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Research on Short-Term Load Forecasting of Micro-Grid Based on PSO-SVM Model

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DOI: 10.23977/jeis.2020.51004 | Downloads: 9 | Views: 840

Author(s)

Shaomin Zhang 1, Xuebao Li 1, Baoyi Wang 1

Affiliation(s)

1 Department of Control and Computer, North China Electric Power University, HuaDian Road, BaoDing, China

Corresponding Author

Shaomin Zhang

ABSTRACT

Due to the uncertainty and volatility of micro-grid load, conventional load forecasting methods cannot be directly used in micro-grid load forecasting. Therefore a hybrid load forecasting model of micro-grid based on particle swarm optimization (PSO) and Support Vector Machine (SVM) is proposed in this paper. Particle swarm optimization (PSO) was used to optimize the model parameters of SVM regression, and the optimized SVM prediction model was obtained. Through the comparative analysis of the experiment, it is concluded that the hybrid prediction model of PSO-SVM is more accurate for the load prediction of micro grid, which can provide a decision basis for the safe and economic dispatch of micro grid and play a positive role in the stable operation of micro grid power system.

KEYWORDS

Micro-grid, Support Vector Machine, Short-term Load Forecasting

CITE THIS PAPER

Shaomin Zhang, Xuebao Li, Baoyi Wang. Research on Short-Term Load Forecasting of Micro-Grid Based on PSO-SVM Model. Journal of Electronics and Information Science (2020) 5: 17-22. DOI: http://dx.doi.org/10.23977/jeis.2020.51004.

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