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Open AccessArticle

A New Hybrid Approach for Short-Term Electric Load Forecasting Applying Support Vector Machine with Ensemble Empirical Mode Decomposition and Whale Optimization

by Tongxiang Liu 1, Yu Jin 2,* and Yuyang Gao 2
1
Faculty of Professions, University of Adelaide, Adelaide 5000, Australia
2
School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China
*
Author to whom correspondence should be addressed.
Energies 2019, 12(8), 1520; https://doi.org/10.3390/en12081520
Received: 25 February 2019 / Revised: 15 April 2019 / Accepted: 17 April 2019 / Published: 22 April 2019
(This article belongs to the Special Issue Modelling and Simulation of Smart Energy Management Systems)
Electrical power system forecasting has been a main focus for researchers who want to improve the effectiveness of a power station. Although some traditional models have been proved suitable for short-term electric load forecasting, its nature of ignoring the significance of parameter optimization and data preprocessing usually results in low forecasting accuracy. This paper proposes a short-term hybrid forecasting approach which consists of the three following modules: Data preprocessing, parameter optimization algorithm, and forecasting. This hybrid model overcomes the disadvantages of the conventional model and achieves high forecasting performance. To verify the forecasting effectiveness of the hybrid method, 30-minutes of electric load data from power stations in New South Wales and Queensland are used for conducting experiments. A comprehensive evaluation, including a Diebold-Mariano (DM) test and forecasting effectiveness, is applied to verify the ability of the hybrid approach. Experimental results indicated that the new hybrid method can perform accurate electric load forecasting, which can be regarded as a powerful assist in managing smart grids. View Full-Text
Keywords: electric load forecasting; ensemble empirical mode decomposition; whale optimization; support vector machine electric load forecasting; ensemble empirical mode decomposition; whale optimization; support vector machine
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Liu, T.; Jin, Y.; Gao, Y. A New Hybrid Approach for Short-Term Electric Load Forecasting Applying Support Vector Machine with Ensemble Empirical Mode Decomposition and Whale Optimization. Energies 2019, 12, 1520.

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