Title: Hybrid approach-based support vector machine for electric load forecasting incorporating feature selection
Authors: Malek Sarhani; Abdellatif El Afia; Rdouan Faizi
Addresses: Moroccan School of Computer Science (ENSIAS), Mohammed V University, Rabat, Morocco ' Moroccan School of Computer Science (ENSIAS), Mohammed V University, Rabat, Morocco ' Moroccan School of Computer Science (ENSIAS), Mohammed V University, Rabat, Morocco
Abstract: Forecasting future electricity demand is very important for the electric power industry. In fact, it has been shown in several research works that machine learning methods are useful for electric load forecasting (ELF) since electric load data are nonlinear in relation and complex. On the other hand, it is important to determine the irrelevant factors as a preprocessing step for ELF. Our objective in this paper is to investigate the importance of applying the feature selection approach to remove the irrelevant factors of electric load. To this end, we introduce a hybrid machine learning approach that combines support vector machine (SVM) and particle swarm optimisation (PSO) in both continuous and binary forms. Specifically, the binary hybridisation is used for feature selection and the continuous one is used for ELF. Experimental results demonstrate the feasibility of applying feature selection by SVM and PSO algorithms without decreasing the performance of the forecasting model for ELF.
Keywords: machine learning; electric load forecasting; ELF; feature selection; big data; support vector machine; SVM; particle swarm optimisation; PSO.
International Journal of Big Data Intelligence, 2017 Vol.4 No.3, pp.141 - 148
Available online: 28 Jul 2017 *Full-text access for editors Access for subscribers Purchase this article Comment on this article