Regional power grid load prediction method and device

A load forecasting and regional power grid technology, applied in the electric power field, can solve problems such as affecting the accuracy of load forecasting and losing unstructured meteorological factor data information, so as to improve the accuracy of short-term load forecasting and reduce the feature dimension.

Active Publication Date: 2021-11-12
NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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Problems solved by technology

[0005] In view of the above analysis, the embodiment of the present invention aims to provide a regional power grid load forecasting method and device to solve the problem that the

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  • Regional power grid load prediction method and device
  • Regional power grid load prediction method and device
  • Regional power grid load prediction method and device

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Embodiment Construction

[0071] Preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of the application and together with the embodiments of the present invention are used to explain the principle of the present invention and are not intended to limit the scope of the present invention.

[0072] A specific embodiment of the present invention discloses a load forecasting method for a regional power grid. Such as figure 1 As shown, the regional power grid load forecasting method includes: step S102, determining the meteorological data affecting the load in each meteorological division in the regional power grid, wherein the meteorological data includes air pressure, temperature, precipitation, relative humidity, wind speed, wind direction, date type and cloud image data, obtain cloud image data by shooting with an all-sky imager; Step S104, preprocess the meteorological data; S...

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Abstract

The invention relates to a regional power grid load prediction method and device, which belong to the technical field of electric power, and solve the problem that data information contained in unstructured meteorological factors is lost when modeling is carried out through structured meteorological data, so that the accuracy of load prediction is influenced. The method comprises the steps of determining meteorological data influencing loads in all meteorological partitions in a regional power grid, wherein the meteorological data comprise air pressure, temperature, precipitation, relative humidity, wind speed, wind direction, date types and cloud picture data obtained through shooting of an all-sky imager, preprocessing the meteorological data, establishing a cloud picture classification and discrimination model of a Gabor filter-convolutional neural network, and performing prediction and classification processing on the preprocessed cloud picture data by using the discrimination model, fusing the classified cloud picture data with other meteorological data to form a meteorological data set, establishing a load prediction model, and predicting the load of each meteorological partition by using the load prediction model. And the accuracy and precision of load prediction are improved.

Description

technical field [0001] The invention relates to the field of electric power technology, in particular to a method and device for load forecasting of a regional power grid. Background technique [0002] Power load forecasting is the basis for guiding power grid planning and arranging power generation plans. High-precision load forecasting plays an important role in improving the safe, stable and economical operation of power grids. Short-term power load is easily affected by various numerical and non-numerical factors such as meteorological conditions and holiday types. The change of load presents a certain degree of randomness and nonlinearity, which affects the accuracy of load forecasting. The accuracy of forecasting needs to be further improved. . [0003] At present, the power load forecasting methods are mainly divided into two categories: traditional forecasting methods and intelligent forecasting methods. Traditional forecasting methods mainly include time series, r...

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Application Information

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IPC IPC(8): G06K9/62G06K9/46G06K9/00G06N3/04G06N3/08G06Q10/04G06Q50/06
CPCG06N3/084G06Q10/04G06Q50/06G06N3/045G06F18/213G06F18/24G06F18/253G06F18/214
Inventor 宋晓华汪鹏刘金朋张露潘继璇翟晓颖韩晶晶赵彩萍
Owner NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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