Recurrent neural network for forecasting next 10 years loads of 9 Japanese utilities

B. Kermanshahi, Y. Akiyama, R. Yokoyama, M. Asari, K. Takahashi

    研究成果: Conference contribution

    抄録

    In this paper, a recurrent neural network (RNN) is applied for long-term load forecasting. The proposed RNN is trained with the past 20 years (1975-1994) of actual data and is tested for target years (1995-1997, 2000, and 2005). In addition to the target year load forecasting, a sliding window training method is proposed for continuous retraining of the RNN. The actual data of 9 Japanese power utilities is used for forecasting the loads of 1975 to 1994. However, forecasted data is applied for forecasting the loads beyond 1994. Since the weather condition data is not available for longer than two weeks ahead, a sensitivity program is developed to produce the future temperature from the present one. Very reasonable results have been obtained for the seen (inner sample) and unseen (out-of-sample or loads of target years) data. In this study, total system load forecast reflecting current and future trends, tempered with good judgment which is the key to all planning, indeed financial success is carried out for 9 power utilities in Japan. The obtained results of this study will be useful for other country's utilities.

    本文言語English
    ホスト出版物のタイトルProceedings of the Universities Power Engineering Conference
    Place of PublicationIraklio, Greece
    出版社Technological Educational Institute
    ページ895-898
    ページ数4
    2
    出版ステータスPublished - 1997
    イベントProceedings of the 1997 32nd Univertsities Power Engineering Conference, UPEC'97. Part 2 (of 2) - Manchester, UK
    継続期間: 1997 9 101997 9 12

    Other

    OtherProceedings of the 1997 32nd Univertsities Power Engineering Conference, UPEC'97. Part 2 (of 2)
    CityManchester, UK
    Period97/9/1097/9/12

    ASJC Scopus subject areas

    • エネルギー(全般)
    • 工学(全般)

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