Power transmission capacity estimation device and power transmission capacity estimation method

Abstract

To provide a power transmission capacity estimation device that can flexibly operate a transmission line with a simple configuration.SOLUTION: A power transmission capacity estimation device 100 includes: an acquisition unit 110 that acquires first weather forecast data 211 including first temperature forecast data and second temperature forecast data for a first period before a predetermined time point and a second period after the predetermined time point, and weather observation data 231 including actual temperature data for the first period; a correction unit 120 that calculates temperature error data using the first temperature forecast data and the actual temperature data, and corrects the second temperature forecast data; and an estimation unit 130 that estimates the transmission capacity for the second period using the second temperature forecast data. The correction unit 120 calculates a plurality of pieces of corrected second temperature forecast data by correcting the second temperature forecast data using a plurality of methods. The estimation unit 130 selects one power transmission capacity from a plurality of transmission capacities obtained using the plurality of pieces of second temperature forecast data, as the power transmission capacity to be estimated.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The present invention relates to a power transmission capacity estimation device and a power transmission capacity estimation method for estimating the available capacity of a power transmission line.

Background Art

[0002] Conventionally, a technique for calculating the available capacity of a power transmission line has been known. For example, in Patent Document 1, using the temperature, wind speed, and solar radiation measured by a plurality of sensors on the power transmission line route where the overhead power transmission line is installed, the current capacity of the overhead power transmission line is calculated for each individual sensor, and the minimum value among the calculated current capacities is output as the current capacity of the overhead power transmission line (the available capacity of the power transmission line).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the above conventional technology, a plurality of sensors are attached on the power transmission line route to measure the temperature and the like, and the available capacity of the power transmission line is calculated according to the local conditions. However, in the above conventional technology, since it is necessary to attach a plurality of sensors on the power transmission line route, the configuration is complicated. Further, in the above conventional technology, the available capacity of the power transmission line is calculated according to the current situation such as the temperature, but since the available capacity of the power transmission line at a future time point is unknown, there is a risk that flexible operation cannot be performed.

[0005] The present invention has been newly made by the inventor of the present application paying attention to the above problems, and an object thereof is to provide a power transmission capacity estimation device and a power transmission capacity estimation method that can achieve flexible operation of a power transmission line with a simple configuration.

Means for Solving the Problems

[0006] To achieve the above objective, a power transmission capacity estimation device according to one aspect of the present invention is a power transmission capacity estimation device for estimating the power transmission capacity, which is the operational capacity of a power transmission line, and comprises: an acquisition unit that acquires first weather forecast data including first temperature forecast data and second temperature forecast data showing predicted temperature values ​​in a predetermined area during a first period before a predetermined time and a second period after the predetermined time, and weather observation data including actual temperature data showing actual temperature values ​​in the predetermined area during the first period; a correction unit that uses the first temperature forecast data and the actual temperature data to calculate temperature error data showing the temperature forecast error in the predetermined area, and uses the calculated temperature error data to correct the second temperature forecast data; and an estimation unit that uses the corrected second temperature forecast data to estimate the power transmission capacity in the predetermined area during the second period, wherein the correction unit calculates a plurality of corrected second temperature forecast data by correcting the second temperature forecast data in a plurality of ways, and the estimation unit selects one power transmission capacity from a plurality of power transmission capacities obtained using the plurality of second temperature forecast data as the power transmission capacity to be estimated.

[0007] According to this, the power transmission capacity estimation device acquires first weather forecast data, which includes first and second temperature forecast data for the first and second periods, and weather observation data, which includes actual temperature data for the first period. For example, the power transmission capacity estimation device can acquire forecast data from the Japan Meteorological Agency's mesoscale model (MSM) and observation data from AMeDAS, thereby acquiring first weather forecast data and weather observation data without the need to install multiple sensors along the power transmission line route. Furthermore, the power transmission capacity estimation device calculates temperature error data using the first temperature forecast data and actual temperature data, corrects the second temperature forecast data using the temperature error data, and estimates the power transmission capacity for the second period using the corrected second temperature forecast data. In particular, the power transmission capacity estimation device calculates multiple corrected second temperature forecast data by correcting the second temperature forecast data in multiple ways, and selects one power transmission capacity from multiple power transmission capacity obtained using the multiple second temperature forecast data. In this way, the power transmission capacity estimation device estimates the power transmission capacity for the second period using multiple second temperature forecast data with corrected errors. As a result, the power transmission capacity estimation device can estimate the power transmission capacity, which is the operational capacity of the transmission line during the second period after a predetermined point in time, with relatively high accuracy. Therefore, the transmission line can be operated flexibly based on the estimated power transmission capacity. Thus, the power transmission capacity estimation device enables flexible operation of the transmission line with a simple configuration.

[0008] Furthermore, the correction unit may update the first weather forecast data using multiple models to calculate multiple updated first weather forecast data, and then calculate multiple second temperature forecast data using the first temperature forecast data and the second temperature forecast data included in each of the multiple first weather forecast data.

[0009] According to this, the power transmission capacity estimation device updates the first weather forecast data using multiple models, and calculates multiple second temperature forecast data using the data contained in each of the updated first weather forecast data. As a result, the power transmission capacity estimation device can select one power transmission capacity from multiple power transmission capacities obtained using multiple second temperature forecast data calculated from multiple models. Therefore, the power transmission capacity estimation device can estimate the power transmission capacity with relatively high accuracy by selecting the power transmission capacity obtained using a model with relatively high estimation accuracy.

[0010] Furthermore, the correction unit may update the first weather forecast data using multiple models that utilize data from different sized areas of the first temperature forecast data and the second temperature forecast data included in the first weather forecast data.

[0011] According to this, the power transmission capacity estimation device updates the first weather forecast data using multiple models that utilize data from areas of different sizes included in the first weather forecast data. As a result, the power transmission capacity estimation device can select one power transmission capacity from multiple power transmission capacities obtained using multiple second temperature forecast data calculated from multiple models that use different data. Therefore, the power transmission capacity estimation device can estimate the power transmission capacity with relatively high accuracy by selecting the power transmission capacity obtained using a model with relatively high estimation accuracy.

[0012] Furthermore, the correction unit may update the first weather forecast data using multiple models, including a model set to either lower the temperature as the amount of cloud cover increases, or raise the temperature as the amount of cloud cover increases, depending on the time of day.

[0013] For example, during the day, a greater amount of cloud cover suppresses the rise in temperature, while at night, a greater amount of cloud cover suppresses the drop in temperature. In other words, the effect of cloud cover on temperature can be reversed depending on the time of day. Therefore, the power transmission capacity estimation device updates the primary weather forecast data using multiple models, including models that are set so that a greater amount of cloud cover results in lower temperatures or higher temperatures, depending on the time of day. As a result, the power transmission capacity estimation device can use models with relatively high estimation accuracy, and thus can estimate the power transmission capacity with relatively good accuracy.

[0014] Furthermore, the estimation unit may select the largest of the multiple power transmission capacities as the estimated power transmission capacity.

[0015] If the estimated transmission capacity is too small compared to the actual transmission capacity, the flexible operation of transmission lines will be hindered. The transmission capacity estimation device takes into account temperature prediction errors in all of the transmission capacity estimates obtained using multiple secondary temperature forecast data, so problems are unlikely to occur regardless of which of the multiple transmission capacity estimates is selected. For this reason, the transmission capacity estimation device selects the largest transmission capacity among the multiple transmission capacity estimates so that the estimated transmission capacity is not too small compared to the actual transmission capacity. This enables flexible operation of transmission lines.

[0016] Furthermore, the estimation unit may select one transmission capacity from the plurality of transmission capacities as the estimated transmission capacity, depending on at least one of the estimated day, time, and area.

[0017] The models used by the power transmission capacity estimation device may have higher estimation accuracy in certain seasons, such as summer, or at times of day when temperatures are high, or in areas closer to the sea than inland. Therefore, the power transmission capacity estimation device selects one transmission capacity from multiple available capacities depending on at least one of the day, time, and area being estimated. This allows the power transmission capacity estimation device to estimate the transmission capacity with relatively high accuracy.

[0018] Furthermore, the correction unit may calculate the difference between the maximum value of the predicted temperature shown in the first temperature forecast data and the maximum value of the actual temperature shown in the actual temperature data as the temperature error data for each of the plurality of methods.

[0019] According to this, the power transmission capacity estimation device calculates the difference between the maximum predicted temperature value and the maximum actual temperature value during the first period as temperature error data. In other words, since the impact of temperature on the power transmission capacity is greatest when the temperature is at its maximum, the power transmission capacity estimation device calculates temperature error data for the case where the temperature is at its maximum. As a result, the power transmission capacity estimation device can estimate the power transmission capacity on the safe side by correcting the second temperature forecast data based on the case where the impact is greatest. Therefore, the power transmission capacity estimation device enables flexible and safe operation of power transmission lines with a simple configuration.

[0020] Furthermore, the correction unit may determine, for each of the multiple methods, whether the predicted temperature value shown in the first temperature forecast data is smaller than the actual temperature value shown in the actual temperature data, and extract the predicted temperature value and the actual temperature value when the predicted temperature value is smaller than the actual temperature value to calculate the temperature error data.

[0021] According to this, the power transmission capacity estimation device determines whether the predicted value of the temperature in the first period is smaller than the actual value of the temperature, extracts the predicted value of the temperature and the actual value of the temperature when the predicted value of the temperature is smaller than the actual value of the temperature, and calculates temperature error data. That is, the power transmission capacity estimation device calculates temperature error data using data on the safe side when the predicted value of the temperature is smaller than the actual value of the temperature. According to this, the power transmission capacity estimation device can achieve flexible and safe operation of the power transmission line with a simple configuration.

[0022] Further, for each of the plurality of methods, the correction unit may extract values within a predetermined range from the predicted value of the temperature indicated by the second temperature prediction data in the predicted value of the temperature indicated by the first temperature prediction data, and calculate the temperature error data.

[0023] If the past predicted value deviates too much from the future predicted value, there is a risk that the calculation accuracy will decrease if such a past predicted value is used for calculating the error of the future predicted value. Therefore, the power transmission capacity estimation device extracts values within a predetermined range from the predicted value of the temperature in the second period in the predicted value of the temperature in the first period, and calculates temperature error data. As a result, since the power transmission capacity estimation device does not use values outside the predetermined range from the predicted value of the temperature in the future second period in the predicted value of the temperature in the past first period in the calculation of the temperature error data, the calculation accuracy of the temperature error data can be improved. According to this, the power transmission capacity estimation device can achieve flexible and accurate operation of the power transmission line with a simple configuration.

[0024] Further, for each of the plurality of methods, the correction unit may calculate the temperature error data using, as the prediction error of the temperature, the value at the 100th percentile in the difference between the predicted value of the temperature indicated by the first temperature prediction data and the actual value of the temperature indicated by the temperature actual data.

[0025] According to this, the power transmission capacity estimation device calculates temperature error data by using, as the temperature prediction error, the value at the 100th percentile in the difference between the predicted value and the actual value of the temperature during the first period. That is, when the difference between the predicted value and the actual value of the temperature varies, the power transmission capacity estimation device adopts, on the safe side, the value at the 100th percentile in the difference as the temperature prediction error and calculates the temperature error data. Thereby, according to the power transmission capacity estimation device, it is possible to achieve flexible and safe operation of the transmission line with a simple configuration.

[0026] Further, the acquisition unit may further acquire second weather prediction data having a shorter prediction period and a shorter distribution delay time than the first weather prediction data, and the correction unit may correct, for each of the plurality of methods, the second temperature prediction data in a period shorter than the second period after the predetermined time point by using the second weather prediction data.

[0027] When the power transmission capacity estimation device acquires the first weather prediction data, if the distribution delay time of the first weather prediction data is long, the accuracy of correcting the second temperature prediction data using the first weather prediction data may decrease. For this reason, the power transmission capacity estimation device acquires second weather prediction data having a shorter prediction period but a shorter distribution delay time than the first weather prediction data, and corrects the second temperature prediction data in a period shorter than the second period after the predetermined time point by using the second weather prediction data. Thereby, the power transmission capacity estimation device can improve the accuracy of correcting the second temperature prediction data in a period shorter than the second period after the predetermined time point. Further, the power transmission capacity estimation device can easily acquire the second weather prediction data from, for example, the Local Model (LFM) of the Japan Meteorological Agency. Thus, according to the power transmission capacity estimation device, it is possible to achieve flexible and accurate operation of the transmission line with a simple configuration.

[0028] Furthermore, the acquisition unit may acquire second weather forecast data with a shorter forecast period and shorter update interval than the first weather forecast data, and the correction unit may, for each of the multiple methods, use the second weather forecast data to correct the second temperature forecast data for a period shorter than the second period after the predetermined time.

[0029] When the power transmission capacity estimation device acquires the first weather forecast data, if the update interval of the first weather forecast data is long, the accuracy of correcting the second temperature forecast data using the first weather forecast data may decrease. Therefore, the power transmission capacity estimation device acquires the second weather forecast data, which has a shorter forecast period but a shorter update interval than the first weather forecast data, and uses the second weather forecast data to correct the second temperature forecast data for a period shorter than the second period after a predetermined time. As a result, the power transmission capacity estimation device can improve the accuracy of correcting the second temperature forecast data for a period shorter than the second period after a predetermined time. In addition, the power transmission capacity estimation device can easily acquire the second weather forecast data from, for example, the Japan Meteorological Agency's Local Facility Model (LFM). As a result, the power transmission capacity estimation device enables flexible and accurate operation of power transmission lines with a simple configuration.

[0030] Furthermore, the acquisition unit may acquire first weather forecast data further including first altitude data indicating the altitude of the prediction point, and weather observation data further including second altitude data indicating the altitude of the observation point, and the correction unit may, for each of the plurality of methods, further use the first altitude data and the second altitude data to correct the second temperature forecast data according to the altitude of the power transmission line in the predetermined area.

[0031] Temperature varies with altitude. For example, temperature decreases at higher altitudes and increases at lower altitudes. Therefore, the power transmission capacity estimation device uses the first altitude data of the prediction point and the second altitude data of the observation point to correct the second temperature prediction data according to the altitude of the power transmission line. As a result, the power transmission capacity estimation device can correct the second temperature prediction data with high accuracy, enabling flexible and accurate operation of power transmission lines with a simple configuration.

[0032] Furthermore, the acquisition unit may acquire the first weather forecast data which further includes data showing at least one predicted value of wind speed, solar radiation, and precipitation in the predetermined area, and the estimation unit may further use the data showing at least one predicted value of wind speed, solar radiation, and precipitation included in the first weather forecast data to estimate the power transmission capacity.

[0033] The transmission capacity is also affected by wind speed, solar radiation, or precipitation. For example, high wind speed around a transmission line lowers the temperature of the transmission line, high solar radiation raises the temperature of the transmission line, and high precipitation lowers the temperature of the transmission line, thus affecting the transmission capacity. Therefore, the transmission capacity estimation device acquires first weather forecast data that further includes data showing at least one predicted value of wind speed, solar radiation, and precipitation, and uses this data to estimate the transmission capacity. As a result, the transmission capacity estimation device can estimate the transmission capacity with high accuracy, enabling flexible and accurate operation of transmission lines with a simple configuration.

[0034] Furthermore, the present invention can be realized not only as such a power transmission capacity estimation device, but also as a power transmission capacity estimation method in which characteristic processing performed by a processing unit included in the power transmission capacity estimation device is defined as steps. The present invention can also be realized as a program for causing a computer to execute the steps included in the power transmission capacity estimation method, or as a recording medium such as a computer-readable CD-ROM on which the program is recorded. The program can then be distributed via the recording medium and a transmission medium such as the Internet. Furthermore, the present invention can also be realized as an integrated circuit comprising a processing unit included in the power transmission capacity estimation device. [Effects of the Invention]

[0035] The power transmission capacity estimation device, etc., according to the present invention allows for flexible operation of power transmission lines with a simple configuration. [Brief explanation of the drawing]

[0036] [Figure 1] This diagram shows the connection relationship between the power transmission capacity estimation device and the weather data management device according to the embodiment. [Figure 2] This is a block diagram showing the functional configuration of a power transmission capacity estimation device according to an embodiment. [Figure 3A] This figure shows an example of first temperature forecast data or second temperature forecast data included in the first weather forecast data stored in the storage unit of the power transmission capacity estimation device according to the embodiment. [Figure 3B] This figure shows an example of actual temperature data included in the weather observation data stored in the memory unit of the power transmission capacity estimation device according to the embodiment. [Figure 4] This flowchart shows the process (transmission capacity estimation method) for estimating the transmission capacity, which is the operational capacity of a power transmission line, of the power transmission capacity estimation device according to the embodiment. [Figure 5] This flowchart shows the process by which the correction unit according to the embodiment corrects the second temperature forecast data. [Figure 6] This flowchart shows the process by which the correction unit according to the embodiment calculates temperature error data using Method 1. [Figure 7] This diagram illustrates the process by which the correction unit according to the embodiment acquires the maximum value of the predicted temperature and the maximum value of the actual temperature. [Figure 8] This figure illustrates the process by which the correction unit according to the embodiment calculates the 100th percentile value of the difference between the predicted temperature and the actual temperature. [Figure 9] This flowchart shows the process by which the correction unit according to the embodiment calculates temperature error data using method 2. [Figure 10] This flowchart shows the process by which the correction unit according to the embodiment calculates temperature error data using method 3. [Figure 11] This flowchart shows the process by which the correction unit according to the embodiment corrects the second temperature forecast data in multiple ways. [Figure 12] This flowchart shows the process by which the correction unit according to the embodiment corrects the second temperature forecast data using the second weather forecast data. [Figure 13] This figure shows the second temperature forecast data after correction by the correction unit according to the embodiment. [Figure 14] This flowchart shows the process by which the estimation unit according to the embodiment estimates the power transmission capacity. [Figure 15] This figure shows the estimated power transmission capacity calculated by the estimation unit according to the embodiment. [Modes for carrying out the invention]

[0037] The following description will explain, with reference to the drawings, a power transmission capacity estimation device and a power transmission capacity estimation method according to embodiments (including modifications thereof) of the present invention. Note that the embodiments described below are all general or specific examples. The numerical values, components, arrangement and connection configurations of components, steps, and the order of steps shown in the following embodiments are examples only and are not intended to limit the present invention.

[0038] (Embodiment) [1. Description of the configuration of the power transmission capacity estimation device 100] First, the configuration of the power transmission capacity estimation device 100 will be described. Figure 1 is a diagram showing the connection relationship between the power transmission capacity estimation device 100 and the weather data management device 200 according to this embodiment. Figure 2 is a block diagram showing the functional configuration of the power transmission capacity estimation device 100 according to this embodiment. Figure 3A is a diagram showing an example of the first temperature forecast data or the second temperature forecast data included in the first weather forecast data 151 stored in the storage unit 150 of the power transmission capacity estimation device 100 according to this embodiment. Figure 3B is a diagram showing an example of the actual temperature data included in the weather observation data 153 stored in the storage unit 150 of the power transmission capacity estimation device 100 according to this embodiment.

[0039] The power transmission capacity estimation device 100 is a device that estimates the power transmission capacity, which is the operational capacity of a power transmission line. A power transmission line is an overhead high-voltage power line (high-voltage line) used to transmit electricity generated at power generation facilities such as power plants to substations, distribution stations, or consumers, and is, for example, located on the commercial power grid of a power company. The operational capacity (power transmission capacity) of a power transmission line is the capacity of electricity that can be supplied by the power transmission line in operation (e.g., MW), and it fluctuates depending on the temperature around the power transmission line. For example, if the temperature around the power transmission line is high, the operational capacity (power transmission capacity) of the power transmission line decreases due to a decrease in the strength of the power transmission line due to heat, and if the temperature around the power transmission line is low, the operational capacity (power transmission capacity) of the power transmission line increases.

[0040] Specifically, as shown in Figures 1 and 2, the power transmission capacity estimation device 100 is connected to the weather data management device 200 via a communication network 300 and is a computer that acquires information from the weather data management device 200 and estimates the power transmission capacity. The power transmission capacity estimation device 100 may be implemented by a general-purpose computer system such as a personal computer executing a program, or it may be implemented by a dedicated computer system. The communication network 300 is a computer network such as the Internet, including wired or wireless LANs (Local Area Networks).

[0041] The meteorological data management device 200 is a device that stores meteorological information such as temperature, wind speed, solar radiation, and precipitation at a predetermined time and location. For example, the meteorological data management device 200 is a computer or other device installed at the Japan Meteorological Agency, or a computer or other device that acquires data from the Japan Meteorological Agency. As shown in Figure 1, the meteorological data management device 200 includes a mesoscale model data storage unit 210, a local model data storage unit 220, and an AMeDAS data storage unit 230.

[0042] The mesoscale model data storage unit 210 is a memory that stores (reserves) data predicted by the Japan Meteorological Agency's mesoscale model (MSM). The mesoscale model (MSM) uses a horizontal grid spacing of 5 km as its computational domain, covering Japan and its surrounding seas. With a delivery delay of approximately 2.5 hours and updates every 3 hours (8 times a day), it performs prediction calculations up to 39 hours in advance, making it a model capable of predicting weather phenomena several hours to one day ahead. Specifically, the mesoscale model data storage unit 210 stores the first weather forecast data 211. The first weather forecast data 211 is a collection of data including predicted values ​​for meteorological information such as temperature, wind speed, solar radiation, precipitation, altitude, atmospheric pressure, humidity, and cloud cover. For this meteorological information, the first weather forecast data 211 has a mesh resolution of 5 km horizontal grid spacing, and is updated every 3 hours (8 times a day), with each hour representing one cross-section, for a total of 39 cross-sections (39 hours of data for each hourly cross-section).

[0043] The local model data storage unit 220 is a memory that stores (reserves) data predicted by the Japan Meteorological Agency's local model (LFM). The local model (LFM) is a model that can grasp meteorological phenomena for a short period (up to 10 hours ahead) by performing prediction calculations with a finer horizontal grid spacing (2 km), a shorter delivery delay time (approximately 1.5 hours), and a shorter update interval (updated every hour (24 times a day)) than the mesoscale model. Specifically, the local model data storage unit 220 stores the second weather forecast data 221. The second weather forecast data 221 is a collection of data that includes predicted values ​​of meteorological information such as temperature, similar to the first weather forecast data 211. For this meteorological information, the second weather forecast data 221 has a high resolution mesh with a horizontal grid spacing of 2 km, and is updated every hour (24 times a day), with each hour being one cross-section, and a total of 10 cross-sections (10 hours of data for each hourly cross-section).

[0044] The AMeDAS data storage unit 230 is a memory that stores (retains) data observed by the Japan Meteorological Agency's AMeDAS (Automated Meteorological Data Acquisition System). The AMeDAS data storage unit 230 can acquire data from each AMeDAS station every hour, with a delivery delay of several minutes. The AMeDAS data storage unit 230 stores meteorological observation data 231. Meteorological observation data 231 is a collection of data including observed values ​​of meteorological information such as temperature, wind speed, wind direction, sunshine duration, and precipitation. For this meteorological information, the meteorological observation data 231 has hourly data for each AMeDAS observation point.

[0045] The power transmission capacity estimation device 100 acquires first weather forecast data 211, second weather forecast data 221, and weather observation data 231 from the weather data management device 200 via the communication network 300. The power transmission capacity estimation device 100 then uses the first weather forecast data 211, second weather forecast data 221, and weather observation data 231 to estimate the power transmission capacity, which is the operational capacity of the power transmission lines. The specific configuration of this power transmission capacity estimation device 100 is described in detail below.

[0046] As shown in Figure 2, the power transmission capacity estimation device 100 includes an acquisition unit 110, a correction unit 120, an estimation unit 130, an output unit 140, and a storage unit 150. As shown in Figure 1, the power transmission capacity estimation device 100 also includes input units such as a keyboard and mouse, and display units such as a liquid crystal display, but a detailed explanation of these is omitted.

[0047] The acquisition unit 110 acquires the first weather forecast data 211, the second weather forecast data 221, and the weather observation data 231. Specifically, the acquisition unit 110 acquires the first weather forecast data 211, the second weather forecast data 221, and the weather observation data 231 from the weather data management device 200 via the communication network 300.

[0048] As described above, the first weather forecast data 211 is a collection of data that includes predicted temperature values. Therefore, the first weather forecast data 211 includes predicted temperature values ​​for a specified area during a first period prior to a specified time (for example, the past 3 or 5 years from the present time), and predicted temperature values ​​for the specified area during a second period after a specified time (for example, from 1 hour ahead to 39 hours ahead). The specified area can be any size, but for example, it is an area within the primary subdivision area (weather forecast issuance area) of the Japan Meteorological Agency, such as the Osaka Prefecture area, the northern Hyogo Prefecture area, or the southern Hyogo Prefecture area. The same applies to the following. Thus, the first weather forecast data 211 includes first temperature forecast data and second temperature forecast data showing predicted temperature values ​​for a specified area during the first period prior to a specified time and the second period after a specified time.

[0049] The first temperature forecast data consists of predicted temperature values ​​for the past 39 hours at each point (points A to E in Figure 3A) in a predetermined area predicted by a mesoscale model (MSM), as shown in Figure 3A, collected over a first period (for example, the past 3 or 5 years from the present). Similarly, the second temperature forecast data consists of predicted temperature values ​​for the second period (for example, from 1 hour ahead to 39 hours ahead) at each point (points A to E in Figure 3A) in a predetermined area predicted by a mesoscale model (MSM), as shown in Figure 3A.

[0050] Furthermore, as mentioned above, the first weather forecast data 211 also includes predicted values ​​such as altitude, wind speed, solar radiation, and precipitation. In other words, the first weather forecast data 211 further includes first altitude data showing the altitude of the forecast point in a predetermined area. The first weather forecast data 211 further includes data showing at least one predicted value for wind speed, solar radiation, and precipitation in the predetermined area during the first and second periods. The first altitude data included in the first weather forecast data 211, as well as the data showing predicted values ​​for wind speed, solar radiation, and precipitation, etc., are also data corresponding to each location and each time, as shown in Figure 3A.

[0051] The second weather forecast data 221, like the first weather forecast data 211, also includes third temperature forecast data showing predicted temperature values ​​for a predetermined area. In this embodiment, the third temperature forecast data shows predicted temperature values ​​for a predetermined period (e.g., 1 hour) prior to a predetermined time (e.g., the present time) in the predetermined area, and predicted temperature values ​​for a period shorter than the second period after the predetermined time (e.g., from 1 hour ahead to 10 hours ahead). Furthermore, the second weather forecast data 221, like the first weather forecast data 211, includes third altitude data showing the altitude of the forecast point in the predetermined area, and data showing at least one predicted value of wind speed, solar radiation, and precipitation in the predetermined area during the first period.

[0052] Furthermore, as mentioned above, the forecast period (up to 10 hours ahead) of the second weather forecast data 221 is shorter than that of the first weather forecast data 211 (up to 39 hours ahead), and the delivery delay time (approximately 1.5 hours) is shorter than that of the first weather forecast data 211 (approximately 2.5 hours). In addition, as mentioned above, the update interval (every hour) of the second weather forecast data 221 is shorter than that of the first weather forecast data 211 (every 3 hours). For this reason, the second weather forecast data 221 has a shorter forecast period and a shorter delivery delay time than the first weather forecast data 211. Furthermore, the second weather forecast data 221 has a shorter forecast period and a shorter update interval than the first weather forecast data 211. Each data point included in the second weather forecast data 221 is, for example, data where the time on the horizontal axis is 10 hours, as shown in Figure 3A.

[0053] As described above, meteorological observation data 231 is a collection of data including actual values ​​such as temperature. Therefore, meteorological observation data 231 includes temperature data showing actual temperature values ​​in a designated area during a first period prior to a given time (for example, the past 3 or 5 years from the present). Meteorological observation data 231 further includes second altitude data showing the altitude of observation points in the designated area. The temperature data is data collected over a first period (for example, the past 3 or 5 years from the present) showing actual temperature values ​​for the past 39 hours at each point in a designated area (in Figure 3B, five points from point A to point E) observed by AMeDAS, as shown in Figure 3B. The second altitude data shows the altitude of each point.

[0054] The acquisition unit 110 writes the acquired first weather forecast data 211, second weather forecast data 221, and weather observation data 231 to the first weather forecast data 151, second weather forecast data 152, and weather observation data 153 stored in the storage unit 150. In other words, the acquisition unit 110 updates the first weather forecast data 151 by writing the first temperature forecast data, second temperature forecast data, first altitude data, and data indicating predicted values ​​for wind speed, solar radiation, and precipitation, etc., included in the first weather forecast data 211 to the first weather forecast data 151. The acquisition unit 110 also updates the second weather forecast data 152 by writing the third temperature forecast data, third altitude data, and data indicating predicted values ​​for wind speed, solar radiation, and precipitation, etc., included in the second weather forecast data 221 to the second weather forecast data 152. Furthermore, the acquisition unit 110 updates the weather observation data 153 by writing the actual temperature data and second altitude data, etc., included in the weather observation data 231 to the weather observation data 153.

[0055] For example, if a thermometer at an AMeDAS observation site is installed at an altitude of 1.5m above the ground, the second altitude data in the meteorological observation data 231 may be known in advance. In this case, the meteorological observation data 231 may not contain the second altitude data, but the second altitude data may be written to the meteorological observation data 153 in advance, and the acquisition unit 110 may acquire the second altitude data from the meteorological observation data 153. The same applies to other data.

[0056] The correction unit 120 uses the first temperature forecast data and actual temperature data to calculate temperature error data indicating the temperature forecast error in a predetermined area, and corrects the second temperature forecast data using the calculated temperature error data. The temperature forecast error is the amount of deviation (error) between the predicted temperature value and the actual temperature value. Specifically, the correction unit 120 calculates a plurality of corrected second temperature forecast data by correcting the second temperature forecast data in a plurality of ways. In this embodiment, the correction unit 120 updates the first weather forecast data 211 using a plurality of models to calculate a plurality of updated first weather forecast data 211, and calculates a plurality of second temperature forecast data using the first temperature forecast data and second temperature forecast data included in each of the plurality of first weather forecast data 211. For example, the correction unit 120 updates the first weather forecast data 211 using a plurality of models that use data from areas of different sizes among the first temperature forecast data and second temperature forecast data included in the first weather forecast data 211. Furthermore, the correction unit 120 updates the first weather forecast data 211 using multiple models, including a model set to either lower the temperature when there is more cloud cover, or higher the temperature when there is more cloud cover, depending on the time of day.

[0057] Specifically, for each of the multiple methods, the correction unit 120 calculates the difference between the maximum value of the predicted temperature shown in the first temperature forecast data and the maximum value of the actual temperature shown in the actual temperature data as temperature error data. The correction unit 120 also determines for each of the multiple methods whether the predicted temperature shown in the first temperature forecast data is smaller than the actual temperature shown in the actual temperature data, and extracts the predicted and actual temperature values ​​when the predicted temperature is smaller than the actual temperature to calculate temperature error data. Furthermore, for each of the multiple methods, the correction unit 120 extracts a value within a predetermined range from the predicted temperature shown in the second temperature forecast data to the predicted temperature shown in the first temperature forecast data to calculate temperature error data. In addition, for each of the multiple methods, the correction unit 120 calculates temperature error data by using the 100th percentile value of the difference between the predicted temperature shown in the first temperature forecast data and the actual temperature shown in the actual temperature data as the temperature forecast error.

[0058] Furthermore, the correction unit 120 corrects the second temperature forecast data using the temperature error data calculated for each of the multiple methods. Specifically, the correction unit 120 further uses the first altitude data and the second altitude data for each of the multiple methods to correct the second temperature forecast data to correspond to the altitude of the power lines in the predetermined area. More specifically, the correction unit 120 uses the second weather forecast data 152 for each of the multiple methods to correct the second temperature forecast data for a period shorter than the second period after the predetermined time. A more detailed explanation of these processes performed by the correction unit 120 will be given later.

[0059] In the above processing performed by the correction unit 120, first, the acquisition unit 110 acquires the first weather forecast data 151, the second weather forecast data 152, and the weather observation data 153 stored in the storage unit 150. Then, for each of the above multiple methods, the correction unit 120 calculates temperature error data using the first temperature forecast data included in the acquired first weather forecast data 151 and the actual temperature data included in the weather observation data 153. Specifically, the correction unit 120 reads and acquires multiple models (such as the first model and the second model described later) from the model data 155 stored in the storage unit 150, and updates the first weather forecast data 211 using these multiple models. In other words, the correction unit 120 calculates the first temperature forecast data and the second temperature forecast data, etc., using each of these multiple models, and creates multiple updated first weather forecast data 211 (including the updated first temperature forecast data and the second temperature forecast data, etc.) according to these multiple models. The correction unit 120 then stores the updated first weather forecast data 211 in the storage unit 150. The correction unit 120 also calculates temperature error data for both the first weather forecast data 211 before the update and the updated first weather forecast data 211. In this way, the correction unit 120 calculates multiple temperature error data to support multiple methods, such as not using a model, using the first model, or using the second model.

[0060] Then, for each of the above multiple methods, the correction unit 120 corrects the second temperature forecast data included in the first weather forecast data 151 using the calculated temperature error data, the first altitude data included in the first weather forecast data 151, the second altitude data included in the weather observation data 153, and the third altitude data included in the second weather forecast data 152. Then, for each of the above multiple methods, the correction unit 120 writes the corrected second temperature forecast data to the first weather forecast data 151 stored in the storage unit 150, thereby updating the second temperature forecast data in the first weather forecast data 151. Alternatively, for each of the above multiple methods, the correction unit 120 may first write the calculated temperature error data to the estimation data 154 stored in the storage unit 150, read the temperature error data from the storage unit 150, and then correct the second temperature forecast data.

[0061] The estimation unit 130 uses the corrected second temperature forecast data to estimate the transmission capacity available in a predetermined area during the second period. Specifically, the estimation unit 130 selects one transmission capacity from a plurality of transmission capacities obtained using the plurality of second temperature forecast data calculated by the correction unit 120 as the transmission capacity to be estimated. For example, the estimation unit 130 selects the largest transmission capacity from the plurality of transmission capacities as the transmission capacity to be estimated. Alternatively, the estimation unit 130 selects one transmission capacity from a plurality of transmission capacities according to at least one of the day, time, and area to be estimated as the transmission capacity to be estimated.

[0062] More specifically, the estimation unit 130 estimates the transmission capacity using data indicating at least one predicted value of wind speed, solar radiation, and precipitation included in the first weather forecast data 151. For example, the acquisition unit 110 acquires the corrected first weather forecast data 151 stored in the storage unit 150 for each of the above multiple methods. Then, for each of the multiple methods, the estimation unit 130 calculates an estimated value of the transmission capacity, which is the operational capacity of the transmission line, using the second temperature forecast data included in the acquired first weather forecast data 151, as well as data indicating predicted values ​​of wind speed, solar radiation, and precipitation. In other words, the estimation unit 130 calculates multiple estimated values ​​of the transmission capacity using multiple second temperature forecast data calculated by the correction unit 120. The estimation unit 130 then selects one estimated power transmission capacity from the multiple estimated power transmission capacity values ​​it has calculated, and writes the selected estimated power transmission capacity to the estimation data 154 stored in the storage unit 150, thereby updating the estimation data 154. A more detailed explanation of the above process performed by the estimation unit 130 will be given later.

[0063] The output unit 140 outputs the power transmission capacity estimated by the estimation unit 130. For example, the acquisition unit 110 acquires the estimation data 154 stored in the storage unit 150. The output unit 140 then reads the estimated power transmission capacity contained in the acquired estimation data 154 and transmits it to an external device. Alternatively, the output unit 140 outputs the estimated power transmission capacity to a display unit such as a liquid crystal display provided in the power transmission capacity estimation device 100 to display the estimated power transmission capacity.

[0064] The memory unit 150 is a memory that stores data for estimating the transmission capacity, which is the operational capacity of the power transmission line. Specifically, the memory unit 150 stores the first weather forecast data 151, the second weather forecast data 152, the weather observation data 153, the estimation data 154, and the model data 155 mentioned above. The model data 155 is a collection of data including the first model and the second model. The first model is a mathematical model such as a neural network, and the second model is a weather model such as a WRF (Weather Research and Forecasting model). The first weather forecast data 151, the second weather forecast data 152, the weather observation data 153, and the estimation data 154 may be rewritten each time the data is updated, or the data may be accumulated.

[0065] [2. Explanation of the processing flow of the power transmission capacity estimation device 100] Next, we will explain the process by which the power transmission capacity estimation device 100 estimates the power transmission capacity, which is the operational capacity of the power transmission line. Figure 4 is a flowchart showing the process (power transmission capacity estimation method) by the power transmission capacity estimation device 100 according to this embodiment to estimate the power transmission capacity, which is the operational capacity of the power transmission line. Figure 5 is a flowchart showing the process (S104 in Figure 4) by the correction unit 120 according to this embodiment to correct the second temperature forecast data.

[0066] As shown in Figure 4, first, the acquisition unit 110 acquires first weather forecast data 211(151), second weather forecast data 221(152), and weather observation data 231(153) (S102, acquisition step). Specifically, the acquisition unit 110 acquires first weather forecast data 211(151) which includes first temperature forecast data for a first period, second temperature forecast data for a second period, first altitude data for the forecast point, and data showing at least one forecast value for wind speed, solar radiation, and precipitation in a predetermined area. The acquisition unit 110 also acquires weather observation data 231(153) which includes actual temperature data for a first period in a predetermined area, and second altitude data for the observation point. Furthermore, the acquisition unit 110 acquires second weather forecast data 221(152) which has a shorter forecast period, shorter delivery delay time, and shorter update interval than the first weather forecast data 211(151).

[0067] For example, the acquisition unit 110 can obtain 8,760 temperature prediction values ​​(= 8 times / day × 365 days × 3 years) from the mesoscale model (MSM) for each point in the past three years (first period) from the present time to the first point in the predetermined area where temperature predictions are to be made. In the example shown in Figure 3A, the acquisition unit 110 acquires the first temperature prediction data, which includes 8,760 temperature prediction values ​​for each point in the past three years at points A to E within the predetermined area. The acquisition unit 110 may also acquire data for the past five years (first period) from the present time, or it may acquire data every hour (24 times / day) instead of every three hours (8 times / day). The acquisition unit 110 may also convert the data every three hours into data every hour by performing linear interpolation. Thus, the acquisition unit 110 may acquire 43,824 temperature prediction values ​​(= 24 hours × 365 days × 4 years + 24 hours × 366 days (leap year)) for each point in time within the past five years (first period) from the present. In addition, the acquisition unit 110 acquires second temperature forecast data which includes temperature prediction values ​​from 1 hour to 39 hours ahead for points A to E within the predetermined area.

[0068] The acquisition unit 110 acquires third temperature forecast data from a local model (LFM) for the second weather forecast data 221(152), which includes the predicted temperature value for one hour ahead (the current time) predicted one hour ago at each point within the predetermined area, and the predicted temperature values ​​for one hour ahead to ten hours ahead from the current time. The acquisition unit 110 also acquires actual temperature values ​​from AMeDAS for each point within the predetermined area at each point within the past three or five years (first period), etc., from the current time, for the weather observation data 231(153).

[0069] Then, the correction unit 120 uses the first temperature forecast data and actual temperature data acquired by the acquisition unit 110 to calculate temperature error data indicating the temperature forecast error in a predetermined area, and corrects the second temperature forecast data using the calculated temperature error data (S104) (correction step). Specifically, the correction unit 120 calculates multiple corrected second temperature forecast data by correcting the second temperature forecast data in multiple ways. In other words, as shown in Figure 5, the correction unit 120 uses the first temperature forecast data and actual temperature data acquired by the acquisition unit 110 to calculate multiple temperature error data in multiple ways (S105), and corrects the second temperature forecast data in multiple ways using the calculated multiple temperature error data (S106). A detailed explanation of the process by which the correction unit 120 calculates temperature error data in multiple ways (S105) and the process by which the correction unit 120 corrects the second temperature forecast data in multiple ways (S106) will be described later.

[0070] Then, the estimation unit 130 uses the first weather forecast data 151, which includes the second temperature forecast data corrected by the correction unit 120, to estimate the transmission capacity available in a predetermined area during the second period (S108, estimation step). Specifically, the estimation unit 130 selects one transmission capacity from among multiple transmission capacities obtained using multiple second temperature forecast data calculated by the correction unit 120 as the transmission capacity available to estimate. A detailed explanation of the process by which the estimation unit 130 estimates the transmission capacity (S108) will be given later.

[0071] The output unit 140 then outputs the power transmission capacity estimated by the estimation unit 130 (S110, output step). For example, the output unit 140 transmits the power transmission capacity to an external device, or outputs it to a display unit such as a liquid crystal display of the power transmission capacity estimation device 100 to display the estimated value of the power transmission capacity. Specifically, the output unit 140 outputs a graph of the estimated value of the power transmission capacity shown in Figure 15 described later, or numerical values ​​indicating the estimated value of the power transmission capacity. The output unit 140 may also output temperature error data calculated by the correction unit 120, or second temperature forecast data corrected by the correction unit 120. For example, the output unit 140 may output a graph of the second temperature forecast data shown in Figure 13 described later, or numerical values ​​indicating the second temperature forecast data.

[0072] The power transmission capacity estimation device 100 performs the above process for all areas for which it wants to estimate the power transmission capacity, and estimates the power transmission capacity for all areas. In this way, the process by which the power transmission capacity estimation device 100 estimates the power transmission capacity, which is the operational capacity of the power transmission lines, is completed.

[0073] Next, the process by which the correction unit 120 calculates temperature error data using multiple methods (S105 in Figure 5) will be described in detail. Figure 6 is a flowchart showing the process by which the correction unit 120 according to this embodiment calculates temperature error data using method 1. Figure 7 is a diagram illustrating the process by which the correction unit 120 according to this embodiment obtains the maximum value of the predicted temperature and the maximum value of the actual temperature (S206 in Figure 6). Figure 8 is a diagram illustrating the process by which the correction unit 120 according to this embodiment calculates the 100th percentile value of the difference between the predicted temperature and the actual temperature (S216 in Figure 6). Figure 9 is a flowchart showing the process by which the correction unit 120 according to this embodiment calculates temperature error data using method 2. Figure 10 is a flowchart showing the process by which the correction unit 120 according to this embodiment calculates temperature error data using method 3.

[0074] First, the process by which the correction unit 120 calculates temperature error data using method 1 will be explained. As shown in Figure 6, the correction unit 120 acquires first temperature forecast data and actual temperature data from first weather forecast data 151 and weather observation data 153 (S202). Specifically, the correction unit 120 acquires first temperature forecast data from first weather forecast data 151 acquired by the acquisition unit 110, and acquires actual temperature data from weather observation data 153 acquired by the acquisition unit 110.

[0075] The correction unit 120 then performs a correction to match the altitude of the predicted temperature in the first temperature forecast data with the actual temperature in the actual temperature data (S204). Specifically, the correction unit 120 obtains first altitude data from the first weather forecast data 151 and second altitude data from the weather observation data 153. Then, the correction unit 120 uses the first altitude data and the second altitude data to perform a correction to match the altitude of the first temperature forecast data with the actual temperature data. For example, the correction unit 120 corrects the actual temperature data with a correction rate of 0.65℃ / 100m so that the actual temperature data in the second altitude data becomes the same as the data in the first altitude data.

[0076] Then, the correction unit 120 obtains the maximum value of the predicted temperature shown in the first temperature forecast data and the maximum value of the actual temperature shown in the actual temperature data (S206). Specifically, the correction unit 120 obtains the maximum value of the predicted temperature and the maximum value of the actual temperature for the first temperature forecast data and actual temperature data that have been corrected to match altitude in the above process (S204). For example, as shown in Figure 7, the correction unit 120 obtains the maximum value of the predicted temperature, which is the predicted temperature TA38 = 34.0℃ for 38 hours ahead at point A, and the maximum value of the actual temperature, which is the actual temperature TD39 = 36.0℃ for 39 hours ahead at point D. In other words, the correction unit 120 obtains the maximum value of the predicted temperature and the maximum value of the actual temperature for each period corresponding to the second period of the first period in a predetermined area. The period corresponding to the second period of the first period is a period of the same length as the second period of the first period in the same season or month as the second period of the first period.

[0077] Then, the correction unit 120 determines whether the predicted temperature value shown in the first temperature forecast data is smaller than the actual temperature value shown in the actual temperature data (S208). Specifically, for each maximum value of the predicted temperature value and the maximum value of the actual temperature value obtained in the above process (S206), the correction unit 120 determines whether the maximum value of the predicted temperature value is smaller than the maximum value of the actual temperature value.

[0078] The correction unit 120 extracts the predicted temperature value and the actual temperature value if the predicted temperature value shown in the first temperature forecast data is smaller than the actual temperature value shown in the actual temperature data (YES in S208) (S210). The correction unit 120 does not extract the predicted temperature value and the actual temperature value if the predicted temperature value shown in the first temperature forecast data is greater than or equal to the actual temperature value shown in the actual temperature data (NO in S208) (S212). In other words, the correction unit 120 extracts the maximum value of the predicted temperature and the maximum value of the actual temperature only if the maximum value of the predicted temperature is smaller than the maximum value of the actual temperature.

[0079] The correction unit 120 then extracts a value from the predicted temperature shown in the second temperature forecast data that falls within a predetermined range from the predicted temperature shown in the first temperature forecast data (S214). Specifically, the correction unit 120 determines whether the maximum value of the predicted temperature extracted in the above process (S210) falls within a predetermined range from the maximum value of the predicted temperature shown in the second temperature forecast data, and extracts the maximum value of the predicted temperature if it falls within the predetermined range. The correction unit 120 obtains the maximum value of the predicted temperature shown in the second temperature forecast data using the same method as the process (S206) used to obtain the maximum value of the predicted temperature shown in the first temperature forecast data. For example, if the maximum value of the predicted temperature shown in the second temperature forecast data is 25°C, the correction unit 120 extracts a value from the maximum value of the predicted temperature shown in the first temperature forecast data that falls within the range of 25°C ± 2°C (23°C to 27°C).

[0080] The correction unit 120 then calculates temperature error data (S216) by using the 100th percentile value of the difference between the predicted temperature value shown in the first temperature forecast data and the actual temperature value shown in the actual temperature data as the temperature forecast error. Specifically, the correction unit 120 calculates the 100th percentile value of the difference between the maximum value of the predicted temperature extracted in the above process (S214) and the corresponding maximum value of the actual temperature. For example, if the maximum value of the predicted temperature shown in the second temperature forecast data is 25°C, and the 100th percentile value of the difference between the maximum value of the predicted temperature shown in the first temperature forecast data and the maximum value of the actual temperature is 5.7°C, the correction unit 120 calculates that the temperature error data is 5.7°C.

[0081] In this embodiment, in the above processing (S214 and S216), the correction unit 120 performs the following processing. As shown in Figure 8, the correction unit 120 samples the prediction error (difference) for cases where the maximum value of the predicted temperature shown in the first temperature forecast data is ±2°C, in 1°C increments, within the range of -10 to 40°C, and calculates the percentile value of the prediction error. Next, the correction unit 120 obtains the maximum value of the predicted temperature shown in the second temperature forecast data. For example, if the maximum value of the predicted temperature shown in the second temperature forecast data is 25°C, the correction unit 120 calculates from the data shown in Figure 8 that the 100th percentile value of the prediction error when the predicted temperature is 25°C is 5.7°C. Thus, the correction unit 120 calculates that the temperature error data is 5.7°C.

[0082] As described above, the process by which the correction unit 120 calculates the temperature error data using method 1 is completed.

[0083] Next, the process by which the correction unit 120 calculates temperature error data using method 2 will be described. As shown in Figure 9, first, the correction unit 120 updates the first weather forecast data 211 using the first model (S201 in Figure 9). In other words, the correction unit 120 applies the first model to the first weather forecast data 211 to update the first temperature forecast data and the second temperature forecast data included in the first weather forecast data 211. In this embodiment, the first model is a neural network model. In the neural network model, the objective function is the actual temperature data, and the explanatory variables are the first temperature forecast data and the second temperature forecast data to construct a machine learning model (the algorithm is a neural network). One model is constructed for each point in the predetermined area. In model creation, processes such as data standardization, learning using the holdout method (3 years as training data and 2 years as test data), and cross-validation to prevent overfitting (the data is divided into 100 parts, and 1% of them is used as validation data) are performed. In model creation, other processes that are generally performed in neural networks are also performed.

[0084] Furthermore, the neural network model is configured so that, depending on the time of day, the temperature decreases as cloud cover increases, or increases as cloud cover increases. In other words, the correction unit 120 updates the first weather forecast data 211 using a model configured so that, depending on the time of day, the temperature decreases as cloud cover increases, or increases as cloud cover increases. Specifically, it is as follows: The effect of cloud cover on ground temperature is reversed between daytime and nighttime. That is, during the day, a large amount of cloud cover suppresses the rise in temperature, while at night, a large amount of cloud cover suppresses the decrease in temperature. For this reason, the neural network model performs data conversion using the following procedure. First, the sunrise and sunset times for a given year are calculated from the latitude and longitude of each point in the predetermined area. Then, the period from sunrise to sunset is set as daytime, and the period from sunset to sunrise is set as nighttime. Then, coefficients are set for each time of day for daytime and nighttime. The conversion formula is as follows.

[0085] Daytime: Converted value = (100 - cloud cover) × coefficient set for each hour × 1 Nighttime: Converted value = (100 - cloud cover) × coefficient set for each hour × (-1)

[0086] Then, the correction unit 120 calculates temperature error data using the first weather forecast data 211 updated by applying the first model (S202-S216 in Figure 9). This process of calculating temperature error data (S202-S216 in Figure 9) is performed by the same process as the process in Method 1 described above (S202-S216 in Figure 6). Note that the process of adjusting altitude in Method 1 (S204 in Figure 6) is performed within the process of updating temperature error data in the neural network model (S201 in Figure 9).

[0087] Next, the process by which the correction unit 120 calculates temperature error data using method 3 will be described. As shown in Figure 10, first, the correction unit 120 updates the first weather forecast data 211 using the second model (S201 in Figure 10). In other words, the correction unit 120 applies the second model to the first weather forecast data 211 and updates the first temperature forecast data and the second temperature forecast data included in the first weather forecast data 211. In this embodiment, the second model is a WRF model. In the WRF model, in order to reduce the computational load, only the area for which the transmission capacity is to be estimated is calculated with high resolution, and the area outside of that is calculated coarsely (nesting method). In model creation, other processes that are generally performed in WRF are also executed.

[0088] The WRF model (second model) uses data from an area larger than the area for which the transmission capacity is estimated. In contrast, the neural network model (first model) uses data from the area for which the transmission capacity is estimated. In other words, the neural network model (first model) and the WRF model (second model) are models that use data from areas of different sizes. Therefore, the correction unit 120 updates the first weather forecast data 211 using multiple models that use data from areas of different sizes among the first temperature forecast data and second temperature forecast data included in the first weather forecast data 211.

[0089] Then, the correction unit 120 calculates temperature error data using the first weather forecast data 211 which has been updated by applying the second model (S202-S216 in Figure 10). This process for calculating temperature error data (S202-S216 in Figure 10) is performed by the same process as the process in Method 1 described above (S202-S216 in Figure 6).

[0090] As described above, the process by which the correction unit 120 calculates temperature error data using multiple methods (methods 1 to 3) (S105 in Figure 5) is completed. In other words, in this embodiment, the correction unit 120 calculates temperature error data using multiple methods (methods 1 to 3). Method 1 is a method that does not use a model. Method 2 is a method that uses the first model. Method 3 is a method that uses the second model.

[0091] Next, the process by which the correction unit 120 corrects the second temperature forecast data in multiple ways (S106 in Figure 5) will be described in detail. Figure 11 is a flowchart showing the process by which the correction unit 120 according to this embodiment corrects the second temperature forecast data in multiple ways (S106 in Figure 5). Figure 12 is a flowchart showing the process by which the correction unit 120 according to this embodiment corrects the second temperature forecast data using the second weather forecast data 152 (S304 in Figure 11). Figure 13 is a diagram showing the second temperature forecast data after correction by the correction unit 120 according to this embodiment.

[0092] As shown in Figure 11, first, the correction unit 120 corrects the second temperature forecast data using the temperature error data for each of the above multiple methods (methods 1 to 3) (S302). Specifically, for each of the above multiple methods (methods 1 to 3), the correction unit 120 obtains the second temperature forecast data from the first weather forecast data 151, and corrects the second temperature forecast data by adding the temperature error data calculated in the above process (S105 in Figure 5) to the second temperature forecast data. For example, if the correction unit 120 calculates that the temperature error data is 5.7°C, it corrects the second temperature forecast data by adding 5.7°C to the predicted temperature value for each time in the second temperature forecast data from 1 hour ahead to 39 hours ahead.

[0093] Then, the correction unit 120 further corrects the second temperature forecast data using the second weather forecast data 152 for each of the above-mentioned methods (methods 1 to 3) (S304). Specifically, the correction unit 120 corrects the second temperature forecast data for a period shorter than the second period after a predetermined time using the second weather forecast data 152. The process by which the correction unit 120 further corrects the second temperature forecast data will be described in detail below.

[0094] As shown in Figure 12, the correction unit 120 acquires third temperature forecast data and actual temperature data from the second weather forecast data 152 and weather observation data 153 (S402). Specifically, the correction unit 120 acquires third temperature forecast data from the second weather forecast data 152 and actual temperature data from the weather observation data 153. In this embodiment, the third temperature forecast data is, for example, data showing the predicted temperature value for one hour ago predicted for the present time in a predetermined area, and the predicted temperature values ​​for time points from one hour to ten hours ahead.

[0095] Then, the correction unit 120 performs a correction to match the altitude of the predicted temperature value in the third temperature forecast data with the actual temperature value in the actual temperature data (S404). Specifically, the correction unit 120 performs a correction to match the altitude of the third temperature forecast data with the actual temperature data using the same method as the correction to match the altitude of the first temperature forecast data with the actual temperature data (S204 in Figure 6).

[0096] Then, the correction unit 120 obtains the maximum value of the predicted temperature shown in the third temperature forecast data and the maximum value of the actual temperature shown in the actual temperature data (S406). Specifically, the correction unit 120 obtains the maximum value of the predicted temperature and the maximum value of the actual temperature for the third temperature forecast data and actual temperature data that have been corrected to match altitude in the above process (S404). For example, the correction unit 120 obtains the maximum value of the predicted temperature one hour ago, which is the current temperature, and the maximum value of the actual temperature at the present time, for a predetermined area.

[0097] Then, the correction unit 120 determines whether the predicted temperature value shown in the third temperature forecast data is smaller than the actual temperature value shown in the actual temperature data (S408). Specifically, the correction unit 120 determines whether the maximum value of the predicted temperature obtained in the above process (S406) is smaller than the maximum value of the actual temperature.

[0098] If the predicted temperature is less than the actual temperature (YES in S408), the correction unit 120 calculates the temperature error data by subtracting the predicted temperature from the actual temperature (S410). If the predicted temperature is greater than or equal to the actual temperature (NO in S408), the correction unit 120 calculates that the temperature error data is 0 (S412).

[0099] Then, the correction unit 120 corrects the second temperature forecast data for a period shorter than the second period after a predetermined time (S414). Specifically, the correction unit 120 adds the temperature error data calculated in the above processing (S410 and S412) to the predicted temperature values ​​of the third temperature forecast data for a period shorter than the second period after a predetermined time, thereby correcting the predicted temperature values ​​of the third temperature forecast data for that short period. Then, the correction unit 120 corrects the second temperature forecast data by rewriting the predicted temperature values ​​of the second temperature forecast data for that short period with the corrected predicted temperature values ​​of the third temperature forecast data for that short period. For example, the correction unit 120 adds the temperature error data to the predicted temperature values ​​of the third temperature forecast data for one hour to two hours ahead, thereby correcting the predicted temperature values ​​of the third temperature forecast data for one hour to two hours ahead. The correction unit 120 then corrects the second temperature forecast data by overwriting the predicted temperature values ​​for the second temperature forecast data from one hour to two hours ahead with the predicted temperature values ​​for the third temperature forecast data from one hour to two hours ahead that have been corrected.

[0100] Returning to Figure 11, the correction unit 120 corrects the second temperature forecast data using the first altitude data for each of the above multiple methods (methods 1 to 3) (S306). Specifically, the correction unit 120 corrects the second temperature forecast data for each of the above multiple methods (methods 1 to 3) to second temperature forecast data corresponding to the altitude of the power lines in the predetermined area. In other words, the correction unit 120 corrects the second temperature forecast data with a correction rate of 0.65℃ / 100m so that the second temperature forecast data using the first altitude data becomes data at the same height as the power lines located in the predetermined area.

[0101] In this way, the correction unit 120 corrects the second temperature forecast data for each of the above methods (methods 1 to 3) to obtain corrected second temperature forecast data as shown in Figure 13. As shown in Figure 13, the corrected second temperature forecast data is smaller than the conventional values ​​considered in the most severe cross-section.

[0102] As described above, the correction unit 120 completes the process of correcting the second temperature forecast data in multiple ways (S106 in Figure 5). The timing at which the correction unit 120 performs the corrections in these multiple ways (methods 1 to 3) is not particularly limited; the corrections in these multiple ways (methods 1 to 3) may be performed in parallel (at the same time) or consecutively (at different times). In this way, the correction unit 120 calculates multiple (three in this embodiment) second temperature forecast data using the first temperature forecast data and second temperature forecast data contained in each of the updated multiple (three in this embodiment) first weather forecast data 211.

[0103] Next, the process by which the estimation unit 130 estimates the power transmission capacity (S108 in Figure 4) will be described in detail. Figure 14 is a flowchart showing the process by which the estimation unit 130 estimates the power transmission capacity (S108 in Figure 4) according to this embodiment. Figure 15 is a diagram showing the estimated value of the power transmission capacity calculated by the estimation unit 130 according to this embodiment.

[0104] As shown in Figure 14, first, the estimation unit 130 calculates multiple transmission capacities using multiple second temperature forecast data (S502). Specifically, for each of the above multiple methods (methods 1 to 3), the estimation unit 130 calculates the transmission capacity in a predetermined area during the second period using the first weather forecast data 151, which includes the second temperature forecast data corrected by the correction unit 120. For example, the estimation unit 130 calculates the transmission capacity in a predetermined area during the second period by substituting the second temperature forecast data for the predetermined area during the second period into a relational expression that shows the relationship between the second temperature forecast data and the transmission capacity. Any relational expression may be used, but for example, it is an expression in which the transmission capacity decreases as the predicted temperature value shown in the second temperature forecast data increases.

[0105] Then, the estimation unit 130 further uses data showing at least one predicted value of wind speed, solar radiation, and precipitation included in the first weather forecast data 151 to correct multiple transmission capacity (S504). In other words, the estimation unit 130 further uses data showing predicted values ​​of weather information other than temperature, such as wind speed, solar radiation, and precipitation, in a predetermined area during the second period to correct multiple (three in this embodiment) transmission capacity calculated in the above process (S502). For example, the estimation unit 130 corrects the transmission capacity in a predetermined area during the second period by substituting the data of wind speed, solar radiation, and precipitation in the predetermined area during the second period into a relational expression showing the relationship between wind speed, solar radiation, precipitation, and transmission capacity. Any relational expression may be used as the relational expression, but for example, it may be an expression in which the transmission capacity increases as the wind speed increases, the transmission capacity decreases as the solar radiation increases, and the transmission capacity increases as the precipitation increases.

[0106] The estimation unit 130 may also calculate the power transmission capacity in a predetermined area during the second period by substituting the second temperature forecast data, wind speed, solar radiation, and precipitation data for a predetermined area during the second period into a relational expression showing the relationship between the second temperature forecast data, wind speed, solar radiation, and precipitation and the power transmission capacity.

[0107] Then, the estimation unit 130 selects one transmission capacity from a plurality of (three in this embodiment) transmission capacities (S506). In other words, the estimation unit 130 selects one transmission capacity to be estimated from a plurality of transmission capacities obtained using the plurality of second temperature forecast data calculated by the correction unit 120. In this embodiment, the estimation unit 130 selects the largest transmission capacity among the plurality of transmission capacities to be estimated.

[0108] The estimation unit 130 may select one transmission capacity from a plurality of transmission capacities as the transmission capacity to be estimated, depending on at least one of the day, time, and area to be estimated. For example, in the case of method 1, which does not use a model, the estimation accuracy tends to be low in summer or on hot days. For this reason, the estimation unit 130 may select a transmission capacity calculated based on the neural network model (first model) or the WRF model (second model) on summer days or at hot times. Also, the WRF model (second model) tends to have high estimation accuracy in summer. For this reason, the estimation unit 130 may select a transmission capacity calculated based on the WRF model (second model) on summer days. Also, the neural network model (first model) tends to have higher estimation accuracy in areas closer to the sea than inland. For this reason, the estimation unit 130 may select a transmission capacity calculated based on the neural network model (first model) in areas close to the sea. The estimation unit 130 may select one transmission capacity from multiple transmission capacities based on criteria other than those described above.

[0109] In this way, the estimation unit 130 estimates (selects) the transmission capacity and obtains an estimated value of the transmission capacity as shown in Figure 15. As shown in Figure 15, the estimated value of the transmission capacity is larger than the transmission capacity that was previously estimated. Therefore, even if the expected power flow without considering the transmission capacity exceeds the conventional transmission capacity, the expected power flow will not exceed the estimated value of the transmission capacity, and thus the system can be operated without restricting the expected power flow.

[0110] As described above, the process by which the estimation unit 130 estimates the power transmission capacity (S108 in Figure 4) is completed.

[0111] [3. Explanation of the effect] The power transmission capacity estimation device 100 according to an embodiment of the present invention acquires first weather forecast data 211, which includes first and second temperature forecast data for the first and second periods, and weather observation data 231, which includes actual temperature data for the first period. For example, the power transmission capacity estimation device 100 can acquire the first weather forecast data 211 and weather observation data 231 without the need to install multiple sensors along the power transmission line route, by acquiring forecast data from the Japan Meteorological Agency's mesoscale model (MSM) and observation data from AMeDAS. Furthermore, the power transmission capacity estimation device 100 calculates temperature error data using the first temperature forecast data and actual temperature data, corrects the second temperature forecast data using the temperature error data, and estimates the power transmission capacity for the second period using the corrected second temperature forecast data. In particular, the power transmission capacity estimation device 100 calculates multiple corrected second temperature forecast data by correcting the second temperature forecast data in multiple ways, and selects one power transmission capacity from multiple power transmission capacities obtained using the multiple second temperature forecast data. In this way, the power transmission capacity estimation device 100 estimates the power transmission capacity for the second period using multiple second temperature forecast data sets with corrected errors. As a result, the power transmission capacity estimation device 100 can estimate the power transmission capacity, which is the operational capacity of the transmission line for the second period from a predetermined point in time, with relatively high accuracy, and the transmission line can be operated flexibly based on the estimated power transmission capacity. Therefore, the power transmission capacity estimation device 100 enables flexible operation of the transmission line with a simple configuration.

[0112] Furthermore, since there is no need to install multiple sensors along the power transmission line route, costs can be reduced. Also, although power transmission lines are arranged across multiple points within a predetermined area, calculating temperature error data for each point can result in excessively large errors. If temperature error data is calculated for each point and the transmission capacity is estimated for each point, controlling the transmission capacity becomes complex. For this reason, the transmission capacity estimation device 100 calculates temperature error data for each area, preventing the transmission capacity from becoming too small and enabling flexible operation of the power transmission lines with a transmission capacity that is appropriate to the actual situation.

[0113] Furthermore, the power transmission capacity estimation device 100 updates the first weather forecast data 211 using multiple models and calculates multiple second temperature forecast data using the data contained in each of the updated first weather forecast data 211. As a result, the power transmission capacity estimation device 100 can select one power transmission capacity from multiple power transmission capacities obtained using multiple second temperature forecast data calculated from multiple models. Therefore, the power transmission capacity estimation device 100 can estimate the power transmission capacity with relatively high accuracy by selecting a power transmission capacity obtained using a model with relatively high estimation accuracy.

[0114] Furthermore, the power transmission capacity estimation device 100 updates the first weather forecast data 211 using multiple models that utilize data from areas of different sizes included in the first weather forecast data 211. This allows the power transmission capacity estimation device 100 to select one power transmission capacity from multiple power transmission capacities obtained using multiple second temperature forecast data calculated from multiple models that utilize different data. Therefore, the power transmission capacity estimation device 100 can estimate the power transmission capacity with relatively high accuracy by selecting a power transmission capacity obtained using a model with relatively high estimation accuracy.

[0115] Furthermore, the effect of cloud cover on temperature can be reversed depending on the time of day. For example, during the day, a greater amount of cloud cover suppresses the rise in temperature, while at night, a greater amount of cloud cover suppresses the drop in temperature. Therefore, the power transmission capacity estimation device 100 updates the first weather forecast data 211 using multiple models, including models set to either lower temperatures or higher temperatures depending on the amount of cloud cover, depending on the time of day. As a result, the power transmission capacity estimation device 100 can use models with relatively high estimation accuracy, and thus can estimate the power transmission capacity with relatively good accuracy.

[0116] Furthermore, if the estimated transmission capacity is too small compared to the actual transmission capacity, the flexible operation of the transmission lines will be hindered. The transmission capacity estimation device 100 takes into account the temperature prediction error in any of the multiple transmission capacities obtained using multiple second temperature prediction data, so problems are unlikely to occur regardless of which of the multiple transmission capacities is selected. For this reason, the transmission capacity estimation device 100 selects the largest transmission capacity from among the multiple transmission capacities so that the estimated transmission capacity is not too small compared to the actual transmission capacity. This enables the flexible operation of the transmission lines.

[0117] Furthermore, the model used by the power transmission capacity estimation device 100 may have higher estimation accuracy in certain seasons, such as summer, or at times of day when temperatures are high, or in areas closer to the sea than inland. For this reason, the power transmission capacity estimation device 100 may select one power transmission capacity from multiple available capacities depending on at least one of the day, time, and area being estimated. This allows the power transmission capacity estimation device 100 to estimate the power transmission capacity with relatively good accuracy.

[0118] Furthermore, the power transmission capacity estimation device 100 calculates the difference between the maximum predicted temperature value and the maximum actual temperature value during the first period as temperature error data. In other words, since the impact of temperature on the power transmission capacity is greater when the temperature is at its maximum, the power transmission capacity estimation device 100 calculates temperature error data for the case where the temperature is at its maximum. As a result, the power transmission capacity estimation device 100 can estimate the power transmission capacity on the safe side by correcting the second temperature forecast data based on the case where the impact is greatest. Therefore, the power transmission capacity estimation device 100 enables flexible and safe operation of power transmission lines with a simple configuration.

[0119] Furthermore, the power transmission capacity estimation device 100 determines whether the predicted temperature for the first period is smaller than the actual temperature, and extracts the predicted and actual temperature values ​​when the predicted temperature is smaller than the actual temperature to calculate temperature error data. In other words, the power transmission capacity estimation device 100 calculates temperature error data using data from cases where the predicted temperature is smaller than the actual temperature, on the safe side. As a result, the power transmission capacity estimation device 100 enables flexible and safe operation of power transmission lines with a simple configuration.

[0120] Furthermore, if past prediction values ​​deviate too much from future prediction values, using such past prediction values ​​to calculate the error of future prediction values ​​may reduce the accuracy of the calculation. For this reason, the power transmission capacity estimation device 100 extracts values ​​within a predetermined range from the predicted temperature values ​​for the second period to the predicted temperature values ​​for the first period and calculates the temperature error data. As a result, the power transmission capacity estimation device 100 does not use values ​​outside the predetermined range from the predicted temperature values ​​for the second period to the predicted temperature values ​​for the first period in the past when calculating the temperature error data, thereby improving the accuracy of the temperature error data calculation. In addition, it is assumed that the error in the temperature prediction value will differ depending on the predicted temperature value (if the predicted temperature is 25°C, an error of 5°C may occur, but if the predicted temperature is 38°C, an error of 5°C is unlikely). For this reason, the power transmission capacity estimation device 100 extracts values ​​within a predetermined range from the predicted temperature values ​​for the second period and calculates the error of the prediction value. As a result, the power transmission capacity estimation device 100 can improve the accuracy of the temperature error data calculation. As a result, the power transmission capacity estimation device 100 enables flexible and accurate operation of power transmission lines with a simple configuration.

[0121] Furthermore, the power transmission capacity estimation device 100 calculates temperature error data by using the 100th percentile value of the difference between the predicted temperature and the actual temperature during the first period as the temperature prediction error. In other words, if there is variation in the difference between the predicted temperature and the actual temperature, the power transmission capacity estimation device 100, on the safe side, adopts the 100th percentile value of that difference as the temperature prediction error and calculates temperature error data. As a result, the power transmission capacity estimation device 100 enables flexible and safe operation of power transmission lines with a simple configuration.

[0122] Furthermore, when the power transmission capacity estimation device 100 acquires the first weather forecast data 211, if the delivery delay time of the first weather forecast data 211 is long, the accuracy of correcting the second temperature forecast data using the first weather forecast data 211 may decrease. For this reason, the power transmission capacity estimation device 100 acquires the second weather forecast data 221, which has a shorter forecast period but a shorter delivery delay time than the first weather forecast data 211, and uses the second weather forecast data 221 (152) to correct the second temperature forecast data for a period shorter than the second period after a predetermined time. As a result, the power transmission capacity estimation device 100 can improve the accuracy of correcting the second temperature forecast data for a period shorter than the second period after a predetermined time. In addition, the power transmission capacity estimation device 100 can easily acquire the second weather forecast data 221 from, for example, the Japan Meteorological Agency's local FM (LFM). As a result, the power transmission capacity estimation device 100 enables flexible and accurate operation of power transmission lines with a simple configuration.

[0123] Furthermore, when the power transmission capacity estimation device 100 acquires the first weather forecast data 211, if the update interval of the first weather forecast data 211 is long, the accuracy of correcting the second temperature forecast data using the first weather forecast data 211 may decrease. For this reason, the power transmission capacity estimation device 100 acquires second weather forecast data 221, which has a shorter forecast period but a shorter update interval than the first weather forecast data 211, and uses the second weather forecast data 221 (152) to correct the second temperature forecast data for a period shorter than the second period after a predetermined time. As a result, the power transmission capacity estimation device 100 can improve the accuracy of correcting the second temperature forecast data for a period shorter than the second period after a predetermined time. In addition, the power transmission capacity estimation device 100 can easily acquire the second weather forecast data 221 from, for example, the Japan Meteorological Agency's local FM (LFM). As a result, the power transmission capacity estimation device 100 enables flexible and accurate operation of power transmission lines with a simple configuration.

[0124] Furthermore, temperature varies with altitude. For example, temperature decreases at higher altitudes and increases at lower altitudes. Therefore, the power transmission capacity estimation device 100 uses the first altitude data of the prediction point and the second altitude data of the observation point to correct the second temperature prediction data according to the altitude of the power transmission line. As a result, the power transmission capacity estimation device 100 can correct the second temperature prediction data with high accuracy, enabling flexible and accurate operation of power transmission lines with a simple configuration.

[0125] Furthermore, the transmission capacity is also affected by wind speed, solar radiation, or precipitation. For example, high wind speed around a transmission line lowers the temperature of the transmission line, high solar radiation raises the temperature of the transmission line, and high precipitation lowers the temperature of the transmission line, thus affecting the transmission capacity. Therefore, the transmission capacity estimation device 100 acquires first weather forecast data 211 which further includes data showing at least one predicted value for wind speed, solar radiation, and precipitation, and uses this data to estimate the transmission capacity. As a result, the transmission capacity estimation device 100 can estimate the transmission capacity with high accuracy, enabling flexible and accurate operation of transmission lines with a simple configuration.

[0126] [4. Explanation of variations] Although the power transmission capacity estimation device 100 according to this embodiment has been described above, the present invention is not limited to the above embodiment. The embodiments disclosed herein are illustrative and not restrictive in all respects, and the scope of the present invention includes all modifications in the sense and scope equivalent to the claims.

[0127] For example, in the above embodiment, the first period before a predetermined time is the past three or five years from the present time, and the second period after a predetermined time is from one hour ahead to 39 hours ahead. However, the first period can be any period of time and length in the past, and the second period can be any period of time and length in the future.

[0128] Furthermore, in the above embodiment, three methods were given as examples of the multiple methods used by the correction unit 120: Method 1, which does not use a model; Method 2, which uses a first model (neural network model); and Method 3, which uses a second model (WRF model). However, these multiple methods may be four or more methods, including methods 1 to 3 plus other methods, or any two of methods 1 to 3 may be used. These two methods may be two methods using two models, or two methods consisting of Method 1, which does not use a model, and one method using one model. Alternatively, the first model may be a model other than a neural network model, and the second model may be a model other than a WRF model. These multiple methods may be determined as appropriate by the user.

[0129] Furthermore, in the above embodiment, the correction unit 120 calculates the difference between the maximum value of the predicted temperature shown in the first temperature forecast data and the maximum value of the actual temperature shown in the actual temperature data as temperature error data. However, the numerical value used by the correction unit 120 when calculating the temperature error data does not have to be the above maximum value, and any numerical value at any cross-sectional area may be used.

[0130] Furthermore, in the above embodiment, the correction unit 120 extracts the predicted temperature value and the actual temperature value when the predicted temperature value shown in the first temperature forecast data is smaller than the actual temperature value shown in the actual temperature data. However, the correction unit 120 may also extract data for all cases without determining whether the predicted temperature value is smaller than the actual temperature value.

[0131] Furthermore, in the above embodiment, the correction unit 120 extracts values ​​within a predetermined range from the predicted temperature values ​​shown in the second temperature forecast data to the predicted temperature values ​​shown in the first temperature forecast data. However, the correction unit 120 may also extract values ​​outside of the predetermined range.

[0132] Furthermore, in the above embodiment, the correction unit 120 calculates temperature error data by using the value at the 100th percentile of the difference between the predicted temperature value shown in the first temperature forecast data and the actual temperature value shown in the actual temperature data as the temperature forecast error. However, the correction unit 120 may also calculate temperature error data by using the value at the 98th, 95th, 90th, or 80th percentile as the temperature forecast error, instead of the value at the 100th percentile.

[0133] Furthermore, in the above embodiment, the acquisition unit 110 acquires the second weather forecast data, and the correction unit 120 corrects the second temperature forecast data using the second weather forecast data. However, the acquisition unit 110 may not acquire the second weather forecast data, and the correction unit 120 may not perform correction of the second temperature forecast data using the second weather forecast data.

[0134] Furthermore, in the above embodiment, the acquisition unit 110 acquires altitude data, and the correction unit 120 corrects the second temperature forecast data using the altitude data. However, the acquisition unit 110 may not acquire altitude data, and the correction unit 120 may not perform correction of the second temperature forecast data using the altitude data.

[0135] Furthermore, in the above embodiment, the acquisition unit 110 acquires data indicating at least one predicted value of wind speed, solar radiation, and precipitation, and the estimation unit 130 uses this data to estimate the power transmission capacity. However, the acquisition unit 110 may not acquire wind speed, solar radiation, and precipitation, and the estimation unit 130 may not perform the estimation of power transmission capacity using this data.

[0136] Furthermore, the present invention can be realized not only as a power transmission capacity estimation device 100 and a power transmission capacity estimation method, but also as a program for causing a computer to execute the steps included in the power transmission capacity estimation method. In other words, each component of the power transmission capacity estimation device 100 may be realized by a program execution unit such as a CPU or processor reading and executing a software program recorded on a recording medium such as a hard disk or semiconductor memory. Moreover, the present invention can also be realized as a computer-readable non-temporary recording medium on which the program is recorded, such as a flexible disk, hard disk, CD-ROM, MO, DVD, DVD-ROM, DVD-RAM, BD (Blu-ray® Disc), or semiconductor memory. The program can then be distributed via the recording medium and a transmission medium such as the Internet. Furthermore, the present invention can also be realized as an integrated circuit comprising a processing unit included in the power transmission capacity estimation device 100. In other words, each functional block of the power transmission capacity estimation device 100 shown in Figure 2 may be realized as an integrated circuit, which is an LSI (Large Scale Integration). These may be individually integrated into a single chip, or some or all of them may be integrated into a single chip. Thus, the power transmission capacity estimation device 100 may be implemented by having each component comprised of dedicated hardware, or by executing a software program suitable for each component.

[0137] Furthermore, forms constructed by combining any of the components in the above embodiments and their variations are also included within the scope of the present invention. [Industrial applicability]

[0138] This invention can be applied to a power transmission capacity estimation device, etc., which estimates the power transmission capacity, which is the operational capacity of a power transmission line. [Explanation of Symbols]

[0139] 100 Power transmission capacity estimation device 110 Acquisition Department 120 Correction section 130 Estimation part 140 Output section 150 Storage section 151, 211 First Weather Forecast Data 152, 221 Second weather forecast data 153,231 weather observation data 154 Estimation data 155 Model Data 200 Weather Data Management Device 210 Mesomodel data storage unit 220 Local model data storage unit 230 AMEDAS data storage unit 300 Communication Networks

Claims

1. A power transmission capacity estimation device that estimates the power transmission capacity, which is the operational capacity of a power transmission line, An acquisition unit that acquires first weather forecast data including first temperature forecast data and second temperature forecast data showing predicted temperature values ​​in a predetermined area during a first period before a predetermined time and a second period after the predetermined time, and weather observation data including actual temperature data showing actual temperature values ​​in the predetermined area during the first period, A correction unit calculates temperature error data indicating the temperature prediction error in the predetermined area using the first temperature prediction data and the actual temperature data, and corrects the second temperature prediction data using the calculated temperature error data. The system includes an estimation unit that estimates the power transmission capacity in the predetermined area during the second period using the corrected second temperature forecast data, The correction unit calculates a plurality of corrected second temperature forecast data by correcting the second temperature forecast data in a plurality of ways. The estimation unit selects one transmission capacity from a plurality of transmission capacities obtained using the plurality of second temperature forecast data as the transmission capacity to be estimated. A device for estimating the power transmission capacity.

2. The correction unit updates the first weather forecast data using multiple models to calculate multiple updated first weather forecast data, and uses the first temperature forecast data and the second temperature forecast data contained in each of the multiple first weather forecast data to calculate multiple second temperature forecast data. The power transmission capacity estimation device according to claim 1.

3. The correction unit updates the first weather forecast data using multiple models that utilize data from different sized areas of the first temperature forecast data and the second temperature forecast data included in the first weather forecast data. The power transmission capacity estimation device according to claim 2.

4. The correction unit updates the first weather forecast data using multiple models, including a model configured so that the temperature decreases as cloud cover increases, or increases as cloud cover increases, depending on the time of day. The power transmission capacity estimation device according to claim 2 or 3.

5. The estimation unit selects the largest of the multiple power transmission capacities as the estimated power transmission capacity. A power transmission capacity estimation device according to any one of claims 1 to 3.

6. The estimation unit selects one transmission capacity from the plurality of transmission capacities as the estimated transmission capacity, according to at least one of the estimated day, time, and area. A power transmission capacity estimation device according to any one of claims 1 to 3.

7. The correction unit calculates, for each of the plurality of methods, the difference between the maximum value of the predicted temperature shown in the first temperature forecast data and the maximum value of the actual temperature shown in the actual temperature data as the temperature error data. A power transmission capacity estimation device according to any one of claims 1 to 3.

8. The correction unit determines, for each of the plurality of methods, whether the predicted temperature value shown in the first temperature forecast data is smaller than the actual temperature value shown in the actual temperature data, and extracts the predicted temperature value and the actual temperature value when the predicted temperature value is smaller than the actual temperature value to calculate the temperature error data. A power transmission capacity estimation device according to any one of claims 1 to 3.

9. The correction unit calculates the temperature error data by extracting a value within a predetermined range from the predicted temperature value shown in the second temperature forecast data to the predicted temperature value shown in the first temperature forecast data for each of the plurality of methods. A power transmission capacity estimation device according to any one of claims 1 to 3.

10. The correction unit calculates the temperature error data for each of the plurality of methods, using the 100th percentile value of the difference between the predicted temperature value shown in the first temperature forecast data and the actual temperature value shown in the actual temperature data as the temperature forecast error. A power transmission capacity estimation device according to any one of claims 1 to 3.

11. The acquisition unit further acquires second weather forecast data, which has a shorter forecast period and shorter delivery delay time than the first weather forecast data. The correction unit corrects the second temperature forecast data for each of the plurality of methods using the second weather forecast data for a period shorter than the second period after the predetermined time. A power transmission capacity estimation device according to any one of claims 1 to 3.

12. The acquisition unit further acquires second weather forecast data, which has a shorter forecast period and shorter update interval than the first weather forecast data. The correction unit corrects the second temperature forecast data for each of the plurality of methods using the second weather forecast data for a period shorter than the second period after the predetermined time. A power transmission capacity estimation device according to any one of claims 1 to 3.

13. The acquisition unit acquires the first weather forecast data, which further includes first altitude data indicating the altitude of the prediction point, and the weather observation data, which further includes second altitude data indicating the altitude of the observation point. The correction unit further uses the first altitude data and the second altitude data to correct each of the plurality of methods to obtain the second temperature forecast data corresponding to the altitude of the power transmission line in the predetermined area. A power transmission capacity estimation device according to any one of claims 1 to 3.

14. The acquisition unit acquires the first weather forecast data, which further includes data showing at least one predicted value of wind speed, solar radiation, and precipitation in the predetermined area. The estimation unit further uses data showing at least one predicted value of the wind speed, solar radiation, and precipitation included in the first weather forecast data to estimate the power transmission capacity. A power transmission capacity estimation device according to any one of claims 1 to 3.

15. A method for estimating the transmission capacity, which is the operational capacity of a power transmission line, Acquisition step of acquiring first weather forecast data including first temperature forecast data and second temperature forecast data showing predicted temperature values ​​in a predetermined area during a first period before a predetermined time and a second period after the predetermined time, and weather observation data including actual temperature data showing actual temperature values ​​in the predetermined area during the first period, A correction step is to calculate temperature error data indicating the temperature prediction error in the predetermined area using the first temperature prediction data and the actual temperature data, and to correct the second temperature prediction data using the calculated temperature error data. The estimation step includes estimating the power transmission capacity in the predetermined area during the second period using the corrected second temperature forecast data, In the correction step, the second temperature forecast data is corrected in multiple ways to calculate a plurality of corrected second temperature forecast data. In the estimation step, one transmission capacity is selected as the estimated transmission capacity from among multiple transmission capacities obtained using the multiple second temperature forecast data. Method for estimating available power transmission capacity.

16. A program for causing a computer to perform the steps included in the method for estimating the power transmission capacity described in claim 15.

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