JP4219292B2 - Temperature prediction system and method for power consumption point - Google Patents

Description

  The present invention relates to a temperature prediction system and method for individual power consumption points in a power business.

  In the recent liberalization and deregulation of the electric power business, it becomes even more important how much energy services can be provided to electric power consumers at a low cost and with great benefits for users.

  On the other hand, demand forecast in the electric power business is an important issue in making a supply plan. Electricity demand fluctuates sensitively to changes in temperature, and the temperature sensitivity is 600 to 700 MWh / ° C per day on average among 10 Japanese power companies. Therefore, accurately predicting the temperature on the next day or the day after next and making an accurate supply plan based on the prediction greatly contributes to reduction in supply cost.

  Currently, as a form of utilizing weather information in the power business, a power company that supplies power to a large number of general customers and large customers in a certain area, There are cases where weather forecast data (mainly the highest temperature of the next day) is acquired from multiple weather forecast companies, and the highest temperature and the time of the next day are independently predicted and used to formulate a power supply plan.

Furthermore, as another example of the utilization form of weather information in the electric power business, for example, as disclosed in Patent Document 1 below, wind power, solar power, hydroelectric power generation, and the like have power generation capacity that depends on the weather conditions at the power generation point. Therefore, an apparatus capable of selecting an appropriate power generation form by predicting wind power, illuminance, precipitation, and the like at a power plant and observing actual values has been proposed.
JP 2002-262458 A

  As in the first case above, when formulating a supply plan that depends only on the temperature prediction at one point in the power supply area, the actual temperature at each point in the power supply area and the Even if there is a mist in the air temperature, the error is offset within a wide area, and it is considered that no major problem will occur if power is supplied to a large number of customers in a wide area. However, when power is supplied to a specific large customer, if there is a temperature difference between the temperature prediction point and the actual power consumption point, the temperature difference directly becomes an error in power demand prediction. A promising supply plan is required. In particular, in Japan, weather conditions are likely to change due to the originally complex topography, and a wide range of weather conditions such as the Kinki and Kanto regions cannot be represented at one point. Therefore, when power is supplied to large customers that are unevenly distributed over a wide range, the weather conditions are uneven for each location, and it is important to grasp the weather conditions for each location.

  On the other hand, the numerical forecast results announced by the Japan Meteorological Agency are limited to specific locations on the ground level, and weather forecast elements such as temperature are susceptible to topographical conditions and have large errors. When the distance from the consumption area is far, the error becomes larger, so the predicted temperature at the surface level cannot be used as it is. Specifically, the error of the predicted temperature of the next day by the Japan Meteorological Agency is, for example, about 1.8 to 2.2 ° C. as a mean square error at a representative point in Osaka, and the error further increases in each region. it is conceivable that. In addition, as described above, since the temperature sensitivity of power demand is on average 600 to 700 MWh / ° C., even if the power generation scale is about 1000 MWh, a demand error of 100 to 200 MWh per day is generated. . Accordingly, the electric power company has a reserve supply capacity corresponding to the demand error in advance, which causes a cost increase, and more accurate temperature prediction is particularly important in power supply to large customers.

  The second example uses a weather forecast to appropriately select a plurality of distributed power sources according to weather conditions, and is not directly applicable to solving a demand prediction error problem in power supply to large customers. .

  The present invention has been made in view of the above-mentioned problems, and its purpose is to predict the temperature at a power consumption point accurately and easily for the prediction of individual power demand of a large power consumer or the like. It is to provide a system and method.

  In order to achieve this object, the first characteristic configuration of the temperature prediction system of the power consumption point according to the present invention is a temperature data collection means for collecting temperature data at one or a plurality of power consumption points and a weather forecasting agency. Grid point data collecting means for collecting the grid point data of the numerical forecast results to be distributed, and the grid point data collected by the grid point data collecting means are distributed in a plurality of upper air pressure planes and the power consumption points are horizontally distributed. A grid in which the grid point data collection means collects a statistical regression equation having one or more weather forecast elements at a plurality of grid points included in the range as explanatory variables and future temperature information at the power consumption point as an objective variable. Based on the point data and the temperature data collected by the temperature data collecting means, the regression equation generating means for generating each power consumption point and the grid point data collecting means There condensing the grid point data point to be equipped with a temperature predicting means for predicting the future temperature information by the power points in the power consumption point is input to the statistical regression equation.

  According to the first characteristic configuration of the temperature prediction system of the power consumption point, first, the topography at the power consumption point is calculated based on the grid point data of the numerical forecast result published by a weather forecasting organization such as the Japan Meteorological Agency by the regression equation generation means. Since a statistical regression equation that can predict an accurate temperature reflecting the situation is provided, the temperature prediction means can accurately predict the temperature at each power consumption point based on the grid point data sequentially collected by the grid point data collection means. it can. As a result, it is possible to accurately predict power demand at a power consumption point where there is no temperature prediction information at the surface level published by a weather forecasting organization, and it is possible to reduce supply costs by formulating a more detailed power supply plan.

  In addition to the first feature configuration, the second feature configuration includes a thermometer that measures temperature at the power consumption point, and the temperature data collection unit collects temperature data measured by the thermometer. In the point.

  According to the second characteristic configuration of the temperature prediction system for the power consumption point, data necessary for generating a statistical regression equation can be prepared from the temperature data measured by the thermometer provided at the power consumption point. Accurate statistical regression equation can be generated. As a result, the effect of the first characteristic configuration is specifically exhibited.

  The third feature configuration is that, in addition to the first feature configuration, the temperature data collection means collects temperature data of observation points of the local weather observation system close to the power consumption point.

  According to the third characteristic configuration of the temperature prediction system of the power consumption point, when there is no temperature data at the power consumption point, by using the temperature data of the observation point of the local weather observation system close to the power consumption point, Although the prediction accuracy is reduced, the operation of the temperature prediction system according to the present invention can be started immediately. In order to generate a highly accurate statistical regression equation, it is preferable to use temperature data at a power consumption point over a long period of time (for example, for one year), but then, the operation start of the temperature prediction system according to the present invention is Since the temperature data is collected at the power consumption point, the temperature data at the observation point of the local meteorological observation system is substituted until the collection of the temperature data at the power consumption point is completed as in this feature configuration. This can expedite the start of operation. In addition, when the power consumption point and the observation point of the local meteorological observation system are close to each other, the temperature data of the observation point of the local meteorological observation system may be used as it is. As a regional weather observation system that can be used in this feature configuration, there is AMeDAS of the Japan Meteorological Agency.

  In addition to any one of the above-described feature configurations, the fourth feature configuration is a predicted temperature correction that corrects the temperature information predicted by the temperature prediction unit based on temperature data collected by the temperature data collection unit. It is in the point provided with means.

  According to the fourth characteristic configuration of the temperature prediction system at the power consumption point, it is determined whether there is an error between the temperature predicted in the past by the statistical regression equation and the actually measured temperature collected by the temperature data collecting means thereafter. Thus, a new predicted temperature based on the statistical regression equation can be corrected based on the error information, and a more accurate temperature prediction is possible.

  In order to achieve this object, the characteristic configuration of the temperature prediction method of the power consumption point according to the present invention includes a temperature data collection step for collecting temperature data at one or more power consumption points, and a numerical forecast published by a weather forecasting agency. The grid point data collecting step for collecting the resulting grid point data, and among the grid point data collected in the grid point data collecting step, the power consumption points are within a horizontal distribution range distributed in a plurality of upper atmospheric pressure planes. A statistical regression equation having one or more weather forecast elements at a plurality of grid points including an explanatory variable and future temperature information at the power consumption point as an objective variable, and grid point data collected in the grid point data collection step; Based on the temperature data collected in the temperature data collection step, a regression equation generation step for generating each power consumption point, and the grid point data collection Certain grid point data collected in the process to a point having a temperature prediction step of predicting the future temperature information by the power points in the power consumption point is input to the statistical regression equation.

  According to the characteristic configuration of the temperature prediction method at the power consumption point, first, in the regression formula generation process, the topographical situation at the power consumption point is reflected based on the grid point data of the numerical forecast result published by a weather forecasting organization such as the Japan Meteorological Agency. Since the statistical regression equation capable of predicting the accurate temperature is provided, it is possible to accurately predict the temperature at each power consumption point based on the grid point data sequentially collected in the grid point data collection step in the temperature prediction step. As a result, it is possible to accurately predict power demand at a power consumption point where there is no temperature prediction information at the surface level published by a weather forecasting organization, and it is possible to reduce supply costs by formulating a more detailed power supply plan.

  Embodiments of a temperature prediction system and method for a power consumption point according to the present invention (hereinafter referred to as “the present system” and “the present method”, respectively) will be described with reference to the drawings.

<First Embodiment>
The system 1 of the present invention is a temperature prediction system for a power consumption point that executes a temperature prediction process at one or more power consumption points by computer calculation processing, and is particularly an electric power provider that supplies power to large power consumers. It is a system that provides temperature prediction data used for demand forecasting.

  As shown in FIG. 1, the system 1 of the present invention includes an air temperature data collecting means 2, a grid point data collecting means 3, a regression equation generating means 4, an air temperature predicting means 5, and an output means 6. A storage device 7 for storing the processing results 2 to 5 is provided. Each means 2-6 of the system 1 of the present invention is composed of computer hardware and application software executed on the hardware.

  As shown in FIG. 1, power consumption points A to C that are targets of temperature prediction of the system 1 of the present invention include a thermometer 8 for measuring temperature data at each point, and temperature data collecting means 2. A data communication device 9 for transmitting temperature data measured by the thermometer 8 is installed separately, and the measured temperature data is sequentially transferred to the temperature data collecting means 2 via a predetermined data communication path 10. Has been. For example, using a public telephone line, a PHS line or the like as the data communication path 10, a dedicated communication path is established by forming a communication path (dial-up etc.) from one of the temperature data collecting means 2 and the data communication device 9 to the other. Thus, it is possible to collect measured temperature data. Alternatively, the temperature data collecting means 2 and the data communication device 9 are configured to be connectable to a computer network such as the Internet, and the measured temperature data is transmitted from the data communication device 9 to the temperature data collecting means 2 using a protocol for computer communication. You may make it perform.

  Next, regarding the functions of the means 2 to 6 of the system 1 of the present invention and their processing operations, refer to the system configuration diagram of FIG. 1 and the flowchart of FIG. To explain.

  First, a regression expression generation process according to the method of the present invention by the temperature data collection means 2, the grid point data collection means 3, and the regression expression generation means 4 will be described. The flow chart of FIG. 2A shows the procedure of the regression equation generation process.

  The temperature data collecting means 2 includes a communication interface adapted to the communication method of the data communication path 10 so as to be able to send and receive data to and from the data communication device 9 installed at each power consumption point A to C via the data communication path 10. It is configured. The temperature data collecting means 2 periodically reads the temperature data D1j at each point, which is measured every fixed time (for example, 1 hour or 30 minutes) every day by the thermometer 8 installed at each power consumption point A to C. The temperature data D1j is received from the data communication device 9 through the data communication path 10 and stored in the storage device 7 together with the measurement date and time for each power consumption point (step # 10). Here, the subscript j represents the power consumption points A to C. When the thermometer 8 has a function of individually measuring the daily average temperature Tave, the daily maximum temperature Tmax, and the daily minimum temperature Tmin, the temperature data D1j is the daily average of each power consumption point A to C. The temperature Tave, the daily maximum temperature Tmax, and the daily minimum temperature Tmin may be used.

  The grid point data collection means 3 accesses the grid point data server 11 that provides the grid point data D2 of the numerical forecast results published by a weather forecasting organization such as the Japan Meteorological Agency via the computer network 12 such as the Internet, and transmits and receives data. It is possible to have a predetermined communication interface. The grid point data collecting means 3 receives the grid point data D2 of the next day and the day after next published by the weather forecasting agency from the grid point data server 11 via the computer network 12 periodically and stores them in the storage device 7 ( Step # 11).

  The grid point data D2 is, for example, a plurality of atmospheric pressure surfaces (for example, 300 hPa, 500 hPa, 700 hPa) on the upper layer (not the sea surface and the ground surface) of the numerical prediction region model (RSM: Regional Spectra1 Model) published twice daily Grid point data in the three layers). The numerical prediction of this area model includes each data of weather forecast elements (temperature T, wind speed W, humidity Q) every 3 hours up to 51 hours ahead. The horizontal grid interval of the grid point data is 0.4 × 0.5 degrees in the longitude coordinates of equal latitude.

  In the present embodiment, as shown in FIG. 3, the grid points (x1, x2, x3,..., Xn) of the grid point data D2j stored for each power consumption point are horizontal on the above-described air pressure surfaces. The distribution range is assumed to be four points in the vicinity of the vertical line from each power consumption point A to C on the ground surface and several points around it. For example, if all the neighboring grid points adjacent to the north and south and east and west are used for the four neighboring points, the total number n of grid points at three atmospheric pressure surfaces is 36. Accordingly, the grid point data D2j to be stored is the temperature Tij (i = 1 to n), the wind speed Wij (i = 1 to n), and the humidity Qij (i = 1 to n) for each power consumption point. Here, the subscript j represents the power consumption points A to C.

  The air temperature data collecting means 2 and the grid point data collecting means 3 periodically repeat the air temperature data collecting process of Step # 10 and the grid point data collecting process of Step # 11 each day, for example, temperature data for 365 days. D1j and grid point data D2j are collected. The regression equation generation means 4 determines the completion of data collection of the temperature data D1j and the grid point data D2j (step # 12), and when the data collection is completed, the meteorological data of each grid point xi is obtained for each power consumption point A to C. The forecast elements (temperature Tij, wind speed Wij, humidity Qij) are explanatory variables, and the daily average temperature ETjave, daily maximum temperature ETjmax, and daily minimum temperature ETjmin of the next day and the next day at each power consumption point A to C are the target variables. A street multiple regression equation (statistical regression equation) is generated (step # 13). Each multiple regression equation is simply shown in Equation 1.

(Equation 1)
ETjave = F1j (T0j, ..., Tmj, W0j, ..., Wmj,
Q0j, ..., Qmj)
ETjmax = F2j (T0j, ..., Tmj, W0j, ..., Wmj,
Q0j, ..., Qmj)
ETjmin = F3j (T0j, ..., Tmj, W0j, ..., Wmj,
Q0j, ..., Qmj)

  In Equation 1, as the explanatory variable, each temperature information of the objective variable and lattice point data D2j every 3 hours on the same day are used. Therefore, m on the right side of each equation in Equation 1 is 8 times the total number of grid points n. The objective variable used for the generation of each multiple regression equation of Equation 1 (derivation of the coefficient and constant on the right side of each equation of Equation 1) is based on the temperature data D1j collected by the temperature data collecting device 2 and the regression equation generating device 4 is 365. The daily average temperature Tave, the daily maximum temperature Tmax, and the daily minimum temperature Tmin are derived and used for each day of the day. Alternatively, when the temperature data collection unit 2 collects the daily average temperature Tave, the daily maximum temperature Tmax, and the daily minimum temperature Tmin for each day for 365 days as the temperature data D1j, the temperature data D1j is used as it is. . Note that the derivation of the coefficients and constants of the multiple regression equations F1j to F3j shown in Equation 1 can be performed using a known calculation method, and thus detailed description thereof is omitted.

  The air temperature data collecting means 2 and the grid point data collecting means 3 continue to perform the air temperature data collecting process of step # 10 and the step # 11 after the generation of the multiple regression equation is completed in the regression expression generating process of step # 13. The grid data collection process is repeated daily. Thereby, when the collection of data for a new 365 days is completed, the update processing (step # 13) of the multiple regression equations F1j to F3j is executed in the same manner as the regression equation generation step. The regression equation update process is performed, for example, every year.

  Next, the temperature prediction process by the method of the present invention by the grid point data collection means 3, the temperature prediction means 5, and the output means 6 will be described. The flowchart of FIG. 2B shows the procedure of the temperature prediction process. With respect to the power consumption point where the generation of the multiple regression equations F1j to F3j is completed in the regression equation generation processing, the temperature prediction processing can be performed from that point.

  First, the grid point data collecting means 3 receives the grid point data D2 published by the weather forecasting agency on the next day and the day after next periodically from the grid point data server 11 via the computer network 12, and stores it in the storage device 7. (Step # 20). Since the grid point data collection process in step # 20 is the same as the grid point data collection process (step # 11) after the regression formula is generated in the regression formula generation process, both processes can be executed in common. Since the grid point data D2j to be stored has already been described in the grid point data collection step of the regression equation generation process, a duplicate description is omitted.

  The temperature predicting means 5 substitutes the lattice point data D2j newly collected by the lattice point data collecting means 3 as explanatory variables into the already derived multiple regression equations F1j to F3j shown in Equation 1, and the next day which is the objective variable The daily average temperature ETjave, the daily maximum temperature ETjmax, and the daily minimum temperature ETjmin are derived (step # 21). Here, the next day grid point data D2j is substituted as an explanatory variable for each temperature information of the next day, and the next day grid point data D2j is substituted as an explanatory variable for each temperature information of the next day.

  Subsequently, the output means 6 outputs the daily average temperature ETjave, the daily maximum temperature ETjmax, the daily maximum temperature ETjmax, and the daily minimum temperature ETjmin derived in the temperature prediction process of step # 21 to a predetermined output destination in a predetermined format. (Step # 22). For example, if the output destination is a display terminal (not shown) of the system 1 of the present invention, each predicted temperature information is displayed on the display screen. When the calculation result is used in another application, the calculation result is transferred to a computer that processes the application in a predetermined data format.

  As described above, the temperature prediction processing in steps # 20 to # 22 is performed regularly every day (for example, twice a day) every time the grid point data D2 is regularly received.

Second Embodiment
Next, a second embodiment of the system 1 of the present invention will be described. In the second embodiment, the system 1 of the present invention is basically the same as the configuration of the first embodiment as shown in FIG. 4, but the temperature data collecting means 2 is the function shown in the first embodiment. In addition, AMeDAS temperature data of AMeDAS observation points (an example of observation points of the regional meteorological observation system) close to the power consumption points A to C can be collected.

  More specifically, the temperature data collection means 2 can access and send data to the AMeDAS data server 13 that provides AMeDAS data published by the weather forecasting agency (JMA) to the outside via the computer network 12 such as the Internet. A predetermined communication interface is provided.

  In the first embodiment, in order for the inventive system 1 to execute the temperature prediction process by the inventive system 1 at a certain power consumption point, for example, temperature data D1j and grid point data D2j for 365 days at the power consumption point. It is necessary to complete the collection of multiple regression equations. That is, temperature data D1j and grid point data D2j from one year before the start of power supply to a certain power consumption point are required. The lattice point data D2j may be acquired collectively because there is past data, but the temperature data D1j at the power consumption point does not exist unless it is acquired in advance. Therefore, in the second embodiment, when the past temperature data D1j does not exist, in order to immediately execute the temperature prediction process by the system 1 of the present invention, the temperature data D3j that replaces the temperature data D1j at the power consumption point is changed. use.

  In the second embodiment, a regression equation generation process is executed using the AMeDAS temperature data of the AMeDAS observation point close to the power consumption point as the alternative temperature data D3j. As shown in FIG. 5, the temperature data collecting means 2 accesses the AMeDAS data server 13 and the AMeDAS temperature data of the AMeDAS observation point for the past 365 days (daily average temperature Tave, daily maximum temperature Tmax, daily minimum temperature). Tmin) is acquired, and the temperature data D3j is stored together with the observation date in the storage device 7 (step # 30).

  Next, the grid point data collecting means 3 receives the data published on the past 365 days of the grid point data D2 published by the weather forecasting agency on the next day and the day after next, from the grid point data server 11 via the computer network 12, and stores them. All the data is stored in the device 7 (step # 31). Note that the lattice point data D2j to be stored is the same as that in the first embodiment, and thus a duplicate description is omitted.

  In the above steps # 30 and # 31, the collection of the temperature data D3j and the grid point data D2j for 365 days has been completed. Therefore, in the same manner as in the first embodiment, the multiple regression equations F1j to F3j shown in Equation 1 are Coefficients and constants are derived, and multiple regression equations F1j to F3j are generated (step # 32). Since the daily average temperature Tave, the daily maximum temperature Tmax, and the daily minimum temperature Tmin have already been acquired for the temperature data D3j corresponding to the objective variable, the coefficients and constants are derived as they are.

  By the above regression equation generation processing (steps # 30 to # 32), when the collective collection of the temperature data D3j and the grid point data D2j is completed, multiple regression equations can be generated. The temperature prediction process can be executed. The temperature prediction process may be executed in the same manner as in the first embodiment.

  Here, the AMeDAS observation point used as an alternative to the power consumption point is selected in principle as the AMeDAS observation point closest to the power consumption point. If there is a hail, the average value (or weighted average value) including other nearby AMeDAS observation points may be used as the alternative temperature data D3j.

  When the above regression equation generation processing (steps # 30 to # 32) is completed, as shown in FIG. 5, the processing after the regression equation generation step (step # 13) of the first embodiment (step of FIG. 2A). # 10 and later). When the collection of temperature data D1j and grid point data D2j for 365 days is completed, update processing of each of the multiple regression equations F1j to F3j (step # 13) is performed.

<Third Embodiment>
Next, a third embodiment of the system 1 of the present invention will be described. In the third embodiment, as shown in FIG. 6, the system 1 of the present invention includes a predicted temperature correction unit 14 in addition to the configuration of the first embodiment. The predicted temperature correction means 14 is also composed of the same computer hardware as the above means 2 to 6 of the system 1 of the present invention and application software executed on the hardware.

  Next, temperature prediction processing in the third embodiment by the temperature data collection unit 2, the grid point data collection unit 3, the temperature prediction unit 5, the predicted temperature correction unit 14, and the output unit 6 will be described. The flowchart of FIG. 7 shows the procedure of the temperature prediction process. In addition, what is necessary is just to perform a regression type production | generation process in the same way as 1st Embodiment or 2nd Embodiment.

  First, the temperature data collecting means 2 receives the temperature data D1j of each power consumption point A to C from the data communication device 9 via the data communication path 10 periodically and stores it in the storage device 7 (step # 40). ). Since the temperature data collection process of step # 40 is the same as the temperature data collection process (step # 10) after the generation of the regression equation in the regression equation generation process, both steps can be executed in common.

  Next, the grid point data collecting means 3 receives the grid point data D2 published by the weather forecasting agency on the next day and the day after next periodically from the grid point data server 11 via the computer network 12, and stores it in the storage device 7. Save (step # 41). Since the grid point data collection process in step # 41 is the same as the grid point data collection process (step # 11) after generating the regression formula in the regression formula generation process, both processes can be executed in common.

  The temperature data D1j and grid point data D2j stored in steps # 40 and # 41 have already been described in the temperature data collection step and grid point data collection step of the regression equation generation process of the first embodiment, so overlapping explanations will be given. Omit.

  The temperature predicting means 5 substitutes the lattice point data D2j newly collected by the lattice point data collecting means 3 as explanatory variables into the already derived multiple regression equations F1j to F3j shown in Equation 1, and the next day which is the objective variable The daily average temperature ETjave, the daily maximum temperature ETjmax, and the daily minimum temperature ETjmin are derived (step # 42). Here, the next day grid point data D2j is substituted as an explanatory variable for each temperature information of the next day, and the next day grid point data D2j is substituted as an explanatory variable for each temperature information of the next day.

  Next, the predicted temperature correction means 14 calculates the daily average temperature Tave, the daily maximum temperature Tmax, and the daily minimum temperature Tmin that are derived from the temperature data D1j of each power consumption point A to C newly collected by the temperature data collection means 2. The daily average temperature ETjave, daily maximum temperature ETjmax, daily maximum temperature ETjmax and daily minimum temperature ETjmin derived in the temperature prediction process (step # 42) one or two times before are compared (step # 43), and the error is predetermined. If it is equal to or higher than a threshold value (for example, 1.5 ° C.), it is determined that the predicted temperature information derived in the current temperature prediction step (step # 42) is corrected, and the daily average temperature ETjave, daily maximum temperature ETjmax, day Each error is corrected for the minimum temperature ETjmin (step # 44). If the error is less than the predetermined threshold value, the process proceeds to step # 45 without executing the predicted temperature correction process in step # 44.

  Subsequently, at step # 45, the output means 6 determines the daily average temperature ETjave, daily maximum temperature ETjmax, daily maximum temperature ETjmin, daily minimum temperature ETjmin derived from the temperature prediction process at step # 42 or predicted temperature at step # 44. The daily average temperature ETjave, the daily maximum temperature ETjmax, and the daily minimum temperature ETjmin corrected in the correcting step are output to a predetermined output destination in a predetermined format (step # 45). For example, if the output destination is a display terminal (not shown) of the system 1 of the present invention, each predicted temperature information is displayed on the display screen. When the calculation result is used in another application, the calculation result is transferred to a computer that processes the application in a predetermined data format.

  Hereinafter, another embodiment will be described.

  <1> In each of the above embodiments, the objective variables of the multiple regression equations F1j to F3j that are generated in the regression equation generation step, that is, used in the temperature prediction step, are the next day and the day after next at each power consumption point A to C. Although the daily average temperature ETjave, the daily maximum temperature ETjmax, and the daily minimum temperature ETjmin are used, instead of this, as shown in Equation 2, every 3 hours or 1 hour ahead of 3 hours to 51 hours at each power consumption point A to C It may be the temperature ETj at each time for each hour.

(Equation 2)
ETj = F4j (T0j, ..., Tmj, W0j, ..., Wmj,
Q0j, ..., Qmj)

  In Equation 2, when the objective variable ETj is a temperature every three hours, the explanatory variable uses lattice point data D2j at the same time as the objective variable ETj. Therefore, m on the right side of Equation 2 is the same as the total number of grid points n. When the objective variable ETj is an hourly temperature, the explanatory variable uses lattice point data D2j at 2 or 3 times near the time of the objective variable ETj. Therefore, m on the right side of Equation 2 is twice or three times the total number of grid points n.

  As in this embodiment, when the objective variable is the temperature ETj at 3 hours to 51 hours ahead at each power consumption point A to C or every hour for each hour, the temperature prediction means 5 Based on the predicted temperature ETj at each time obtained by substituting the grid point data D2j every 3 hours from 3 hours to 51 hours into the multiple regression equation F4j, the daily average temperature Tave and the daily maximum temperature Tmax of the next day and the next day The daily minimum temperature Tmin may be derived.

  <2> In the second embodiment, the temperature data collecting means 2 accesses the AMeDAS data server 13 and collects AMeDAS temperature data of AMeDAS observation points close to the power consumption point as alternative temperature data D3j, and grid point data. Although the case where the collecting means 3 collects the lattice point data D2 from the lattice point data server 11 via the computer network 12 has been described, it is manually or semi-automatically acquired by an operator in each of the collective data collection processes. The AMeDAS temperature data and grid point data D2 may be input to the temperature data collection unit 2 and the grid point data collection unit 3, respectively.

  <3> In the regression equation generation process of each of the above embodiments, the order of the temperature data collection step and the grid point data collection step, or the temperature data batch collection step and the grid point data batch collection step may be mixed within the same day. Absent.

  <4> In the grid point data collection process of each of the embodiments described above, the grid point number of grid point data D2j to be stored is set to 4 points in the vicinity of the vertical line from each power consumption point A to C and the surroundings on each atmospheric pressure surface. Although limited to a few points, the horizontal distribution range of grid points of the grid point data D2j to be stored is not limited to the current power consumption points A to C, but is set so as to include the planned power supply range, and the horizontal distribution range thereof The lattice point data D2 of all of the lattice points may be once saved together. In the regression equation generation step, only the lattice point data D2j in the lattice point distribution range necessary for generating the multiple regression equation is used for each power consumption point A to C from the stored lattice point data D2. It doesn't matter. Similarly, in the temperature prediction process, only the grid point data D2j in the grid point distribution range necessary for substitution into the multiple regression equation is used for each power consumption point A to C from the stored grid point data D2. To do.

  <5> In each of the above embodiments, the case where the predicted temperature information can be the next day and the next day is described. In this way, by including the predicted temperature information not only for the next day but also for the next day, it is possible to make an optimal demand prediction according to the lead time of each power generation method. However, in the present invention, the predicted temperature information is not necessarily limited to the prediction of the next day and the next day.

  <6> In each of the above embodiments, for convenience of explanation, the power consumption points are illustrated with three points A to C. However, in the present invention, the power consumption points are not limited to three points.

The block block diagram which shows the structural example in 1st Embodiment of the temperature prediction system which concerns on this invention. The flowchart which shows each procedure of the regression type production | generation process (A) and temperature prediction process (B) in 1st Embodiment of the temperature prediction method which concerns on this invention. Explanatory drawing which shows the relationship between the grid point of the grid point data used with the temperature prediction system which concerns on this invention, and a power consumption point The block block diagram which shows the structural example in 2nd Embodiment of the temperature prediction system which concerns on this invention. The flowchart which shows each procedure of the regression type production | generation process in 2nd Embodiment of the temperature prediction method which concerns on this invention. The block block diagram which shows the structural example in 3rd Embodiment of the temperature prediction system which concerns on this invention. The flowchart which shows each procedure of the temperature prediction process in 3rd Embodiment of the temperature prediction method which concerns on this invention.

Explanation of symbols

1: Temperature prediction system for power consumption point according to the present invention 2: Temperature data collection means 3: Grid point data collection means 4: Regression expression generation means 5: Temperature prediction means 6: Output means 7: Storage device 8: Thermometer 9 : Data communication device 10: Data communication path 11: Lattice point data server 12: Computer network 13: AMeDAS data server 14: Predicted temperature correction means A, B, C: Electricity consumption points D1j, D1A to D1C: Temperature data D2: Lattice Point data D3j: AMeDAS temperature data (alternative temperature data)
x1, x2, x3, ...: Grid points of grid point data

Claims (5)

Temperature data collecting means for collecting temperature data at one or more power consumption points;
Grid point data collection means for collecting grid point data of numerical forecast results published by the weather forecasting agency;
Explain one or more weather forecast elements at a plurality of grid points distributed in a plurality of upper air pressure planes of the grid point data collected by the grid point data collection means and including the power consumption points in a horizontal distribution range. And a statistical regression equation with the future temperature information at the power consumption point as a target variable, based on the grid point data collected by the grid point data collection means and the temperature data collected by the temperature data collection means, Regression expression generation means to generate for each power consumption point,
Temperature prediction means for inputting the grid point data collected by the grid point data collection means into the statistical regression equation and predicting the future temperature information at the power consumption points for each power consumption point;
A temperature prediction system for a power consumption point, characterized by comprising: A thermometer for measuring the temperature at the power consumption point,
The temperature prediction system for a power consumption point according to claim 1, wherein the temperature data collection means collects temperature data measured by the thermometer.   2. The temperature prediction system for a power consumption point according to claim 1, wherein the temperature data collection unit collects temperature data of an observation point of a local weather observation system close to the power consumption point.   The predicted temperature correction means for correcting the temperature information predicted by the temperature prediction means based on the temperature data collected by the temperature data collection means is provided. Temperature prediction system for electricity consumption points in Japan. An air temperature data collecting step for collecting air temperature data at one or more power consumption points;
Grid point data collection process for collecting grid point data of numerical forecast results published by the weather forecasting agency;
Explains one or more weather forecast elements at a plurality of grid points distributed in a plurality of atmospheric pressure planes in the grid layer data collected in the grid point data collection step and including the power consumption point within a horizontal distribution range. And a statistical regression equation with the future temperature information at the power consumption point as a target variable, based on the grid point data collected in the grid point data collection step and the temperature data collected in the temperature data collection step, Regression formula generation process for each power consumption point,
A temperature prediction step of inputting the grid point data collected in the grid point data collection step into the statistical regression equation and predicting the future temperature information at the power consumption point for each power consumption point;
A method for predicting the temperature of a power consumption point, characterized by comprising:

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