CN-121998232-A - Method, device, equipment and storage medium for predicting power of inter-province medium-long term transaction

CN121998232ACN 121998232 ACN121998232 ACN 121998232ACN-121998232-A

Abstract

The application relates to a method, a device, equipment and a storage medium for predicting the electric quantity of a long-term transaction in provinces. The method comprises the steps of obtaining equipment account data of power transmission equipment and weather data of a historical preset time period of a power generation system, obtaining target sunshine days according to the weather data and a trained sunshine day prediction model, evaluating ageing grades of the power transmission equipment according to the equipment account data, determining power transmission prediction data according to the target sunshine days, preset single-day sunshine power transmission quantity and preset single-day non-sunshine power transmission quantity, matching a target power transmission quantity optimization strategy from a plurality of preset power transmission quantity optimization strategies according to the ageing grades and the preset ageing grade range of the power transmission equipment, determining lost power transmission quantity in the power transmission quantity prediction data according to the target power transmission quantity optimization strategy, determining difference values of the power transmission quantity prediction data and the lost power transmission quantity, and obtaining transaction power prediction data in a future preset time period. The method is beneficial to improving the prediction accuracy of the transaction electric quantity.

Inventors

PAN HUA
BAI YUXUAN
HE HAONAN
CHEN HAOYU
LIU ZHENG
CHEN YUNFENG
SUN HONGYANG
WANG WENBIN
YOU ZEDONG
GUO XIAOYA
ZENG WEI

Assignees

国家能源集团新能源技术研究院有限公司
国家能源投资集团有限责任公司
国能长源能源销售有限公司

Dates

Publication Date: 20260508
Application Date: 20251208

Claims (10)

1. A method for predicting power for long-term transactions in provinces, the method comprising: acquiring equipment standing book data of power transmission equipment and meteorological data of a historical preset time period of a power generation system; predicting the sunshine days in a preset time period in the future according to the meteorological data and the trained sunshine days prediction model to obtain target sunshine days; according to the equipment account data, evaluating the aging grade of the power transmission equipment; Determining power output prediction data in the future preset time period according to the target sunshine days, the preset single-day sunshine power output and the preset single-day non-sunshine power output; According to the aging grade and the preset aging grade range of the power transmission equipment, matching a target power transmission capacity optimization strategy from a plurality of preset power transmission capacity optimization strategies; And determining the lost power transmission amount in the power transmission amount prediction data according to the target power transmission amount optimization strategy, and determining the difference value between the power transmission amount prediction data and the lost power transmission amount to obtain the transaction power transmission amount prediction data in the future preset time period.
2. The method of claim 1, wherein determining a lost power output in the power output prediction data according to the target power output optimization strategy, determining a difference between the power output prediction data and the lost power output, and obtaining transaction power prediction data within the future preset time period, comprises: determining a target maintenance period of the power transmission equipment according to the aging grade of the power transmission equipment and a preset maintenance period; Determining the product of the maintenance period and the preset lost power transmission amount in the unit period to obtain a first lost power transmission amount; and determining a difference value between the power transmission quantity prediction data and the first lost power transmission quantity to obtain first transaction power prediction data, and determining the first transaction power prediction data as transaction power prediction data in the future preset time period.
3. The method according to claim 2, wherein determining the lost power output in the power output prediction data according to the target power output optimization strategy, determining the difference between the power output prediction data and the lost power output, and obtaining the transaction power output prediction data within the future preset time period includes: acquiring the number of maintenance personnel; determining the product of the number of maintenance personnel and a preset unit maintenance speed grade to obtain a first maintenance speed grade of the power transmission equipment; Determining a second lost power transmission amount according to the preset unit maintenance speed grade, the first maintenance speed grade and the preset unit grade; And determining a difference value of the first transaction electric quantity prediction data and the second lost electric quantity to obtain second transaction electric quantity prediction data, and determining the second electric quantity prediction data as the transaction electric quantity prediction data in the future preset time period.
4. A method according to claim 3, wherein, in the case that there is a target power transmission device whose aging level exceeds a preset aging level range in the power transmission device, the determining, according to the target power transmission optimization policy, a lost power transmission in the power transmission prediction data, determining a difference between the power transmission prediction data and the lost power transmission, and obtaining transaction power prediction data within the future preset time period includes: Acquiring the number of the target power transmission devices; determining a difference value between the number of maintenance personnel and the number of the target power transmission equipment, and determining a product of the difference value and the preset unit maintenance speed level to obtain a second maintenance speed level of the power transmission equipment; determining the ratio of the preset unit maintenance speed grade to the second maintenance speed grade, and determining the product of the ratio and the loss power transmission quantity of the preset unit grade to obtain a third loss power transmission quantity; And determining a difference value between the second transaction electric quantity prediction data and the third lost electric quantity to obtain third transaction electric quantity prediction data, and determining the third transaction electric quantity prediction data as the transaction electric quantity prediction data in the future preset time period.
5. The method according to claim 4, wherein, in the case that a target power transmission device whose aging level exceeds a preset aging level range exists in the power transmission device, the determining, according to the target power transmission optimization policy, a lost power transmission in the power transmission prediction data, determining a difference between the power transmission prediction data and the lost power transmission, and obtaining transaction power prediction data within the future preset time period includes: Acquiring the distance between power transmission devices and the maximum distance among the distances; Determining the product of the ratio of the distance to the maximum distance and the preset unit maintenance speed level, and determining the average value of the second maintenance speed level and the product to obtain a third maintenance speed level of the power transmission equipment; Determining the ratio of the preset unit maintenance level to the third maintenance speed level, and determining the product of the ratio and the loss power transmission quantity of the preset unit level to obtain a fourth loss power transmission quantity; and determining a difference value between the third transaction electric quantity prediction data and the fourth lost electric quantity to obtain fourth transaction electric quantity prediction data, and determining the fourth transaction electric quantity prediction data as transaction electric quantity prediction data in the future preset time period.
6. The method according to claim 5, wherein, in the case that a target power transmission device whose aging level exceeds a preset aging level range exists in the power transmission device, determining a lost power transmission amount in the power transmission amount prediction data according to the target power transmission amount optimization policy, determining a difference between the power transmission amount prediction data and the lost power transmission amount, and obtaining transaction power transmission amount prediction data within the future preset time period includes: determining a product of a ratio of the distance to the maximum distance and the preset unit maintenance speed level, and determining a fifth maintenance speed level of the power transmission equipment according to the second maintenance speed level, the product and a preset fourth maintenance speed level; Determining a ratio of the preset unit maintenance level to the fifth maintenance speed level, and determining a product of the ratio and the loss power transmission quantity of the preset unit level to obtain a fifth loss power transmission quantity; And determining a difference value of the product of the fourth transaction electric quantity prediction data and the product to obtain fifth transaction electric quantity prediction data, and determining the fifth transaction electric quantity prediction data as the transaction electric quantity prediction data in the future preset time period.
7. An inter-provincial medium-to-long term transaction power prediction apparatus, the apparatus comprising: The data acquisition module is used for acquiring equipment standing book data of the power transmission equipment and meteorological data of a historical preset time period of the power generation system; the sunshine duration prediction module is used for predicting sunshine duration in a preset time period in the future according to the meteorological data and the trained sunshine duration prediction model to obtain target sunshine duration; the aging grade evaluation module is used for evaluating the aging grade of the power transmission equipment according to the equipment ledger data; The power output prediction module is used for determining power output prediction data in the future preset time period according to the target sunshine days, the preset single-day sunshine power output and the preset single-day non-sunshine power output; The power transmission optimization strategy determining module is used for matching a target power transmission optimization strategy from a plurality of preset power transmission optimization strategies according to the aging grade of the power transmission equipment and the preset aging grade range; and the transaction electric quantity prediction module is used for determining the lost electric quantity in the electric quantity prediction data according to the target electric quantity optimization strategy, determining the difference value between the electric quantity prediction data and the lost electric quantity, and obtaining the transaction electric quantity prediction data in the future preset time period.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.

Description

Method, device, equipment and storage medium for predicting power of inter-province medium-long term transaction Technical Field The present application relates to the technical field of power systems, and in particular, to a method, an apparatus, a computer device, a computer readable storage medium and a computer program product for predicting power of long-term transactions in provinces. Background The trade electricity quantity forecast may be used for ‌ for reasonably arranging the electricity supply and demand, ‌ for improving the energy utilization efficiency, guaranteeing the energy safety and economic sustainable development, ‌ for guiding the operation and maintenance ‌ of the electric power equipment, promoting the market competition and the benefit improvement, ‌ for helping the user to reasonably arrange the electricity utilization plan ‌ and guaranteeing the income of the new energy power station. However, existing trade power predictions are poor in accuracy, and inaccurate predictions can affect power supply and demand predictions. Disclosure of Invention In view of the foregoing, it is desirable to provide a method, an apparatus, a computer device, a computer-readable storage medium, and a computer program product for predicting a transaction amount in a long term in a province, which can improve the accuracy of the transaction amount prediction. In a first aspect, the present application provides a method for predicting the power of a long-term transaction in a province, including: acquiring equipment standing book data of power transmission equipment and meteorological data of a historical preset time period of a power generation system; Predicting the sunshine days in a preset time period in the future according to the meteorological data and the trained sunshine days prediction model to obtain target sunshine days; According to the equipment account data, evaluating the ageing grade of the power transmission equipment; Determining power output prediction data in a future preset time period according to the target sunshine days, the preset single-day sunshine power output and the preset single-day non-sunshine power output; according to the aging grade and the preset aging grade range of the power transmission equipment, matching a target power transmission optimization strategy from a plurality of preset power transmission optimization strategies; And determining the lost power transmission amount in the power transmission amount prediction data according to a target power transmission amount optimization strategy, and determining the difference value between the power transmission amount prediction data and the lost power transmission amount to obtain transaction power transmission amount prediction data in a preset time period in the future. In a second aspect, the present application also provides a device for predicting the power of a long-term transaction in a province, including: The data acquisition module is used for acquiring equipment standing book data of the power transmission equipment and meteorological data of a historical preset time period of the power generation system; the sunshine duration prediction module is used for predicting sunshine duration in a preset time period in the future according to meteorological data and a trained sunshine duration prediction model to obtain target sunshine duration; The aging grade evaluation module is used for evaluating the aging grade of the power transmission equipment according to the equipment account data; The power output prediction module is used for determining power output prediction data in a future preset time period according to the target sunshine days, the preset single-day sunshine power output and the preset single-day non-sunshine power output; the power transmission optimization strategy determining module is used for matching a target power transmission optimization strategy from a plurality of preset power transmission optimization strategies according to the aging grade of the power transmission equipment and the preset aging grade range; The transaction electric quantity prediction module is used for determining the lost electric quantity in the electric quantity prediction data according to a target electric quantity optimization strategy, determining the difference value between the electric quantity prediction data and the lost electric quantity, and obtaining the transaction electric quantity prediction data in a future preset time period. In a third aspect, the present application further provides a computer device, including a memory and a processor, where the memory stores a computer program, and the processor executes the computer program to implement the steps in the embodiment of the method for predicting long-term transaction electric quantity in any province. In a fourth aspect, the present application also provides a computer readable storage medium having stored thereon a computer program which,