A PSO based NN-SVM for short-term load forecasting

Zhenyuan Xu*, Junzo Watada, Jiliang Xue

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contribution


    Load forecasting has become one of the core research topics in the power system. As power load has time-variant characteristics and nonlinear characteristics, different computational intelligent techniques, neural networks (NN) in particular, are used in short-term load forecasting (STLF) to make it more effective. This study proposes a Particle Swarm Optimization (PSO) - based neural network with support vector machine (NN-SVM) model to predict the power load in short-term forecasting by using a radial-basis-function neural network (RBFNN), SVM and PSO. There are two stages in the proposed model. The first stage applies the RBFNN to predict monthly variations, and the second stage trains the SVM through hourly data to obtain the final forecast for short-term load forecasting (STLF). In the process of SVM training and NN learning, PSO is used to find the optimal parameters. The results of several experiments show that this new model performs more accurately and stably than some conventional models including RBFNN, RGA-SVM, Karman filter in STLF.

    Original languageEnglish
    Title of host publicationFrontiers in Artificial Intelligence and Applications
    PublisherIOS Press
    Number of pages9
    ISBN (Print)9781614994046
    Publication statusPublished - 2014

    Publication series

    NameFrontiers in Artificial Intelligence and Applications
    ISSN (Print)09226389


    • particle swarm optimization (PSO)
    • radial-basis-function neural network (RBFNN)
    • short-term forecasting (STLF)
    • support vector machine (SVM)

    ASJC Scopus subject areas

    • Artificial Intelligence


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