基于变分模态分解和改进粒子群算法优化最小二乘支持向量机的短期电价预测

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Abstract According to the characteristics of strong non-linearity and high volatility of electricity price series, a short-term electricity price prediction model based on variational mode decomposition (VMD) and least square support vector machine (LSSVM) optimized by improved particle swarm optimization (PSO) is proposed. First of all, VMD is used to decompose the original electricity price sequence into multiple component sequences, and LSSVM modeling and prediction is performed on each component sequence. And then, in order to improve the prediction accuracy, aiming at the problem of selecting the optimal parameters of the LSSVM prediction model, an improved particle swarm algorithm is proposed to optimize the parameters of the LSSVM model. Finally, the prediction results of each component are integrated to obtain the final electricity price prediction value. In order to verify the effectiveness of the proposed model, the US PJM market electricity price data is used as an example to analyze and compare with other forecasting models. It shows that the proposed model can predict short-term electricity prices well.

Key words： electricity price forecasting variational mode decomposition (VMD) particle swarm optimization (PSO) least square support vector machine (LSSVM)

Received: 10 March 2021

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	Articles by authors
	YANG Zhao
	ZHANG Gang
	ZHAO Junjie
	ZHANG Hao
	LIN Yicun

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YANG Zhao,ZHANG Gang,ZHAO Junjie等. Short term electricity price forecasting based on variational mode decomposition and improved particle swarm optimization-least square support vector machine[J]. Electrical Engineering, 2021, 22(10): 11-16.

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http://dqjs.cesmedia.cn/EN/Y2021/V22/I10/11

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