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Energies 2017, 10(8), 1186; doi:10.3390/en10081186

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

1
School of Economics and Management, China University of Geosciences, Wuhan 430074, China
2
Mineral Resource Strategy and Policy Research Center, China University of Geosciences, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Received: 14 July 2017 / Revised: 4 August 2017 / Accepted: 7 August 2017 / Published: 11 August 2017
(This article belongs to the Section Electrical Power and Energy System)
View Full-Text   |   Download PDF [1585 KB, uploaded 11 August 2017]   |  

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. View Full-Text
Keywords: electric load forecasting; variational mode decomposition; extreme learning machine; differential evolution electric load forecasting; variational mode decomposition; extreme learning machine; differential evolution
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Lin, Y.; Luo, H.; Wang, D.; Guo, H.; Zhu, K. An Ensemble Model Based on Machine Learning Methods and Data Preprocessing for Short-Term Electric Load Forecasting. Energies 2017, 10, 1186.

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