Abstract—This article presents the review of the computing models applied for solving problems of mid-term load forecasting. The load forecasting results can be used in electricity generation such as energy reservation and maintenance scheduling. Principle, strategy and results of short term, midterm, and long term load forecasting using statistic methods and artificial intelligence technology (AI) are summaried, Which, comparison between each method and the articles have difference feature input and strategy. The last, will get the idea or literature review conclusion to solve the problem of mid term load forecasting (MTLF).
Index Terms—Artificial Intelligent, Mid-term load, Forecasting, state-of-the-arts.
P. Bunnoon is with Department of Electrical Engineering, Prince of Songkla University, Thailand (e-mail:add2002k@hotmail.com) Ph.D. Prog.
K. Chalermyanont is with Department of Electrical Engineering, Prince of Songkla University, Thailand (e-mail: kusumal.c@psu.ac.th).
C. Limsakul is with Department of Electrical Engineering, Prince of Songkla University, Thailand (e-mail: chusak.l@psu.ac.th).
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Cite: Pituk Bunnoon, Kusumal Chalermyanont and Chusak Limsakul, "A Computing Model of Artificial Intelligent Approaches to Mid-term Load Forecasting: a state-of-the-art- survey for the researcher,"
International Journal of Engineering and Technology vol. 2, no. 1, pp. 94-100, 2010.
