A Permutation Importance-Based Feature Selection Method for Short-Term Electricity Load Forecasting Using Random Forest

Energies 2016, 9(10), 767; doi:10.3390/en9100767

Author

Nantian Huang ^1,* , Guobo Lu ¹

 and Dianguo Xu ²

¹

School of Electrical Engineering, Northeast Dianli University, Jilin 132012, China

²

Department of Electrical Engineering, Harbin Institute of Technology, Harbin 150001, China

^*

Author to whom correspondence should be addressed. 

Academic Editor: Guido Carpinelli

Received: 15 June 2016 / Revised: 4 September 2016 / Accepted: 14 September 2016 / Published: 22 September 2016

(This article belongs to the Special Issue Energy Time Series Forecasting)

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Abstract 

The prediction accuracy of short-term load forecast (STLF) depends on prediction model choice and feature selection result. In this paper, a novel random forest (RF)-based feature selection method for STLF is proposed. First, 243 related features were extracted from historical load data and the time information of prediction points to form the original feature set. Subsequently, the original feature set was used to train an RF as the original model. After the training process, the prediction error of the original model on the test set was recorded and the permutation importance (PI) value of each feature was obtained. Then, an improved sequential backward search method was used to select the optimal forecasting feature subset based on the PI value of each feature. Finally, the optimal forecasting feature subset was used to train a new RF model as the final prediction model. Experiments showed that the prediction accuracy of RF trained by the optimal forecasting feature subset was higher than that of the original model and comparative models based on support vector regression and artificial neural network. View Full-Text

Keywords: short-term load forecast (STLF);  random forest (RF);  feature selection;  permutation importance (PI);  sequential backward search (SBS)

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