Short term electricity price forecasting based on variational mode decomposition and improved particle swarm optimization-least square support vector machine
YANG Zhao, ZHANG Gang, ZHAO Junjie, ZHANG Hao, LIN Yicun
Xi'an Thermal Power Research Institute Co., Ltd, Xi'an 710054
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.
2021, Vol. 22 
