• 대한전기학회
Mobile QR Code QR CODE : The Transactions of the Korean Institute of Electrical Engineers
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  • 한국과학기술단체총연합회
  • 한국학술지인용색인
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Title Application of Neural Networks to Short-Term Load Forecasting Using Electrical Load Pattern
Authors 박후식(Park, Hu-Sik) ; 문경준(Mun, Gyeong-Jun) ; 김형수(Kim, Hyeong-Su) ; 황지현(Hwang, Ji-Hyeon) ; 이화석(Lee, Hwa-Seok) ; 박준호(Park, Jun-Ho)
Page pp.8-14
ISSN 1975-8359
Keywords Short-term load forecasting ; Kohonen neural networks ; Back-propagation neural networks
Abstract This paper presents the methods of short-term load forecasting Kohonen neural networks and back-propagation neural networks. First, historical load data is divided into 5 patterns for the each seasonal data using Kohonen neural networks and using these results, load forecasting neural network is used for next day hourly load forecasting. Next day hourly load of weekdays and weekend except holidays are forecasted. For load forecasting in summer, max-temperature and min-temperature data as well as historical hourly load date are used as inputs of load forecasting neural networks for a better forecasting accuracy. To show the possibility of the proposed method, it was tested with hourly load data of Korea Electric Power Corporation(1994-95).