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

Zhenyuan Xu, Junzo Watada, Jiliang Xue

    研究成果: Conference 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.

    ホスト出版物のタイトルFrontiers in Artificial Intelligence and Applications
    出版者IOS Press
    出版物ステータスPublished - 2014


    名前Frontiers in Artificial Intelligence and Applications

    ASJC Scopus subject areas

    • Artificial Intelligence

    フィンガープリント A PSO based NN-SVM for short-term load forecasting' の研究トピックを掘り下げます。これらはともに一意のフィンガープリントを構成します。

  • これを引用

    Xu, Z., Watada, J., & Xue, J. (2014). A PSO based NN-SVM for short-term load forecasting. : Frontiers in Artificial Intelligence and Applications (巻 262, pp. 219-227). (Frontiers in Artificial Intelligence and Applications; 巻数 262). IOS Press. https://doi.org/10.3233/978-1-61499-405-3-219