Short-term electric load forecasting based on singular spectrum analysis and support vector machine optimized by Cuckoo search algorithm

Short-term electric load forecasting (STLF) has been one of the most active areas of research because of its vital role in planning and operation of power systems. Additionally, intelligent methods are increasingly popular in forecasting model applications. However, the observed data set is often contaminated and nonlinear by as a result of such that it becomes difficult to enhance the accuracy of STLF. Therefore, the novel model (CS-SSA-SVM) for electric load forecasting in this paper was successfully proposed by the combination of SSA (singular spectrum analysis), SVM (support vector machine) and CS (Cuckoo search) algorithms. First, the signal filtering technique (SSA) is applied for data pre-processing and the novel model subsequently models the resultant series with different forecasting strategies using SVM optimized by the CS algorithm. Finally, experiments of electric load forecasting are used as illustrative examples to evaluate the performance of the developed model. The empirical results demonstrated that the proposed model (CS-SSA-SVM) can improve the performance of electric load forecasting considerably in comparison with other methods (SVM, CS-SVM, SSA-SVM, SARIMA and BPNN). (C) 2017 Elsevier B.V. All rights reserved.