Regional power grid load prediction method and device based on heterogeneous meteorological data fusion

Abstract

The invention relates to a regional power grid load prediction method and device based on heterogeneous meteorological data fusion, which belong to the technical field of electric power, and solve the problems of low power grid load prediction accuracy and low power grid load prediction speed caused by existing meteorological data. The method comprises the following steps of determining meteorological data influencing a load in each meteorological sub-region in a regional power grid, wherein the meteorological data comprises cloud picture data shot by an all-sky imager, preprocessing the meteorological data, establishing a cloud picture classification and discrimination model of a Gabor filter-convolution automatic encoder, and performing prediction and classification processing on the preprocessed cloud picture data by using the discrimination model, fusing the classified cloud picture data and other meteorological data to form a meteorological data set, wherein the other meteorological data comprise air pressure, temperature, precipitation, relative humidity, wind speed, wind direction, date type and cloud picture data, establishing a load prediction model, and predicting the load of each meteorological zone by using the load prediction model. And the accuracy and speed of load prediction are improved.

Description

technical field [0001] The invention relates to the field of electric power technology, in particular to a regional power grid load forecasting method and device based on fusion of heterogeneous meteorological data. 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 forec...

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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 based on the fusion of heterogeneous meteorological data to solve the problem of small samples and unreliability of cloud image data due to the interference of complex factors in the collection environment. Directly using labeling and cloud image data as the input of the neural network will generate huge workload and complexity, which will lead to low accuracy and low speed of power grid load prediction

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