Short-Term Electric Load Forecasting Based on a Neural Fuzzy Network

S. Ling, H.K. Leung, H.K. Lam, P.K.S. Tam

    Research output: Contribution to journalArticle

    77 Citations (Scopus)

    Abstract

    Electric load forecasting is, essential to improve the reliability of the ac power line data network and provide optimal load scheduling in an intelligent home system. In this paper, a short-term load forecasting realized by a neural fuzzy network (NFN) and a modified genetic algorithm (GA) is proposed. It can forecast the hourly load accurately with respect to different day types and weather information. By introducing new genetic operators, the modified GA performs better than the traditional GA under some benchmark test functions. The optimal network structure can be found by the modified GA when switches in the links of the network are introduced. The membership functions and the number of rules of the NFN can be obtained automatically. Results for a short-term load forecasting will be given.
    Original languageEnglish
    Pages (from-to)1305-1315
    JournalIEEE Transactions on Industrial Electronics
    Volume50
    Issue number6
    DOIs
    Publication statusPublished - 2003

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