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Energies 2013, 6(4), 1887-1901; doi:10.3390/en6041887
Article

Support Vector Regression Model Based on Empirical Mode Decomposition and Auto Regression for Electric Load Forecasting

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Received: 28 November 2012 / Revised: 2 February 2013 / Accepted: 25 March 2013 / Published: 2 April 2013
(This article belongs to the Special Issue Hybrid Advanced Techniques for Forecasting in Energy Sector)
Download PDF [542 KB, 17 March 2015; original version 17 March 2015]

Abstract

Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR), this paper presents a SVR model hybridized with the empirical mode decomposition (EMD) method and auto regression (AR) for electric load forecasting. The electric load data of the New South Wales (Australia) market are employed for comparing the forecasting performances of different forecasting models. The results confirm the validity of the idea that the proposed model can simultaneously provide forecasting with good accuracy and interpretability.
Keywords: electric load prediction; support vector regression; empirical mode decomposition auto regression electric load prediction; support vector regression; empirical mode decomposition auto regression
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Fan, G.-F.; Qing, S.; Wang, H.; Hong, W.-C.; Li, H.-J. Support Vector Regression Model Based on Empirical Mode Decomposition and Auto Regression for Electric Load Forecasting. Energies 2013, 6, 1887-1901.

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