Short-Term Load Forecasting Method Based on QPSO-ELM Combined with GA Theory

doi:10.3969/j.issn.1008-0198.2022.01.013

Abstract

Abstract: In order to screen out the nonlinear relationship in massive load data more quickly and improve the accuracy of short-term load forecasting model, this paper focuses on the shortcomings of quantum particle swarm optimization algorithm (QPSO) in dealing with complex high-dimensional parameter optimization problems, and the genetic quantum particle swarm optimization algorithm (GAQPSO) combined with hybrid evolutionary theory of genetic algorithm (GA) is proposed to optimize the input weight and hidden layer deviation of the regular extreme learning machine, which forms a hybrid short-term load forecasting GAQPSO-ELM model. Meanwhile, when the input features are selected, the influences of historical load, temperature, time interval and type of date are fully considered, so the accuracy of the short-term load forecasting model is further improved. The experimental result shows that the proposed short-term load forecasting model has a higher accuracy than the QPSO-ELM model and the common ELM model, and it can better reflect the change trend of the daily load curve, which verifies the effectiveness of the proposed predictive model.

Key words: genetic algorithm, quantum particle swarm optimization algorithm, extreme learning machine, short-term load forecasting

CLC Number:

TM714

CHEN Yonglong, SHI Qi, WANG Erqing. Short-Term Load Forecasting Method Based on QPSO-ELM Combined with GA Theory[J]. Hunan Electric Power, 2022, 42(1): 64-70.

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