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Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 712417, 17 pages
Research Article

Swarm Intelligence-Based Hybrid Models for Short-Term Power Load Prediction

1School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China
2School of Mathematics & Statistics, Lanzhou University, Lanzhou 73000, China
3MOE Key Laboratory of Western China’s Environmental Systems, Research School of Arid Environment & Climate Change, Lanzhou University, Lanzhou 73000, China

Received 6 June 2014; Revised 18 July 2014; Accepted 1 August 2014; Published 30 September 2014

Academic Editor: Fang Zong

Copyright © 2014 Jianzhou Wang et al. 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.


Swarm intelligence (SI) is widely and successfully applied in the engineering field to solve practical optimization problems because various hybrid models, which are based on the SI algorithm and statistical models, are developed to further improve the predictive abilities. In this paper, hybrid intelligent forecasting models based on the cuckoo search (CS) as well as the singular spectrum analysis (SSA), time series, and machine learning methods are proposed to conduct short-term power load prediction. The forecasting performance of the proposed models is augmented by a rolling multistep strategy over the prediction horizon. The test results are representative of the out-performance of the SSA and CS in tuning the seasonal autoregressive integrated moving average (SARIMA) and support vector regression (SVR) in improving load forecasting, which indicates that both the SSA-based data denoising and SI-based intelligent optimization strategy can effectively improve the model’s predictive performance. Additionally, the proposed CS-SSA-SARIMA and CS-SSA-SVR models provide very impressive forecasting results, demonstrating their strong robustness and universal forecasting capacities in terms of short-term power load prediction 24 hours in advance.