Modular Predictor for Day-Ahead Load Forecasting and Feature Selection for Different Hours

Lin Lin, Lin Xue, Zhiqiang Hu and Nantian Huang
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Lin Lin: College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 132022, China
Lin Xue: School of Electrical Engineering, Northeast Electric Power University, Jilin 132013, China
Zhiqiang Hu: Zhejiang Electric Power Corporation Wenzhou Power Supply Company, Wenzhou 325000, China
Nantian Huang: School of Electrical Engineering, Northeast Electric Power University, Jilin 132013, China

Energies, 2018, vol. 11, issue 7, 1-30

Abstract: To improve the accuracy of the day-ahead load forecasting predictions of a single model, a novel modular parallel forecasting model with feature selection was proposed. First, load features were extracted from a historic load with a horizon from the previous 24 h to the previous 168 h considering the calendar feature. Second, a feature selection combined with a predictor process was carried out to select the optimal feature for building a reliable predictor with respect to each hour. The final modular model consisted of 24 predictors with a respective optimal feature subset for day-ahead load forecasting. New England and Singapore load data were used to evaluate the effectiveness of the proposed method. The results indicated that the accuracy of the proposed modular model was higher than that of the traditional method. Furthermore, conducting a feature selection step when building a predictor improved the accuracy of load forecasting.

Keywords: day-ahead load forecasting; modular predictor; feature selection (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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