Short-term impact load prediction method based on two-layer decomposition technology

Abstract

The invention discloses a short-term impact load prediction method based on a two-layer decomposition technology. The short-term impact load prediction method comprises the following steps of obtaining impact load historical data and performing equalization preprocessing on the data; decomposing the preprocessed impact load historical data into a plurality of discrete modal components through a variable mode, and recording the discrete modal components as IMFn, where n is a serial number of the discrete modal components; performing secondary decomposition on the component with the highest frequency in the discrete modal components by singular spectrum analysis to obtain a plurality of sub-sequences; constructing an extreme learning machine neural network prediction model based on whale algorithm optimization; inputting the components except the component with the highest frequency in the discrete modal components and a sub-sequence obtained by secondary decomposition into an extreme learning machine neural network prediction model based on whale algorithm optimization; and superposing prediction values output by the extreme learning machine neural network prediction model based onwhale algorithm optimization to obtain an actual prediction result. According to the method, the influence of nonlinear characteristics in the impact load is overcome, and the prediction precision iseffectively improved.

Description

technical field [0001] The invention relates to the technical field of electrical engineering, and more specifically, to a short-term impact load prediction method based on two-layer decomposition technology. Background technique [0002] Power system load forecasting is the key basis for power plants to coordinate generation of units, and it is also the main source for the power market to adjust real-time electricity prices. The accuracy of its prediction will directly affect the power generation cost of the power plant, the power grid dispatching and the quality of electricity consumption of the local residents. With the growth of urban electricity consumption, the increase of electricity users has caused the complexity of regional load types, and a single load forecasting method (such as fuzzy logic method, time series method, support vector machine, artificial neural network, etc.) is prone to fall into local Optimal, slow convergence speed, it is difficult to meet the ...

AI Technical Summary

Problems solved by technology

[0003] The present invention provides a short-term impact load prediction method based on two-layer decomposition technology to overcome the defect that the non-linear characteristics of the impact load cannot be dealt with inaccurately when predicting the impact load in the prior art

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