Short-Term Electricity Price Forecasting Using Relief-Correlation Analysis Based on Feature Selection and Differential Evolution Support Vector Machine
Peng Chunhua, Liu Gang, Xiang Longyang
East China Jiaotong University Nanchang 330013 China
Abstract：To avoid the unreasonable inputs selection for electricity price forecasting, based on the improvements of traditional Relief algorithm, a novel feature selection method is proposed in this paper. Moreover, correlation analysis is used to eliminate redundant features. Based on the selected features, the price forecasting model is established by using support vector machine(SVM), and differential evolution algorithm is employed to determine the best parameters of SVM. Simulation results based on the real electricity price data obtained from PJM electric power market demonstrate the proposed feature selection method can extract features that reflect the short-run and periodical characters of electricity price, and furthermore, the SVM optimized by differential evolution algorithm can achieve better forecasting results than BP neural network and common SVM.
彭春华, 刘刚, 相龙阳. 基于Relief相关性特征提取和微分进化支持向量机的短期电价预测[J]. 电工技术学报, 2013, 28(1): 277-284.
Peng Chunhua, Liu Gang, Xiang Longyang. Short-Term Electricity Price Forecasting Using Relief-Correlation Analysis Based on Feature Selection and Differential Evolution Support Vector Machine. Transactions of China Electrotechnical Society, 2013, 28(1): 277-284.
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2013, Vol. 28 
