Short-Term Electricity Consumption Forecasting based on LSTM Neural Network

カテゴリ: 部門大会

論文No: SS3-1

グループ名: 【C】2019年電気学会電子・情報・システム部門大会プログラム

発行日: 2019/08/28

タイトル(英語): Short-Term Electricity Consumption Forecasting based on LSTM Neural Network

著者名: Song Wen(早稲田大学),Widyaning Chandramitasari(早稲田大学),Fujimura Shigeru(早稲田大学)

著者名(英語): Wen Song|Chandramitasari Widyaning|Shigeru Fujimura

キーワード: consumption forecasting|time series|deep learning|LSTM

要約(日本語): Electricity consumption forecast plays a significant role in the electric supply management system. Power companies need to maintain a balance between power demand and supply for customers power consumption is always affected by several factors. Our goal is to predict the electricity consumption of the manufacturing company every half an hour the next day. In our work, we proposed a deep learning neural network model based on the Long Short-Term Memory(LSTM). Our proposed method performs several experiments in actual time series data of the manufacturing company's power consumption. The experiment result shows that the proposed method outperforms the previous research of LSTM-FFNN and Moving Average(MA) based on the loss of Root Mean Squared Error(RMSE) score.

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