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Correction published on 16 December 2016, see Energies 2016, 9(12), 1076.

Open AccessArticle
Energies 2016, 9(10), 827; doi:10.3390/en9100827

Short-Term Load Forecasting Based on Wavelet Transform and Least Squares Support Vector Machine Optimized by Improved Cuckoo Search

1
School of Economics and Management, North China Electric Power University, Beijing 102206, China
2
School of Economics and Management, North China Electric Power University, Baoding 071003, China
3
Department of Information Management, Oriental Institute of Technology, New Taipei 220, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editor: Sukanta Basu
Received: 31 August 2016 / Revised: 25 September 2016 / Accepted: 11 October 2016 / Published: 17 October 2016
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Abstract

Due to the electricity market deregulation and integration of renewable resources, electrical load forecasting is becoming increasingly important for the Chinese government in recent years. The electric load cannot be exactly predicted only by a single model, because the short-term electric load is disturbed by several external factors, leading to the characteristics of volatility and instability. To end this, this paper proposes a hybrid model based on wavelet transform (WT) and least squares support vector machine (LSSVM), which is optimized by an improved cuckoo search (CS). To improve the accuracy of prediction, the WT is used to eliminate the high frequency components of the previous day’s load data. Additional, the Gauss disturbance is applied to the process of establishing new solutions based on CS to improve the convergence speed and search ability. Finally, the parameters of the LSSVM model are optimized by using the improved cuckoo search. According to the research outcome, the result of the implementation demonstrates that the hybrid model can be used in the short-term forecasting of the power system. View Full-Text
Keywords: short-term load forecasting; wavelet transform; least squares support vector machine; cuckoo search; Gauss disturbance short-term load forecasting; wavelet transform; least squares support vector machine; cuckoo search; Gauss disturbance
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MDPI and ACS Style

Liang, Y.; Niu, D.; Ye, M.; Hong, W.-C. Short-Term Load Forecasting Based on Wavelet Transform and Least Squares Support Vector Machine Optimized by Improved Cuckoo Search. Energies 2016, 9, 827.

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