data with reference to the data, and the power generation included in the management data with reference to the characteristic data from weather and power output values and the estimated power periodically acquires the means for estimating the season, the electric power consumption in the demand house in the demand house, the use electrostatic Means for storing and adding amount of the management data, the estimated weather and season, the use amount of power stored in the management data, the temporal variation of the power usage in the demand house, season and every weather And means for creating and storing performance data classified into the above.

According to this configuration, using the generated power of the solar power generation device at the customer's house, the weather at the customer's house is estimated, and the use at the customer's house is calculated for each season and estimated weather when the amount of power used is acquired. The actual data of the temporal change of the electric energy is accumulated in the storage means. According to this, it is possible to accurately grasp the life cycle of the customer's house, which varies depending on the season and weather, using the data of solar power generation.
Moreover, since the generated power and weather of the solar power generation device are used, the season can be estimated with high accuracy, and the life cycle of the customer's house can be grasped with higher accuracy.

  Further, in the above life cycle use system of the present invention, the means for storing in advance standard data indicating a standard model of temporal change in power consumption at a customer's house for each season, weather and household structure, Standard data deciding means for deciding what approximates the actual data of a predetermined consumer house among standard data, and a household for estimating the household composition corresponding to the decided standard data as the household composition of the prescribed consumer house It is good also as providing a structure estimation means.

  According to this configuration, the temporal change in the amount of power used at the customer's house varies depending not only on the current season and weather, but also on the household composition of the customer's house. Set in advance. Then, by comparing actual data of a certain customer's house with each standard model, an approximate standard model is determined, and the household composition of the consumer house is estimated from the household composition of the standard model. According to this, the household composition of a consumer's house can be estimated accurately.

  Further, in the life cycle use system of the present invention, means for identifying a customer house located in a predetermined area or under a predetermined feeder, and the standard data determining means and the household composition estimating means for each of the specified consumer houses It is good also as a means to perform the process by, and to estimate the household composition of each consumer's house, and a means to output the household composition estimated for every said consumer house.

  According to this configuration, since the distribution of the household configuration within a predetermined range is output, it can be used for various services. For example, by outputting the household composition of the customer's house in the area, it can be used as marketing information for examining the products to be displayed at a store such as a convenience store or home center in the area. Also, for each substation feeder, output the household composition of the customer's homes under its control, for example, when the power company recovers from a power failure, give priority to feeders with many elderly people living alone or children Can contribute to the formulation of

  Moreover, in the life cycle utilization system of the present invention, the means for acquiring the latest performance data of a predetermined customer's house among the stored performance data, and the acquired performance data of the stored standard data. Means for acquiring standard data indicating a standard model corresponding to the season and weather, and the household composition of the predetermined customer house, the acquired actual data and the standard data are collated, and whether both data are consistent or not It may be further provided with means for determining whether or not, and means for issuing an alarm when it is determined that the result data and the standard data do not match.

  According to this configuration, the actual data in the customer's house is verified by comparing the actual data of the customer's house with the standard data that is the same season and weather as the actual data and the same household composition as the customer's house. It is determined whether or not the temporal change in the amount of power used matches the standard model. Then, when it is determined that they are not consistent, an alarm is transmitted as there is a possibility that an abnormality has occurred in the customer's house. According to this, it can be judged whether the life cycle in a consumer's house is abnormal, and a warning can be issued as needed. And, for example, it can be used for a service for watching an elderly person living alone.

In the above lifecycle utilization system of the present invention, a predetermined distance away is a first demand house should predict weather, and the manner in which weather transitory preempt the weather of the first demand house, 1 The means for selecting the second consumer house, the means for calculating the predetermined time by dividing the predetermined distance by the speed at which the weather changes, and the estimated current weather at the second consumer house, It is good also as a means made into the predicted value of the weather after the said predetermined time in a said 1st consumer house.

  According to this configuration, the second consumer house that anticipates the weather of the first consumer house to be predicted is selected according to the changing weather, and the current weather at the second consumer house is estimated. To do. Next, from the distance between the first consumer house and the second consumer house, the difference (time) between the times when the two customer houses are in the same weather is calculated. And the weather in the 1st consumer house after that time is predicted by the estimated current weather in the 2nd consumer house. According to this, the weather after a predetermined time in a predetermined customer's house can be predicted, so that it can be utilized for a business that performs pinpoint weather prediction.

  The present invention includes a life cycle utilization method. In addition, the problems disclosed by the present application and the solutions thereof will be clarified by the description of the mode for carrying out the invention and the drawings.

  ADVANTAGE OF THE INVENTION According to this invention, a person's life cycle can be grasped | ascertained accurately using the data of photovoltaic power generation.

It is a figure which shows the structure of the life cycle utilization system. 2 is a diagram illustrating a hardware configuration of a management device 2. FIG. It is a figure which shows the structure of the data memorize | stored in the memory | storage part 25 of the management apparatus 2, (a) shows the structure of solar power generation management DB25A, (b) shows the structure of car navigation information DB25B, (c). The structure of present weather condition DB25C is shown. (A) shows the configuration of the customer management DB 25D, (b) shows the configuration of the standard life cycle model DB 25E, and (c) shows the configuration of the life cycle record DB 25F. The structure of connection feeder management DB25G is shown. It is a flowchart which shows the weather prediction process of the management apparatus. It is a figure which shows the estimation method of a weather, (a) shows the output variation example of the solar power generation device 3, (b) shows the reference | standard which estimates a weather from an electric power output value and an output differential value. It is a figure which shows the estimation method of external temperature and a season division, (a) shows the temperature characteristic of the solar power generation device 3, (b) shows the average value of the solar radiation amount for every period. It is a figure which shows the map data which plotted the observation point. It is a figure which shows the example of the selection method of an observation point, (a) shows the forecast circle centering on a forecast point, (b) shows the candidate point selected from the observation point. It is a figure which shows the positional relationship of a forecast point and a candidate point on an actual map. It is a flowchart which shows the life cycle utilization process of the management apparatus. It is a figure which shows the specific example of standard life cycle model DB25E. It is a flowchart which shows the feeder relevant process of the management apparatus. It is a figure which shows the correspondence of a watt-hour meter number and feeder ID.

Hereinafter, embodiments for carrying out the present invention will be described with reference to the drawings. The life cycle utilization system according to the embodiment of the present invention collects data such as the power generated by the photovoltaic power generation apparatus and the amount of power used by the load of the customer's house, and based on the collected data, the pinpoint season and weather It estimates the temperature, etc., and combines the changes in the amount of power used over time, and accumulates the life cycle pattern at each customer's house for each season and weather. Further, based on the estimated weather, temperature, etc., and weather radar information, the future weather at the predetermined point is predicted.
According to this, it is possible to accurately grasp the life cycle of the customer's house.

≪System configuration and overview≫
FIG. 1 is a diagram showing a configuration of the life cycle use system 1. The life cycle utilization system 1 includes a management device 2, a customer home 4, a car navigation device 5, and a weather radar 6, and each device is configured to be able to communicate via a network 7 such as the Internet. The management device 2 periodically collects information from each other device, estimates the current weather and season, predicts the future weather, and grasps the change in power consumption and household composition at each customer home 4 The apparatus is used for various services, and is realized by, for example, a PC (Personal Computer) or a server. The customer home 4 is a home of a customer who has a power usage contract with an electric power company, and regularly (for example, every 30 minutes to 1 hour) the amount of power used together with the current time and position to the management device 2. While transmitting, when the solar power generation device 3 is installed, it has a function of transmitting the output value of the generated power to the management device 2 and is actually realized by a watt hour meter or the like. The car navigation device 5 is a car navigation device mounted on an automobile, and transmits the outside temperature and the presence / absence of a wiper operation together with the current time and position to the management device 2. The weather radar 6 is a radar for observing weather conditions, and observes the position and density of rain and snow, the wind speed, the wind direction, and the like, and transmits them to the management device 2.

  FIG. 2 is a diagram illustrating a hardware configuration of the management apparatus 2. The management device 2 includes a communication unit 21, a display unit 22, an input unit 23, a processing unit 24, and a storage unit 25, and is configured so that each unit can transmit and receive data via a bus 26. The communication unit 21 is a part that performs IP (Internet Protocol) communication and the like with other devices via the network 7, and is realized by, for example, a NIC (Network Interface Card) or the like. The display unit 22 is a part that displays data in accordance with an instruction from the processing unit 24, and is realized by, for example, a liquid crystal display (LCD). The input unit 23 is a part where an operator inputs data (for example, data of a standard life cycle model) and instructions, and is realized by, for example, a keyboard, a mouse, a touch panel, or the like. The processing unit 24 exchanges data between each unit via a predetermined memory and controls the entire management apparatus 2. A CPU (Central Processing Unit) stores a program stored in the predetermined memory. It is realized by executing. The storage unit 25 stores data from the processing unit 24 and reads the stored data. For example, the storage unit 25 is realized by a nonvolatile storage device such as an HDD (Hard Disk Drive) or an SSD (Solid State Drive). The

<< Data structure >>
3 to 5 are diagrams illustrating a configuration of data stored in the storage unit 25 of the management device 2. FIG. 3A shows the configuration of the photovoltaic power generation management DB 25A. The photovoltaic power generation management DB 25A accumulates data related to the amount of power received periodically (for example, every 30 minutes to 1 hour) from each customer home 4 where the photovoltaic power generation device 3 is installed, It consists of a record including time 25A1, position information 25A2, watt-hour meter number 25A3, voltage 25A4, power output value 25A5, output differential value 25A6, and power consumption 25A7. Time 25A1 is the time when the power output value 25A5, the power consumption 25A7, and the like are measured. The position information 25A2 is information indicating the position of the solar power generation device 3 or the customer's home 4, and may be a measured value by GPS (Global Positioning System) or a preset default value. The watt-hour meter number 25A3 is a number unique to the watt-hour meter installed in the customer's home 4, and a unique number is given to each watt-hour meter within a predetermined area. The voltage 25 </ b> A <b> 4 is a voltage output value from the solar power generation device 3. The power output value 25A5 is an output value of the power generated by the solar power generation device 3. The output differential value 25A6 is a time differential value of the power output value 25A5 and indicates the amount of change per unit time. The amount of power used 25A7 is the amount of power consumed by a load such as a home appliance installed in the customer's home 4.

  In addition, about the customer home 4 in which the solar power generation device 3 is not installed, voltage 25A4, electric power output value 25A5, and output differential value 25A6 using the data of the customer home 4 with the solar power generation device 3 of the vicinity. Set. As a result, the life cycle can be grasped and used in the same manner as the customer home 4 where the solar power generation device 3 is located.

  FIG. 3B shows the configuration of the car navigation information DB 25B. The car navigation information DB 25B stores information received from the car navigation device 5 in order to improve the accuracy of the weather and temperature to be estimated or predicted. The car navigation information DB 25B includes time 25B1, position information 25B2, outside air temperature 25B3, and presence / absence of wiper operation 25B4. Consists of records that contain. Time 25B1 is the time when the car navigation apparatus 5 acquires the outside air temperature 25B3 and the like. The position information 25B2 indicates the position of the car navigation device 5 when the outside air temperature 25B3 or the like is acquired, and is acquired from an in-vehicle GPS device or the like. The outside air temperature 25B3 is a temperature outside the automobile on which the car navigation device 5 is mounted, and is obtained from an in-vehicle thermometer. The presence / absence of the wiper operation 25B4 indicates whether or not the wiper of the automobile is operating, and thus it can be determined whether or not it is raining. Note that the speed at which the wiper operates may be set. The degree of rainfall (whether heavy rain or light rain) can be determined by the magnitude of the speed.

  FIG. 3C shows the configuration of the current weather situation DB 25C. The current weather situation DB 25C is a collection of weather conditions at each point at the latest time, and includes observation points 25C1, seasonal divisions 25C2, weather 25C3, temperature 25C4, solar power generation 25C5, and power consumption 25C6. Consists of records at each point. The observation point 25C1 is basically a unique code for each customer home 4 where the solar power generation device 3 is installed, and the position of the solar power generation device 3 or the customer home 4 (position information 25A2, watt-hour meter number 25A3). And can be plotted on the map data in the memory. The season division 25C2 is a seasonal division of the observation point 25C1, and a number indicating a season estimated by a temporal change in the power output value 25A5 is set. For example, 1, 2, 3, and 4 indicate spring, summer, autumn, and winter, respectively. The weather 25C3 is the weather at the observation point 25C1, and a number indicating the weather estimated by the power output value 25A5, the output differential value 25A6, the presence / absence of the wiper operation 25B4, and the like is set. For example, 1, 2, 3, and 4 indicate clear, cloudy, rain, and snow, respectively. The temperature 25C4 is the temperature at the observation point 25C1, and the temperature estimated from the voltage 25A4 and the power output value 25A5 is set. Further, the outside temperature 25B3 of the position information 25B2 close to the observation point 25C1 may be set. The solar power generation amount 25C5 is the power generation amount of the solar power generation device 3 at the observation point 25C1, and the power output value 25A5 of the position information 25A2 corresponding to the observation point 25C1 is set. The power consumption 25C6 is the power consumption of the customer's home 4 at the observation point 25C1, and the power consumption 25A7 of the position information 25A2 corresponding to the observation point 25C1 is set.

  FIG. 4A shows the configuration of the customer management DB 25D. The customer management DB 25D is data associating a watt hour meter with a customer, and includes a record including a watt hour meter number 25D1 and a customer name 25D2. The watt-hour meter number 25D1 is a number unique to the watt-hour meter. The customer name 25D2 is the name of the customer's home 4 where the watt-hour meter with the watt-hour meter number 25D1 is installed, and is, for example, the name of the contractor who has signed a power usage contract with the power company.

  FIG. 4B shows the configuration of the standard life cycle model DB 25E. The standard life cycle model DB 25E is obtained by accumulating a daily change in power consumption and temperature of the customer's house 4 for each season, weather, and household structure as a standard model. The standard model number 25E1 and the season classification 25E2 , Weather 25E3, household composition 25E4, time 25E5, power consumption 25E6, and temperature 25E7. The standard model number 25E1 is a number unique to the data of the standard model. Seasonal division 25E2 indicates the seasonal division of the standard model. The weather 25E3 indicates the weather of the standard model. Household configuration 25E4 shows the household configuration of the standard model, and is set as a distinction between a single household or a family household, and if it is a family household, the number of people and the configuration are set. Each time of the day is set as the time 25E5. As the power consumption 25E6, a standard power consumption is set for each time 25E5. As the temperature 25E7, a standard temperature is set for each time 25E5.

  FIG. 4C shows the configuration of the life cycle record DB 25F. Life cycle record DB25F accumulate | stores the time change of the electric power consumption and temperature in 1 day as a track record for every watt-hour meter, watt-hour meter number 25F1, time 25F2, electric power consumption 25F3, temperature 25F4, It consists of a record including a season division 25F5 and a standard model number 25F6. The watt-hour meter number 25F1 is a number unique to the watt-hour meter installed in the customer's home 4 and corresponds to the watt-hour meter number 25A3. Time 25F2 is each time in a day and corresponds to time 25A1. The power consumption 25F3 is the power consumption of the customer home 4 at the time 25F2, and corresponds to the power consumption 25A7. The temperature 25F4 is the temperature of the customer's home 4 at the time 25F2, and corresponds to the temperature 25C4. The season division 25F5 is estimated and set by the temporal change of the power output value 25A5. The standard model number 25F6 is set to the standard model number 25E1 in which the temporal changes of the power consumption 25F3 and the temperature 25F4 are most approximated in the standard life cycle model DB 25E.

  The weather may be added as an item of the record of the life cycle record DB 25F, or the weather 25E3 of the standard life cycle model DB 25E associated by the standard model number 25F6 may be associated with the record. At that time, the life cycle record DB 25F accumulates performance data in which temporal changes in the electric power consumption 25F3 and the temperature 25F4 in each customer home 4 are classified for each season and weather.

  FIG. 5 shows the configuration of the connection feeder management DB 25G. The connection feeder management DB 25G indicates an association between the watt hour meter and the feeder, and includes a record including the watt hour number 25G1 and the feeder ID 25G2. The watt-hour meter number 25G1 is a number unique to the watt-hour meter installed in the customer's home 4. The feeder ID 25G2 is an ID unique to the feeder that transmits power from the substation to each distribution line, and is set with the feeder ID of the distribution line to which the watt-hour meter number 25G1 is connected.

≪System processing≫
FIG. 6 is a flowchart showing the weather prediction process of the management device 2. In the management device 2, the processing unit 24 mainly receives data from other devices through the communication unit 21, and predicts the weather at a predetermined point while referring to and updating the data in the storage unit 25. .

  First, the management device 2 estimates the current weather by the photovoltaic power generation management DB 25A and aggregates it in the current weather situation DB 25C (S601). Specifically, the weather is estimated from the power output value 25A5 and the output differential value 25A6 among the records of the recently acquired photovoltaic power generation management DB 25A, and is set as the weather 25C3 at the observation point 25C1.

  FIG. 7 is a diagram illustrating a weather estimation method. As shown to Fig.7 (a), when the weather is fine, the electric power generation amount of the solar power generation device 3 rises gradually, and after reaching a peak, it falls gradually. When it is cloudy, the amount of solar radiation increases or decreases according to the flow of the clouds, so the amount of generated power fluctuates. In the case of rain, there is almost no amount of solar radiation, so the amount of generated power is small. Therefore, since it is considered that the amount of generated power changes in the same pattern depending on the weather regardless of the season, first, as shown in FIG. 7B, the output differential value 25A6 is checked, and the power is used as a complementary guide. Check the output value 25A5. That is, when the output differential value 25A6 changes gradually and the power output value 25A5 is large (greater than or equal to the first threshold value), the weather is estimated to be sunny. Next, when the output differential value 25A6 changes greatly and the power output value 25A5 is medium (between the first threshold value and the second threshold value), the weather is estimated to be cloudy. When the output differential value 25A6 changes small and the power output value 25A5 is small (below the second threshold), the weather is estimated to be rain. According to this, since the estimation includes not only the maximum output but also the output change, the weather accuracy is high.

  Next, the management device 2 estimates the current outside air temperature and season classification by the photovoltaic power generation management DB 25A, and sets them in the current weather situation DB 25C (S602). Specifically, among the recently acquired records of the photovoltaic power generation management DB 25A, the outside temperature is estimated from the voltage 25A4 and the power output value 25A5, set as the temperature 25C4, the season is estimated from the power output value 25A5, and the season classification 25C2 Set as.

  FIG. 8 is a diagram illustrating a method for estimating the outside air temperature and the season classification. As shown in FIG. 8 (a), as the temperature characteristics of the photovoltaic power generation device 3, the voltage is low when the temperature is high and the voltage is high when the temperature is low for the same current below a predetermined value. Become. Using this characteristic, the temperature is estimated from the voltage and current of the solar power generation device 3. A voltage of 25A4 is used as the voltage. As the current, a value obtained by dividing the power output value 25A5 by the voltage 25A4 is used. Next, as shown in FIG. 8B, the average value of the amount of solar radiation varies from time to time, so the power generated by the solar power generation device 3 also varies depending on the amount of solar radiation. Using this characteristic, the season is estimated from the power output value 25A5. Note that the data indicating the average value of the amount of solar radiation for each period is classified for each weather, and data is selected according to the weather estimated in S601, and the season is estimated using the selected data.

  Then, the management device 2 collects information from the car navigation device 5 in the current weather situation DB 25C (S603). Specifically, it is conceivable to use the position information 25B2 among the recently acquired records of the car navigation information DB 25B as the observation point 25C1. In this case, the temperature 25C4 is set based on the outside temperature 25B3, and the weather 25C3 is set based on the presence / absence 25B4 of the wiper operation. Alternatively, the set temperature 25C4 may be corrected using the outside temperature 25B3, and the set weather 25C3 may be corrected using the wiper operation presence / absence 25B4. For example, when the position information 25B2 is within a predetermined distance from the observation point 25C1 and is very close, the measured information is more reliable than the estimated information, so the temperature 25C4 and the weather 25C3 are rewritten based on the car navigation information DB 25B.

  Subsequently, the management device 2 organizes the current weather situation from the above-described weather estimation result and position information (S604). Specifically, the current weather situation DB 25C is set without omission and, as shown in FIG. 9, the observation point 25C1 is plotted on the map data in the memory according to the position information 25A2.

  Next, the management device 2 selects an observation point for predicting the weather at a predetermined point based on information from the weather radar 6 (S605). For example, when it is estimated from the cloud flow that is one of the information of the weather radar 6, when it is recognized that a rain cloud is approaching the predetermined point, an observation point between the predetermined point and the rain cloud is selected. . FIG. 10 is a diagram illustrating an example of an observation point selection method. As shown in FIG. 10A, in the map data in which the observation points are plotted, first, a prediction circle centered on the prediction point (predetermined point) is drawn. The radius of the forecast circle is the distance that the rain cloud moves in the time of 10, 30, 60, and 120 minutes depending on the moving speed of the rain cloud, which is one of the information of the weather radar 6. Then, as shown in FIG. 10B, the observation points that intersect with the forecast circle are selected as candidate points between the rain cloud and the forecast point. In addition, after drawing a forecast circle with a predetermined radius (distance), the radius may be divided by the moving speed of the rain cloud to calculate the time.

  Then, the management device 2 predicts the weather at the predetermined point (S606). Specifically, the current weather at the candidate point is the weather (weather forecast) at a predetermined point after the time indicated in each forecast circle. For example, as shown in FIG. 10B, when the candidate points c and d are on the forecast circle 60 minutes after the forecast point x, the average of the current weather at the candidate points c and d is calculated as 60 minutes. It is assumed that the weather forecast of the forecast point x later. In FIG. 11, the average of the current weather at the candidate points e, f, and g is used as the weather forecast for the forecast point x after 120 minutes.

  In addition, when estimating from the cloud flow, which is one of the information of the weather radar 6, when recognizing that the rain cloud is moving away from the forecast point, the forecast point is sandwiched between the observation points that intersect with the forecast circle. Then, select an observation point on the opposite side of the rain cloud. According to the above, taking into account the information of the weather radar 6 and the effect of the westerly wind, the weather change (speed and direction) at each point is recognized. Then, based on the change of the weather, an observation point that is estimated to be the current weather after the predetermined time at the forecast point x is selected as a candidate point.

  It is conceivable to use the above-described weather prediction processing for the weather forecast business. For example, the management device 2 periodically performs weather prediction processing in response to a request from the customer home 4. That is, a weather forecast request message including a customer name and a forecast time interval is received, the electricity meter number 25D1 is specified from the customer name 25D2 by the customer management DB 25D, the weather at that point is predicted, the mobile phone of the requesting customer, etc. Notify

  FIG. 12 is a flowchart showing the life cycle utilization process of the management apparatus 2. This processing utilizes the life cycle of the customer home 4 by outputting data to the communication unit 21 and the display unit 22 while the processing unit 24 mainly refers to and updates the data in the storage unit 25 in the management device 2. To do.

  First, the management device 2 stores “season, weather, time zone, temperature” and “photovoltaic power generation amount, power consumption amount”, associates them, and analyzes the life cycle of the customer home 4 (S1201). . In detail, first, each record of photovoltaic power generation management DB25A is collected for every watt-hour meter number, and is set to the memory | storage part 25 as life cycle record DB25F. Next, the records in the life cycle record DB 25F are collated with the records in the standard life cycle model DB 25E as shown in FIG. The standard model number 25E1 is specified and set as the standard model number 25F6. Thereby, the household composition in the customer home 4 of the said electricity meter number 25F1 is estimated. Further, the customer name 25D2 (contractor name) is specified from the electricity meter number 25F1 (25D1) by the customer management DB 25D.

  According to FIG. 13, in a family household, at least one person is often at home, so the amount of power used changes all day with a predetermined value or more. On the other hand, in single-person households, since there are no people in the time zone other than from night to morning, only standby power is used, so the change in the amount of power used becomes flat. According to this, it is possible to estimate whether the household is a single household or a family household based on the change pattern of the used electric energy, and it is possible to estimate the number of family households based on the magnitude of the used electric energy. Even in the same household composition, the amount of power used varies depending on the season, weather, and temperature. For example, in spring and autumn, if the weather is good, it is comfortable and often goes out, so the amount of power used at the customer home 4 decreases. On the other hand, since the summer is hot when the weather is good, the amount of electric power used at the customer's home 4 increases because it is often spent at home without going out.

  Based on the above results, two processes are performed. First, the management device 2 first estimates the power consumption of a specific customer home 4 (S1202). For example, a standard model that fits from the standard life cycle model DB 25E is specified based on the current weather and season in the customer home 4 and the actual household configuration of the customer home 4, and the power consumption is estimated using the standard model. Next, “estimated power consumption” and “actual value of power consumption in the same situation as the current weather” are collated, and an alarm is issued if necessary (S1203). For example, the customer management DB 25D identifies the electricity meter number 25D1 from the customer name 25D2, and is the same as the current record or the current season or weather of the electricity meter number 25F1 (25D1) in the life cycle record DB 25F. A past record (latest performance data) is extracted, and the amount of power used 25F3 is set as a performance value. Then, the estimated value and the actual value of the temporal change in the power consumption are collated (for example, the area of the plane figure surrounded by two curves indicating the temporal change is obtained). As a result of the collation, if the temporal changes of the two do not match (for example, if the area of the plane figure is equal to or greater than a predetermined value), it is recognized that some abnormality has occurred, and an alarm is issued. As a specific example, it can be considered to be used for a service for watching an elderly person living alone in a specific customer house 4.

  Secondly, the management apparatus 2 transmits marketing information (S1204). By creating and outputting the distribution data of the household composition in a predetermined area from the household composition estimated in S1201, it is used, for example, in selecting a product to be displayed at a store (convenience store, home center, etc.) that opens in each area. . Marketing information including distribution data of household composition is transmitted to a server such as a company that manages a store, a market research company, or a company that develops a platform business.

  FIG. 14 is a flowchart showing feeder-related processing of the management apparatus 2. In the management apparatus 2, the processing unit 24 mainly performs processing related to the substation feeder while referring to and updating data in the storage unit 25.

  First, the management device 2 links the “watt-hour meter number” and the “feeder ID to which the photovoltaic power generation device 3 is connected”, and sets the linked feeder management DB 25G in the storage unit 25 (S1401). As shown in FIG. 15, the watt hour meter is connected to a distribution line extending from a substation feeder. The correspondence relationship between the watt-hour meter and the feeder is stored in the connection feeder management DB 25G.

  Next, the management device 2 predicts the amount of photovoltaic power generation for each feeder based on the previously predicted weather (weather forecast) (S1402). For example, referring to FIG. 7 (a), the amount of power generated by the solar power generation device 3 according to the season and weather is known, so the number of the solar power generation devices 3 connected to the feeder is grasped by the connection feeder management DB 25G. Then, the total power generation amount for the feeder is calculated by multiplying the power generation amount by the number of units. This is based on the premise that the solar power generation device 3 is installed in the customer's home 4 where the watt-hour meter is installed, but it may not be installed. In the feeder management DB 25G, a presence / absence flag of the solar power generation device 3 may be provided for each watt-hour meter number 25G1.

  And the management apparatus 2 performs the voltage adjustment of a distribution line, and the supply-and-demand adjustment of electric power according to the electric power generation amount estimated for every feeder (S1403).

  It is also possible to create a distribution data of the household composition for each feeder from the previously estimated household composition and output it for reference when determining the priority of power failure recovery. For example, the feeder is preferentially selected as a recovery target so that power supply is preferentially resumed for a customer living house 4 who is estimated to be spending alone with a single person or a child during the day.

  In the above embodiment, in order to cause each unit in the management apparatus 2 shown in FIG. 2 to function, the program executed by the processing unit 24 is recorded on a computer-readable recording medium, and the recorded program is stored in the computer. It is assumed that the life cycle utilization system 1 according to the embodiment of the present invention is realized by causing the system to be read and executed. In this case, the program may be provided to the computer via a network such as the Internet, or a semiconductor chip or the like in which the program is written may be incorporated in the computer.

  According to the embodiment of the present invention described above, the life cycle of a person can be accurately grasped using the data of solar power generation.

  Specifically, as shown in S1201 of FIG. 12, the power consumption of each household can be grasped as a life cycle with high accuracy in accordance with changes in the external environment such as weather and temperature. The household composition can be accurately estimated by collating with the standard model. Next, as shown in S601 to S606 in FIG. 6, since the weather can be predicted using the data of the photovoltaic power generation, the weather forecast business can be performed without providing a new dedicated facility such as a hundred-leaf box. And since distribution of household composition is known, as shown to S1202-S1204 of Drawing 12, a new business using solar power generation data can be developed. For example, HEMS (Home Energy Management System), a monitoring service for elderly people living alone, and transmission of marketing information can be considered.

  Furthermore, as shown in S1401 to S1403 in FIG. 14, when the spread of the solar power generation device 6 is expanded, the data of the amount of solar power generation for each feeder is adjusted when adjusting the voltage and power supply / demand of the distribution line. Can be used. Moreover, the restoration order after a power failure can be formulated based on the distribution data of household composition.

<< Other embodiments >>
As mentioned above, although the form for implementing this invention was demonstrated, the said embodiment is for making an understanding of this invention easy, and is not for limiting and interpreting this invention. The present invention can be changed and improved without departing from the gist thereof, and equivalents thereof are also included in the present invention. For example, in the above embodiment, the management device 2 has been described as estimating the season from the power output value 25A5 of the photovoltaic power generation management DB 25A. However, the season may be estimated from the date at that time. At that time, one year is divided into four periods, March to May may be spring, June to August may be summer, September to November may be autumn, and December to February may be winter. You may go.

DESCRIPTION OF SYMBOLS 1 Life cycle utilization system 2 Management apparatus 24 Processing part 25 Storage part 25E Standard life cycle model DB (standard data)
25F Life cycle record DB (result data)
25G connection feeder management DB
3 Solar power generation equipment 4 Customer homes (customer homes)
a, b, c, d, e, f, g Observation point (second customer's house)
x Forecast point (first customer's house)

Claims (10)

Means for periodically acquiring the generated power of the photovoltaic power generation apparatus installed in the customer's house and storing management data including the generated power;
Means for storing reference data indicating a reference for estimating weather from a power output value and an output differential value of the generated power;
Means for storing characteristic data indicating the generated power for each weather and season;
With reference to the reference data, from the power output value and the output differential value of the generated power included in the management data, means for estimating the weather in the customer house,
With reference to the characteristic data, means for estimating the season in the customer's house from the power output value of the generated power included in the management data and the estimated weather;
Means for periodically acquiring the amount of power used in the customer's home, and storing the amount of power used in addition to the management data ;
Means for creating and storing actual data classified by season and weather from the estimated weather and season , from the power consumption stored in the management data, the temporal change of the power consumption in the customer's house;
A life cycle utilization system characterized by comprising: The life cycle use system according to claim 1 ,
Means for storing in advance standard data indicating a standard model of a temporal change in power consumption at a customer's house for each season, weather, and household composition;
Standard data determining means for determining what approximates the actual data of a predetermined consumer house among the stored standard data;
Household composition estimation means for estimating the household composition corresponding to the determined standard data as the household composition of the predetermined consumer house;
A life cycle utilization system characterized by further comprising: The life cycle use system according to claim 2 ,
Means for identifying a customer's house located in a predetermined area or under a predetermined feeder;
Means for estimating the household composition of each consumer house by performing processing by the standard data determining means and the household composition estimating means for each identified consumer house;
Means for outputting a household composition estimated for each of the customer houses;
A life cycle utilization system characterized by further comprising: The life cycle use system according to claim 2 ,
Of the stored performance data, means for obtaining the latest performance data of a predetermined consumer house,
Among the stored standard data, means for acquiring standard data indicating a standard model corresponding to the season and weather of the acquired actual data, the household configuration of the predetermined consumer house,
Means for comparing the acquired result data with standard data to determine whether or not both data are consistent;
Means for issuing an alarm when it is determined that the actual data and the standard data are not consistent;
A life cycle utilization system characterized by further comprising: The life cycle use system according to claim 1,
Predetermined distance away is a first demand house should predict weather, weather is ahead of weather from the first demand house how transitory, means for selecting one or more second demand house ,
Means for calculating the predetermined time by dividing the predetermined distance by the speed at which the weather changes;
Means for setting the estimated current weather at the second consumer house as a predicted value of the weather after the predetermined time at the first consumer house;
A life cycle utilization system characterized by further comprising: A method of using a life cycle by a computer,
The computer
Periodically acquiring the generated power of the solar power generation apparatus installed in the customer's house and storing the management data including the generated power; and
Storing reference data indicating a reference for estimating the weather from the power output value and the output differential value of the generated power;
Storing characteristic data indicating the generated power for each weather and season;
With reference to the standard data, from the power output value and output differential value of the generated power included in the management data, estimating the weather at the customer's house,
With reference to the characteristic data, from the power output value of the generated power included in the management data and the estimated weather, estimating the season at the customer's house,
Regularly acquiring the amount of power used at the customer's home, and storing the amount of power used in addition to the management data ;
Creating and storing actual data classified according to season and weather from the estimated weather and season , from the power consumption stored in the management data, the temporal change of the power consumption in the customer's house;
The life cycle utilization method characterized by performing. The life cycle use method according to claim 6 ,
The computer
Pre-storing standard data indicating a standard model of temporal change in power consumption at a customer's house for each season, weather, and household composition;
Of the stored standard data, a standard data determination step for determining what approximates the actual data of a predetermined customer's house;
A household composition estimation step for estimating a household composition corresponding to the determined standard data as a household composition of the predetermined consumer house;
The life cycle utilization method characterized by further executing. The life cycle use method according to claim 7 ,
The computer
Identifying a customer's home located within a given area or under a given feeder;
Performing the process according to the standard data determining step and the household composition estimation step for each identified consumer house, and estimating the household composition of each consumer house;
Outputting an estimated household composition for each of the customer homes;
The life cycle utilization method characterized by further executing. The life cycle use method according to claim 7 ,
The computer
Of the stored performance data, obtaining the latest performance data of a predetermined consumer house;
Of the stored standard data, obtaining standard data indicating a standard model corresponding to the season and weather of the acquired actual data, the household configuration of the predetermined consumer house,
Collating the acquired result data with standard data to determine whether both data are consistent;
When it is determined that the actual data and the standard data are not consistent, a step of issuing an alarm;
The life cycle utilization method characterized by further executing. The life cycle use method according to claim 6 ,
The computer
Predetermined distance away is a first demand house should predict weather, weather is ahead of weather from the first demand house how transitory, and selecting one or more second demand house ,
Dividing the predetermined distance by the speed at which the weather changes to calculate a predetermined time;
Estimating the current weather at the second consumer house as a predicted value of the weather after the predetermined time at the first consumer house;
The life cycle utilization method characterized by further executing.