An implementation of novel CMAC algorithm for very short term load forecasting

H.A. Khan, A.C.M. Tan, Y. Xiao, Victor Sreeram, Herbert Iu

    Research output: Contribution to journalArticle

    1 Citation (Scopus)

    Abstract

    Load forecasting is an issue that needs to be addressed to prevent overloading and catastrophic blackouts in power grids. This paper proposes a dual cerebellar model articulation controller (CMAC) neural network which is able to give an accurate very short term prediction of the required load curve. This is the first time a CMAC algorithm has been employed for load forecasting. The paper depicts that the proposed method has the advantage of reduced training time and reduced computational requirements as compared to the other load forecasting techniques. The data of the south west interconnected system was employed to give load predictions whilst using the proposed dual CMAC and back propagation neural network. The performance evolution has shown that the proposed dual CMAC neural network works efficiently and accurately for very short term load forecasting scenarios as compared to other conventional load forecasting techniques. © 2012 Springer-Verlag.
    Original languageEnglish
    Pages (from-to)673-683
    JournalJournal of Ambient Intelligence and Humanized Computing
    Volume4
    Issue number6
    DOIs
    Publication statusPublished - 2013

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