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

Yi Liang, Dongxiao Niu, Minquan Ye and Wei-Chiang Hong ()
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Yi Liang: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Dongxiao Niu: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Minquan Ye: School of Economics and Management, North China Electric Power University, Baoding 071003, China

Energies, 2016, vol. 9, issue 10, 1-17

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.

Keywords: short-term load forecasting; wavelet transform; least squares support vector machine; cuckoo search; Gauss disturbance (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: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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