カテゴリ: 論文誌(論文単位)グループ名: 【C】電子・情報・システム部門発行日: 2020/07/01タイトル(英語): Short-Term Electricity Consumption Forecasting Based on the Attentive Encoder-Decoder Model著者名: Wen Song (Graduate School of Information, Production and Systems, Waseda University), Widyaning Chandramitasari (Graduate School of Information, Production and Systems, Waseda University), Wei Weng (Graduate School of Information, Production and Systems, 著者名(英語): Wen Song (Graduate School of Information, Production and Systems, Waseda University), Widyaning Chandramitasari (Graduate School of Information, Production and Systems, Waseda University), Wei Weng (Graduate School of Information, Production and Systems, Waseda University), Shigeru Fujimura (Graduate School of Information, Production and Systems, Waseda University)キーワード: attention-mechanism，consumption forecasting，deep learning，Encoder-Decoder，time series要約(英語): Electricity consumption forecasting plays an important role in establishing and maintaining electric supply management systems. Power companies need to keep a balance between the power demand and supply for customers; this requires an accurate forecast. However, electricity consumption forecasting is affected by various factors such as different weather conditions, season, or temperature. If we cannot predict electricity accurately, the balance between the demand and supply would be destroyed, which may cause huge penalties to power companies. Therefore, electricity consumption forecasting is an important task. The purpose of this study was to forecast the electricity consumption of a manufacturing company every half an hour in the next day to prevent a power supply company from running out of power. In our work, we proposed a short-term electricity consumption forecasting method based on the attentive encoder-decoder and several nonlinear multi-layer correctors. The proposed method is verified in several experiments by using the actual data on electricity consumption of the manufacturing company. The results show that the proposed method outperforms previous methods.本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.140 No.7 （2020） 特集：2019年電子・情報・システム部門大会本誌掲載ページ: 846-855 p原稿種別: 論文／英語電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/140/7/140_846/_article/-char/ja/
