An Ensemble Model Based on Machine Learning Methods and Data Preprocessing for Short-Term Electric Load Forecasting

Yanbing Lin, Hongyuan Luo, Deyun Wang, Haixiang Guo and Kejun Zhu
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Yanbing Lin: School of Economics and Management, China University of Geosciences, Wuhan 430074, China
Hongyuan Luo: School of Economics and Management, China University of Geosciences, Wuhan 430074, China
Deyun Wang: School of Economics and Management, China University of Geosciences, Wuhan 430074, China
Haixiang Guo: School of Economics and Management, China University of Geosciences, Wuhan 430074, China
Kejun Zhu: School of Economics and Management, China University of Geosciences, Wuhan 430074, China

Energies, 2017, vol. 10, issue 8, 1-16

Abstract: The experience with deregulated electricity market has shown the increasingly important role of short-term electric load forecasting in the energy producing and scheduling. However, because of nonlinear, stochastic and nonstable characteristics associated with the electric load series, it is extremely difficult to precisely forecast the electric load. This paper aims to establish a novel ensemble model based on variational mode decomposition (VMD) and extreme learning machine (ELM) optimized by differential evolution (DE) algorithm for multi-step ahead electric load forecasting. The proposed model is novel in the sense that VMD is firstly applied to decompose the original electric load series into a set of components with different frequencies in order to effectively eliminate the stochastic fluctuation characteristic so as to improve the overall prediction accuracy. The proposed ensemble model is tested using two electric load series collected from New South Wales (NSW) and Queensland (QLD) in the Australian electricity market. The experimental results show that: (1) the data preprocessing by VMD can effectively decrease the stochastic fluctuation characteristics that existed in the electric load series, consequently improving the whole forecasting accuracy, and; (2) the proposed forecasting model performs better than all other benchmark models for both one-step and multi-step ahead electric load forecasting.

Keywords: electric load forecasting; variational mode decomposition; extreme learning machine; differential evolution (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: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

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