JP4266011B2 - Distribution system load assumption method, distribution system load assumption device - Google Patents

Description

  The present invention relates to a power distribution system load assumption method, and more particularly to a power distribution system load assumption method and a power distribution system load assumption device for estimating a consumer load and a section load in a power distribution system.

  A power distribution system is a power facility composed of electrical lines and SW devices (hereinafter referred to as switches) between substations, power plants or transmission lines and power demand facilities, or between demand facilities. The future power consumption (load) is assumed along with the distribution system distribution facility plan. That is, when a future load becomes necessary in accordance with the above plan, a future load in the distribution system is assumed. And among the future loads that will be required in connection with the distribution system equipment plan, the future daily loads for each switch (section), feeder, and transformer unit will be accurately determined based on past performance data. It is the distribution system load assumption problem.

For this distribution system load assumption problem, a method of “distributing the feeder load based on the equipment capacity ratio of each section” (hereinafter, a proportional load assumption method) is used. Various solutions based on the above apportionment method have been shown. For example, in Japanese Patent Laid-Open No. 7-79530, load estimation accuracy is improved by considering regional characteristics in the distribution section load estimation method. Further, in Japanese Patent Laid-Open No. 11-206016, an estimated load after a predetermined period of each load is calculated based on a proration rate, and the distribution state is visually displayed. Japanese Patent Laid-Open No. 2001-112171 supports the calculation of a future predicted power demand and the creation of a predicted accident case and the minimization of countermeasure costs.
JP 7-79530 A (paragraphs 0015-0016) JP-A-11-206016 (paragraphs 0010-0016) JP 2001-112171 (paragraphs 0024-0027)

  In the conventional section load assumption, the graph shape (load shape) indicating the transition of the daily load to be assumed is similar to the load shape in the entire feeder, so the characteristics for each section are not reflected. That is, there are consumers of various types (houses, shops, factories, etc.) in one section, and the load shape of each consumer is completely different. Therefore, a problem remains in terms of accuracy. Moreover, the power demand in recent years has a wide variety of load shapes such as “midnight power” and “seasonal power”. In addition, due to the spread of residential solar power generation facilities and the like, there is also a “reverse power flow” in which power generation is dispersed and the surplus power generated by the distributed power generation is returned to the feeder, making the load assumption more difficult. In addition, conventionally, it has been limited only to calculating the peak load several years ahead, but more detailed assumed load in time increments is required. Under such circumstances, the conventional load assumption method has a problem in accuracy, and it has been difficult to apply to an actual power distribution system. In order to increase the amount of data by installing a measuring instrument or the like in order to increase the accuracy, the measurement and installation costs a great deal.

The present invention has been created in view of the above problems. An object of the present invention is to provide a distribution system load estimation method and apparatus that can estimate a load for each section at low cost and with high accuracy. The basis of the present invention is specified by the following items (1) to (4).
(1) It is a distribution system load assumption method that predicts the future load of a wiring system that is divided into a plurality of sections by a plurality of switches and that includes a measurement customer having daily load measurement data and a non-measurement customer having no daily load measurement data. .
(2) Based on the annual fluctuations in past monthly power consumption of both non-measurement consumers and measurement consumers, each non-measurement consumer is assumed to be similar to the daily load of the forecast target day. Specify daily load measurement data.
(3) The daily load based on the specified daily load measurement data is determined for non-measured consumers, the daily load based on the daily load measurement data is determined for measured consumers, and the daily load is calculated for all consumers in each section. To calculate the daily load by section.
(4) Based on the daily load of each section, the distribution ratio of the daily load of each section to the daily load of the entire distribution system on the prediction target day of the future load is obtained, and the total load of the distribution system and the distribution ratio Determine the load of each section based on.

  In addition to the above items (1) to (4), the present invention includes any of the following items (11) to (14) or any combination of any of (11) to (14). It is good also as a distribution system load assumption method provided with.

(11) When specifying daily load measurement data of a measured consumer that is assumed to be similar for a non-measured consumer, the pattern shape of the annual fluctuation of the past monthly power consumption and the absolute power consumption While obtaining the correlation, the daily load measurement data of the measurement consumer assumed to be similar to the daily load of the prediction target day for each non-measurement consumer is specified based on the correlation between the pattern shape and the absolute power consumption. (12) The daily load of the non-measurement consumer is specified based on the daily load measurement data of the measurement consumer having the same consumer type.

(13) When specifying daily load measurement data of a measured consumer that is assumed to be similar for a non-measurement consumer, the shape of the annual transition graph of the monthly power consumption for the daily load measurement data of the measurement consumer The shapes are associated in the order of strong correlation with each other, and the ranks are associated in order of strong correlation of the absolute power consumption of the monthly power consumption, and are associated with the daily load measurement data of each measurement consumer. And daily load measurement data with the lowest total sum of ranks for absolute power consumption and absolute power consumption.

(14) The daily load for a non-measurement consumer is the ratio of the monthly power consumption of both the non-measurement consumer and the measurement consumer assumed to be similar to this non-measurement consumer in the month to which the forecast target day belongs To be corrected accordingly.

(15) The daily load should be corrected according to the temperature data of the prediction target day.

The present invention extends to a device that assumes a distribution system load based on the above distribution system load assumption method, and includes the following items (21) to (26).
(21) It is a distribution system load assumption device that predicts the future load of a distribution system that is divided into multiple sections by multiple switches and that includes non-measurement consumers who have daily measurement data and non-measurement consumers. .
(22) Provide a means for accessing a database for storing and managing information such as personal information, daily load measurement data, and monthly power consumption for each consumer.
(23) One measurement that is assumed to be the most similar to the daily load of the forecast target day for each non-measurement consumer based on the annual fluctuations in past monthly power consumption of both the non-measurement consumer and the measurement consumer A means for specifying daily load measurement data of a consumer is provided.
(24) Daily load based on the specified daily load measurement data for non-measured consumers, daily load based on the daily load measurement data for measured consumers, and daily load for all consumers in each section A means to obtain the daily load of each section by integrating
(25) A means for obtaining a prorated ratio of the daily load of each section to the daily load of the entire distribution system on the prediction target date of the future load based on the daily load by section.
(26) Based on the daily load of each section, obtain a prorated ratio of the daily load of each section to the daily load of the entire distribution system on the prediction target day of the future load, and calculate the total load of the distribution system and the prorated ratio Determine the load of each section based on.

  In addition to the above items (21) to (26), the distribution system load assumption device of the present invention includes any of the following items (31) to (35) or any of (31) to (35): You may provide the matter combined suitably.

(31) The means for identifying the daily load measurement data obtains a correlation for each of the pattern shape of the annual fluctuation of the past monthly power consumption and the absolute power consumption, and is a prediction target for each non-measurement consumer. Specifying daily load measurement data of a measurement consumer assumed to be similar to the daily load of the day based on the correlation between the pattern shape and the absolute power consumption.

(32) The means for specifying the daily load measurement data is to specify the daily load of each non-measurement consumer from the measurement consumers of the same type as the type of each non-measurement consumer.

(33) The means for specifying the daily load measurement data associates the rank with the daily load measurement data of the measurement consumer in order of increasing correlation of the pattern shape of the annual fluctuation of the monthly power consumption, and the monthly consumption Associating ranks in descending order of the correlation between the absolute power consumption and the power consumption, the total value of the ranks for each of the pattern shape and the absolute power consumption associated with the daily load measurement data of each measurement consumer is The lowest daily load measurement data should be specified.

(34) Percentage of daily power consumption of a non-measurement consumer for both the non-measurement consumer and the measurement consumer assumed to be similar to this non-measurement consumer in the month to which the forecast target day belongs Provide a means to correct according to.

(35) A means for acquiring the temperature data for the prediction target day of the daily load and a means for correcting the daily load according to the temperature data of the prediction target day.

  According to the distribution system load estimation method of the present invention, the future load of each section can be accurately estimated based on the monthly power consumption. Therefore, it is possible to reduce the cost for load assumption without additionally installing a device for measuring the daily load transition to a large number of consumers. Moreover, since the load can be accurately estimated, the cost can be reduced when various facilities are installed in the distribution system.

=== Distribution system ===
FIG. 1 shows an example of a distribution system to which the distribution system load assumption method of the present invention is applied. The distribution system in this example is connected via a distribution line breaker (CB) 1 in a substation. A plurality of switches 2 are installed in the power distribution system, and a section 5 is defined between the switches 2. A large number of customers (facilities that consume power) 4 belong to each section 5. The high voltage power from the substation exit is stepped down by the transformer 3 in each section 5 and distributed to each main consumer (house 4a, store 4b, office 4c, etc.). In this way, a distribution network (feeder) 10 that supplies power from the CB 1 to each consumer 4 is configured.

  In CB 1, actual data obtained by measuring the load of the entire feeder 10 in increments of time is recorded, but in the switch 2 and the transformer 3, actual load data is not recorded. Further, the consumer 4 includes a measurement consumer having a load survey measuring instrument for measuring daily load and a non-measurement consumer having no load. As is well known, the load survey meter records a time transition of power consumption (load) in one day.

  The example of the daily load measurement data about various measurement consumers was shown in Drawing 2 (A)-(C). This figure shows the daily load measurement data of a general house (A), a consumer (B) who consumes midnight power, and a commercial facility (C). In addition, a wattmeter is installed in a non-measurement consumer, and in a power company, a meter reader records the monthly power consumption in a non-measurement consumer by reading the wattmeter every month. Fig. 3 shows an example of annual fluctuation of monthly power consumption.

=== Outline of Load Assumption Method ===
For reference, FIG. 4 shows the relationship between the current distributed in each section 5 and the current of the entire feeder 10 in the distribution system shown in FIG. For example, it is assumed that there is a section from a to d in a certain distribution system. In the conventional apportioning load assumption method, since the current of the entire feeder 10 in CB1 is apportioned by the capacity of the entire customer 4 belonging to each section a to d, each of the current of the feeder 10 and the current of each section a to d is determined. The graph shapes showing the time transition are similar. However, as shown in FIG. 2, the daily load time transition of each customer 4 is various, so the graph shape of the daily load of the entire feeder 10 and each section 5 is not similar. Therefore, it can be seen that the conventional apportioning load assumption method lacks accuracy.

  Therefore, the present invention calculates the proportion of daily load of each section 5 in the entire feeder 10 after totalizing the daily load data of all the consumers 4 for each section 5 and obtaining the daily load of each section 5. . Thereby, the trend of daily load can be reflected for each section 5, and the daily load can be assumed with high accuracy. However, since there are measurement consumers and non-measurement consumers in the consumer 4, it is necessary to assume the daily load of the non-measurement consumer with reference to the daily load measurement data of the measurement consumer.

  The present inventors have empirically found that there is a high possibility that both daily loads are similar between consumers of the same type. The type is a distinction between facilities such as commercial facilities and houses, a distinction between industries such as chemical factories and garment factories, and received voltages such as 100 V and 6.6 kV. In addition, it is also found that the tendency of daily load (graph shape in FIG. 2) is more similar between consumers who have similar pattern shapes (graph shape in FIG. 3) of annual fluctuation of monthly power consumption. ing. However, for example, when looking at the annual trend of monthly power consumption between customers of the same industry, “Chemical Factory” as the same type, the absolute amount of power consumption differs between large factories and town factories. The daily load measurement data of the same industry cannot be adopted as it is as the daily load of non-measurement consumers. In the present embodiment, the fluctuation pattern of the monthly power consumption for a certain non-measurement consumer for one year and the absolute value of the monthly power consumption are respectively represented by the power consumption of all measurement consumers of the same type as this non-measurement consumer. Compare the transition and absolute value. Based on the correlation between the fluctuation pattern of the monthly power consumption and the absolute value, the daily load measurement data of the measurement consumer that seems to be the most similar / approximate is identified, and the daily load measurement data is determined as non-measurement demand. It is adopted as the most suitable origin for assuming the daily load of the house. As a result, the monthly power consumption of the same type of consumers is compared, and based on the correlation between the annual fluctuation pattern shape of the power consumption and the absolute value of the monthly power consumption, Since the daily load measurement data that is the origin of the load is specified, it is possible to narrow down the comparison target and suppress the load and time required for the specifying process and extract more similar daily load measurement data. In this example, for the measured consumer, the daily load of the expected date is obtained based on the daily load measurement data of the same month / week / day of the week as the expected date of the previous year. The daily load of the assumed date is obtained based on the daily load measurement data of the same month / week / day of the past year of similar customers.

  Thus, if the daily load of the assumption day about all the consumers 4 is obtained, the daily load of all the consumers 4 which belongs to every area 5 will be totaled. Thereby, the daily load for each section 5 is obtained. FIG. 5 shows an outline of the daily load for each section. When the loads 20a to 20c for each time of the consumers a to c belonging to a certain section are totaled, the daily load 30 of the section is obtained.

  If the daily load of all sections is obtained in this way, the load of the entire feeder 10 is set to 1, and the load for each section 5 is apportioned. Thereby, the daily load of the entire feeder 10 on the assumed date and the daily load distributed to each section 5 can be assumed in time increments.

=== Distribution system load assumption device ===
Here, a distribution system load assumption device (hereinafter referred to as load assumption device) that assumes the daily load of each section 5 on the distribution system based on the method of the present invention will be described as an embodiment of the present invention. FIG. 6 shows a functional block configuration of the load assumption device. The load assumption device 40 is mainly composed of a computer with a database 42 attached.

  The database 42 includes personal information (name, address, telephone number, contract number, customer type, power pole number, presence / absence of daily load data, etc.) about all customers 4 belonging to the power distribution system, and past monthly power consumption. The daily load measurement data and the like are accumulated and managed for the quantity and measurement customers. In this embodiment, the remote monitoring control unit 60 is a computer different from the load assumption device 40, and monitors the state of the distribution system such as the switching state and power flow of the switch 2 by appropriate communication means. Remotely control the opening and closing operation of the switch.

  The load assumption calculation control unit (hereinafter, calculation control unit) 43 is realized by executing a program installed in the assumption device 40, and performs various calculations for assuming a daily load for each section 5 according to the method of the present invention. . The main processing units included in the calculation control unit 43 include a customer type discrimination determination unit (hereinafter referred to as a type determination unit) 44, a correlation processing unit 45, a similar customer extraction unit 46, a load correction unit 47, and an assumed result output. Part 48 is present. Then, the overall control unit 41 inputs information from the input device 49 attached to the assumed device 40 such as a keyboard, inputs information output from the remote monitoring control unit 60, obtains stored information in the database 42, and these Input / acquisition information is provided to the calculation control unit 43, the processing result from the calculation control unit 43 is output to the output device 50 such as a display or a printer, the remote monitoring control device 60, or stored in the database 42. Input / output interface function is provided.

=== Daily load assumption processing ===
FIG. 7 shows a flow of load assumption processing in the load assumption device 40 of this embodiment. Here, the number of non-measurement consumers in the entire distribution system is N, the measurement consumer in the entire distribution system is L, and a non-measurement consumer (assumed consumer) that is going to assume daily load data is i (i = , N), the number of measurement customers of the same type as i is M, and the measurement customer of the same type as i is k (k = 1, 2,..., M). Therefore, M is a number that varies according to i. Further, a certain measurement consumer is assumed to be j (j = 1, 2,..., L).

  The overall control unit 41 reads the monthly power consumption data x (i) of the non-measurement consumer and the daily load measurement data y (j) of the measurement consumer from the database 42 (s1, s2). Furthermore, in this embodiment, since the daily load varies depending on the temperature, the temperature in the area where the assumed consumer is located on the expected date, which is input via the input device, is acquired (s3). Data on temperature can be obtained from the Japan Meteorological Agency in time increments by region. Of course, the load assumption device 40 may be configured to be communicable with an external computer that provides temperature data in the Japan Meteorological Agency or the like, and the temperature data may be obtained from this external computer.

  After the necessary temperature data is input, the daily load measurement data is integrated for each measurement consumer by month to calculate the monthly power consumption (s4). In addition, since the measurement consumer also records the monthly power consumption by meter reading, the monthly power consumption acquired by the meter reading may be read from the database 42 as it is, as with non-measurement consumers. Then, the overall control unit 41 gives various data related to the acquired daily load assumption to the calculation control unit 43. The calculation control unit 43 extracts a non-measurement customer (assumed customer) i that is a target of daily load (s5), and assumes a daily load for the non-measurement consumer by various processing units attached to the i. To do.

A specific daily load assumption process will be described below. First, the type determination unit 44 searches the database for a measured customer k of the same type as the assumed consumer (s6), and the correlation processing unit 45 determines the monthly power consumption of the assumed customer i and the measured customer k for the year. The shape correlation coefficient r1 of the graph showing the trend of transition and the absolute amount correlation coefficient r2 of the monthly electric energy are calculated by the following equations (Equation 1) and (Equation 2), respectively (s7, s8). In Equations 1 and 2, S _i and S _k are standard deviations regarding monthly power consumption of each of the non-measurement customer i and the measurement customer k, and m is a month. F _im and f _iave are the monthly power consumption and monthly average power consumption of m month of the non-measurement consumer i, respectively, and f _km and f _kave are the monthly consumption of the measurement consumer k in m month, respectively. Power consumption and average monthly power consumption. The standard deviation S is obtained from (Equation 3).

  When the above two correlation coefficients r1 and r2 are calculated for all measured consumers (total number M) of the same type as i for a certain assumed customer i, the similar customer extraction unit 46 causes the daily load of this i And the most similar daily load measurement data and the measurement customer (similar customer) having the measurement data are specified (s9 → s11). In the present embodiment, a method called a “ranking method” is employed for identifying similar customers. In the ranking method, each of r1 and r2 is listed by assigning ranks to the corresponding measurement consumers in descending order, and the customer with the smallest total rank is set as a similar consumer. The daily load measurement data of the similar consumer is adopted as the calculation origin of the daily load (assumed daily load) of the assumed consumer (s12 → s13). 8A and 8B show an outline of the ranking method. For an assumed consumer i, ranks are assigned to the measurement consumers a to e according to the magnitudes of the values r1 and r2 (FIG. 8A). And the sum total of the order | rank with respect to the value of r1 and r2 is calculated | required for every measurement consumer (FIG. 8B). In the example illustrated in FIG. 8, it is determined that the measured customer b is a similar customer, and an expected daily load is obtained based on the daily load measurement data of the similar customer b. In this way, by determining the correlation based on the total value of the two ranks, it is possible to treat the two correlations that should be equally important as well as different entities, and more appropriately measure the daily load. Data can be extracted. In this embodiment, when the values of r1 and r2 do not exceed a preset threshold value, the daily load measurement data of the measurement consumer of the same type as i is assumed as the daily load of the assumed consumer i. And the average daily load is adopted as the origin for determining the assumed daily load (s12 → s14).

  Since the similar consumer and the assumed consumer i have different power consumption, the daily load measurement data of the similar consumer cannot be directly adopted as the daily load of the assumed consumer i. Therefore, the load correction unit 47 corrects the daily load measurement data of the similar consumer based on the monthly power consumption of the assumed consumer i and the monthly power consumption of the similar consumer. Specifically, for example, for the same month as the month of the past expected date, obtain the monthly power consumption of the assumed customer and similar customers, find the ratio of both power consumption, and calculate this ratio of similar customers First, the daily load data is multiplied to perform first correction (s15). With this first correction, it is possible to accurately determine the power consumption of the assumed consumer in time increments.

  Furthermore, the load correction unit 47 corrects the daily load measurement data of similar customers based on the average temperature on the assumed day (s16). In the present embodiment, the past daily average temperature data is stored in the database 42, and the load correction unit 47 acquires the average temperature data for the past year and the daily load measurement data for the past year for the measurement consumer. To do. In addition, the measurement consumer in this case may be the same type of consumer as the assumed consumer, or may be a similar consumer specified earlier. Then, an average load is calculated for each day in the past year, and a correlation between the temperature and the average load is obtained. FIG. 9 shows the correlation. A function of a regression curve 71 indicating the correlation between the temperature and the average load is derived from the scatter diagram 70 in which the actual values of the average load with respect to the average temperature are plotted. The forecast average temperature is calculated by substituting the forecast average temperature on the expected function into the derived function. Then, a ratio between the average load with respect to the air temperature and the average daily load obtained by the first correction is obtained, and this ratio is further multiplied by the daily load obtained by the first correction. As a result, it is possible to finally obtain the daily load of the assumed customer with higher accuracy considering the temperature of the expected date.

  In this way, when the daily load for all non-measured consumers is obtained, the calculation control unit 43 integrates the daily load for all consumers belonging to each section, and apportions the feeder load in each section. A constant is obtained (s17 → s19). In other words, the daily load obtained by applying temperature correction to the daily load measurement data for the same month / week / day of the week as the expected date is adopted for the measurement customer, and the daily load obtained by the above method is adopted for the non-measurement customer. Then, the daily load of the entire section is obtained by integrating the load in time increments for all consumers. If the daily load for each section is obtained, the ratio of the time increments of the load for all sections (proportional constant) is calculated, and the current sent to the feeder is apportioned by the calculated proportional constant (s20). As a result, it is possible to accurately estimate the load in the time interval of the expected date allocated to each section, and when installing various facilities in the power distribution system in the future, it is possible to more accurately estimate the load in that system, Can be installed without excess or deficiency.

  Note that various types of information obtained in the process described above are output to the output device 50 such as a display or a printer, or output as data stored in the database 42, by the assumed result output unit. As the information output format and the presentation format of the information, for example, the daily load on the expected date of the entire feeder is displayed and displayed as a graph, or user input for selecting a customer or a section is accepted, and a specified consumer is selected. For example, an expected day load graph for a section or the like can be output as appropriate.

It is the schematic of the distribution system used as the assumption object of the distribution system load based on the distribution system load assumption method in the Example of this invention. It is the schematic about the daily load of the various consumers who belong to the said power distribution system. It is a graph which shows annual transition of monthly power consumption in a certain consumer. It is a related figure of the present electric current for every section assumed by the conventional apportioning load assumption method, and the present electric current of the whole feeder. It is a figure explaining the outline about the distribution system load assumption method of a present Example. It is a functional block diagram of the distribution system load assumption apparatus which estimates the daily load of each area on a distribution system based on the method of a present Example. It is a flowchart of the load assumption process in the said load assumption apparatus. It is explanatory drawing of the similar consumer extraction process included in the said load assumption process. It is a correlation diagram of temperature and average load.

Explanation of symbols

1 Distribution line breaker (CB)
DESCRIPTION OF SYMBOLS 2 Switch 3 Transformer 4, 4a-4c Consumer 10 Feeder 40 Distribution system load assumption apparatus 41 Overall control part 42 Database 43 Load assumption calculation control part 60 Remote monitoring control apparatus

Claims (12)

A distribution system load assumption method that predicts the future load of a distribution system that is divided into a plurality of sections by a plurality of switches and that has both a measurement consumer with daily load measurement data and a non-measurement consumer without
Daily load measurement of the measured consumer that is assumed to be similar to the daily load of the forecast target day for each non-measured consumer, based on the annual fluctuations in past monthly power consumption of both the non-measured consumer and the measured consumer Identify the data,
For non-measured consumers, the daily load based on the specified daily load measurement data is obtained, and for the measured consumer, the daily load based on the daily load measurement data is obtained, and the daily load for all consumers in each section is integrated. To determine the daily load for each section,
Based on the daily load of each section, the distribution ratio of the daily load of each section to the daily load of the entire distribution system on the prediction target day of the future load is determined, and each of the distribution load based on the total load of the distribution system and the distribution ratio Determine the load on the leg,
Distribution system load assumption method characterized by this.   In Claim 1, when specifying the daily load measurement data of the measurement consumer assumed to be similar for a certain non-measurement consumer, each of the pattern shape and the absolute power consumption of the annual fluctuation of the past monthly power consumption The daily load measurement data of the measured consumer that is assumed to be similar to the daily load of the forecast target day for each non-measured consumer is calculated based on the correlation between the pattern shape and the absolute power consumption. Distribution system load assumption method characterized by specifying.   3. The distribution system load assumption method according to claim 1, wherein the daily load of the non-measurement consumer is specified based on the daily load measurement data of the measurement consumer having the same consumer type. In any one of Claims 1-3, when specifying the daily load measurement data of one measurement consumer assumed to be the most similar about a certain non-measurement consumer,
Corresponding ranks in order of increasing correlation of the pattern shape of the annual fluctuation of the monthly power consumption to the daily load measurement data of the measurement consumer, and ranking in order of strong correlation of the absolute power consumption of the monthly power consumption , And
The daily load measurement data that is associated with the daily load measurement data of each measurement consumer and has the lowest total value of the ranks for each of the pattern shape and the absolute power consumption is targeted for identification.
Distribution system load assumption method characterized by this.   In any one of Claims 1-4, the daily load about a certain non-measurement consumer is both the said non-measurement consumer in the month to which a prediction object day belongs, and the measurement consumer assumed to be similar to this non-measurement consumer It is corrected according to the ratio of monthly power consumption.   6. The distribution system load assumption method according to claim 1, wherein the daily load is corrected according to temperature data of a prediction target day. A distribution system load assumption device that predicts the future load of a wiring system that is divided into a plurality of sections by a plurality of switches and that includes a measurement customer having daily load measurement data and a non-measurement customer having no daily load measurement data,
Means for accessing a database for storing and managing information such as personal information, daily load measurement data, and monthly power consumption for each consumer;
Daily load measurement of the measured consumer that is assumed to be similar to the daily load of the forecast target day for each non-measured consumer, based on the annual fluctuations in past monthly power consumption of both the non-measured consumer and the measured consumer A means of identifying data;
For non-measured consumers, the daily load based on the specified daily load measurement data is obtained, and for the measured consumer, the daily load based on the daily load measurement data is obtained, and the daily load for all consumers in each section is integrated. To obtain the daily load by section,
Based on the daily load of each section, a means for obtaining a prorated ratio of the daily load of each section with respect to the daily load of the entire distribution system on the prediction target day of the future load;
Based on the daily load of each section, the distribution ratio of the daily load of each section to the daily load of the entire distribution system on the prediction target day of the future load is determined, and each of the distribution load based on the total load of the distribution system and the distribution ratio Determine the load on the leg,
A distribution system load assumption device characterized by comprising:   8. The means for specifying the daily load measurement data according to claim 7, wherein a correlation is obtained for each of the pattern shape of the annual fluctuation of the past monthly power consumption and the absolute power consumption, and for each non-measurement consumer. A distribution system load assumption device characterized by specifying daily load measurement data of a measurement consumer assumed to be similar to the daily load of a prediction target day based on the correlation between the pattern shape and the absolute power consumption.   9. The means for specifying the daily load measurement data according to claim 7 or 8, wherein the daily load of each non-measurement consumer is specified from a measurement consumer of the same type as the type of each non-measurement consumer. Distribution system load assumption device.   The means for specifying daily load measurement data according to any one of claims 7 to 9 corresponds to the daily load measurement data of a measurement consumer in order of increasing correlation of the pattern shape of the annual fluctuation of the monthly power consumption. In addition, each of the pattern shape and the absolute power consumption associated with the daily load measurement data of each measurement consumer is associated with the rank in order of strong correlation of the absolute power consumption of the monthly power consumption. A distribution system load assumption device, characterized in that the daily load measurement data having the lowest total order value is specified.   In any one of Claims 7-10, both the measurement consumer assumed that the daily load about a certain non-measurement consumer is similar to the said non-measurement consumer in the month to which a prediction object day belongs, and this non-measurement consumer. A distribution system load assumption device comprising means for correcting according to the ratio of monthly power consumption. The method according to any one of claims 7 to 11, comprising means for acquiring temperature data of a daily load prediction target day, and means for correcting the daily load according to the temperature data of the prediction target day. Distribution system load assumption device.

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