PSO-LSSVM short-term load prediction method based on improved variational mode decomposition

Abstract

The invention belongs to the field of power systems, and relates to a PSO-based on improved variational mode decomposition. The LSSVM short-term load prediction method comprises the following steps: S1, selecting a decomposition effect evaluation index; S2, setting an SMD decomposition upper limit; S3, optimizing the VMD parameters by using a particle swarm optimization algorithm, performing VMD decomposition, and finally obtaining a period corresponding to the center frequency of the modal component; S4, combining the modal components to obtain a combined component; S5, solving mutual information between the sequences of the influence factor data and the combination components and the predicted daily load sequence, and obtaining an influence factor input variable set according to a threshold requirement; S6, substituting the selected influence factor input variable into the PSO- LSSVM model. According to the method, the utilization efficiency of influence factor data is improved, andan optimized mode decomposition result is obtained; By quantifying the correlation between the internal structure components of the influence factors and the loads, effective influence factor variables are accurately selected, the number of the influence factors is increased, and the prediction precision is improved.

Description

technical field [0001] The invention belongs to the field of power systems, in particular to a PSO-LSSVM short-term load forecasting method based on improved variational mode decomposition. Background technique [0002] With the steady advancement of power system reform, my country's power market mechanism is gradually improving, and the power system will gradually change the operation mode of planned production in the past, and will operate in a more independent and open market environment. The short-term load forecasting of the power system is based on the historical load change law, combined with meteorological, economic and other factors to scientifically predict the load in the next few days or hours. Accurate short-term load forecasting is of great significance to improve the utilization rate of power generation equipment and the effectiveness of economic dispatch in the market environment. [0003] At present, the methods used for power system load forecasting mainly...

AI Technical Summary

Problems solved by technology

In the current research, the data series of influencing factors are often directly added to the prediction model for research, and the influence of the internal structure of these influencing factors on the load is not studied in depth, and there are problems such as low data utilization efficiency and too subjective selection of influencing factors.

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