• CSCD核心库收录期刊
  • 中文核心期刊
  • 中国科技核心期刊

ELECTRIC POWER CONSTRUCTION ›› 2023, Vol. 44 ›› Issue (3): 49-55.doi: 10.12204/j.issn.1000-7229.2023.03.005

• Research and Application of Key Technologies for Distribution Network Planning and Operation Optimization under New Energy Power Systems?Hosted by Professor WANG Shouxiang and Dr. ZHAO Qianyu? • Previous Articles     Next Articles

Multi-Objective Optimization Based on DWT-MOSMA-SVM for Short-Term Bus Load Forecasting

CHEN Yicong(), ZHANG Dahai(), LI Yuxin(), WANG Ying()   

  1. School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
  • Received:2022-03-11 Online:2023-03-01 Published:2023-03-02
  • Contact: ZHANG Dahai E-mail:20126123@bjtu.edu.cn;dhzhang1@bjtu.edu.cn;21121453@bjtu.edu.cn;wangying1@bjtu.edu.cn
  • Supported by:
    National Natural Science Foundation Youth Fund Program(52107067)


The bus load has small base, high volatility and uncertainty. With the access of renewable energy such as photovoltaic and wind power, the bus load is increasingly disturbed by random factors such as weather, and high precision forecasting of bus load is greatly affected. Aiming at the problem of bus load forecasting in small-sample scenario, this paper proposes a short-term bus load forecasting method based on discrete wavelet transformation-multiple objective slime mould algorithm-support vector machine (DWT-MOSMA-SVM). Firstly, discrete wavelet transform is used to process bus load data. Then giving consideration to the accuracy and stability of the forecasting, multiple-objective slime mold algorithm (MOSMA) is used to optimize the penalty factor and kernel function parameters of SVM. Finally, selecting the Pareto optimal solution on the Pareto front, SVM forecasting model is built for training. The forecasting results are compared with those of LSTM, un-optimized SVM and MOSSA-optimized SVM model. Experimental results show that the proposed MOSMA-SVM model has better forecasting accuracy and stability.

Key words: bus load forecasting, support vector machine (SVM), multiple-objective slime mould algorithm (MOSMA), multi-objective optimization

CLC Number: