Abstract
Accurate forecasting of short-term electricity load has been one of the most important issues in the electricity industry. Because of the remarkable nonlinear mapping capabilities of forecasting, artificial neural networks have played a crucial role in forecasting electricity load. Support vector machine (SVM) is a novel type of learning machine, which has been successfully employed to solve nonlinear regression and time series problems. This paper investigates the feasibility of using SVM to forecast electricity load. Moreover, the particle swarm optimization (PSO) algorithm is employed to determine the free parameters of the SVM model automatically. Subsequently, examples of electricity load data from a practical power system were used to verify the proposed PSO-SVM model. The empirical results reveal that the proposed model outperforms the other two models. Consequently, the PSO-SVM model provides a promising alternative for forecasting electricity load
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
