Short-Term Electrical Load Forecasting with A Self-Organizing Map

カテゴリ: 部門大会

論文No: 3

グループ名: 【B】平成17年電気学会電力・エネルギー部門大会講演論文集

発行日: 2005/08/10

タイトル(英語): Short-Term Electrical Load Forecasting with A Self-Organizing Map

著者名: Shu Fan(Osaka Sangyo University),Chengxiong Mao(Huazhong University of Science and Technology),Luonan Chen(Osaka Sangyo University)

著者名(英語): Shu Fan(Osaka Sangyo University),Chengxiong Mao(Huazhong University of Science and Technology),Luonan Chen(Osaka Sangyo University)

キーワード: Self-organizing map|load forecast|unsupervised learning|neural networks

要約(日本語): This paper aims to study the short-term load forecasting of electricity by using an extended self-organizing map. We first adopt a traditional Kohonen self-organizing map (SOM) to learn time-series load data with weather information as parameters, based on an unsupervised learning strategy. Then, in order to improve the accuracy of the prediction, an extension of SOM algorithm with an error-correction learning rule is proposed, based on a supervised learning scheme. The estimation of the peak load for next day is achieved by averaging the output of all the neurons in the forecasting. Finally, as an implementation example, data of electricity demand from New York Independent System Operator (ISO) are used to verify the effectiveness of the learning and prediction for the proposed methods.

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