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Energies
Volume 11
Issue 7
10.3390/en11071899
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Article

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

by
Lin Lin
1,*,
Lin Xue
2,
Zhiqiang Hu
3 and
Nantian Huang
2
1
College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 132022, China
2
School of Electrical Engineering, Northeast Electric Power University, Jilin 132013, China
3
Zhejiang Electric Power Corporation Wenzhou Power Supply Company, Wenzhou 325000, China
*
Author to whom correspondence should be addressed.
Energies 2018, 11(7), 1899; https://doi.org/10.3390/en11071899
Submission received: 25 June 2018 / Revised: 10 July 2018 / Accepted: 12 July 2018 / Published: 20 July 2018
(This article belongs to the Special Issue Optimization Methods Applied to Power Systems)
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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 day-ahead load forecasting; modular predictor; feature selection

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MDPI and ACS Style

Lin, L.; Xue, L.; Hu, Z.; Huang, N. Modular Predictor for Day-Ahead Load Forecasting and Feature Selection for Different Hours. Energies 2018, 11, 1899. https://doi.org/10.3390/en11071899

AMA Style

Lin L, Xue L, Hu Z, Huang N. Modular Predictor for Day-Ahead Load Forecasting and Feature Selection for Different Hours. Energies. 2018; 11(7):1899. https://doi.org/10.3390/en11071899

Chicago/Turabian Style

Lin, Lin, Lin Xue, Zhiqiang Hu, and Nantian Huang. 2018. "Modular Predictor for Day-Ahead Load Forecasting and Feature Selection for Different Hours" Energies 11, no. 7: 1899. https://doi.org/10.3390/en11071899

APA Style

Lin, L., Xue, L., Hu, Z., & Huang, N. (2018). Modular Predictor for Day-Ahead Load Forecasting and Feature Selection for Different Hours. Energies, 11(7), 1899. https://doi.org/10.3390/en11071899

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