Short-term impact load forecasting model based on signal decomposition and intelligent optimization algorithm

Abstract

The invention discloses a short-term impulse load forecasting model building method based on an signal decomposition and intelligent optimization algorithm, comprising the following steps: S1, according to the non-stationarity of load data, adopting complementary set empirical mode decomposition (CEEMD) to decompose the time series of the original load into several intrinsic mode functions (IMFs);complementary empirical mode decomposition (CEEMD) adding positive and negative white noise to the original time series, which not only guarantees the same decomposition effect as empirical mode decomposition (EEMD), but also reduces the reconstruction error caused by adding white noise. The invention adopts the decomposition technology to decompose the sequence into a plurality of modal components, optimizes the parameters of the prediction model combined with the optimization algorithm, and finally superimposes the prediction results of each component as the final prediction value. Comparedwith other models, the combined model can obtain higher prediction accuracy in the short-term impact load prediction.

Description

technical field [0001] The invention relates to the technical field of power system load forecasting, in particular to a method for establishing a short-term impact load forecasting model based on signal decomposition and an intelligent optimization algorithm. Background technique [0002] Power system load forecasting is the key basis for power plants to coordinate power generation, and it is also the main source for adjusting real-time electricity prices in the power market. The accuracy of its forecasting will directly affect the power generation costs of power plants, power grid dispatching, and the quality of electricity used by local residents. With the growth of electricity consumption in cities, 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 converg...

AI Technical Summary

Problems solved by technology

At present, there are very few studies on the application of complementary ensemble empirical mode decomposition (CEEMD) method in load forecasting. Related literatures have proposed a short-term load forecasting model based on complementary ensemble empirical mode decomposition (CEEMD), which has achieved relatively good results in simulation experiments. Good results, but the parameters of the model have not been optimized using intelligent algorithms, and the generalization ability of the model has yet to be proven when forecasting in areas with a large number of impact loads

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