Short-term electricity consumption forecasting based on the attentive encoder-decoder model

Wen Song*, Widyaning Chandramitasari, Wei Weng, Shigeru Fujimura


研究成果: Article査読

2 被引用数 (Scopus)


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.

ジャーナルIEEJ Transactions on Electronics, Information and Systems
出版ステータスPublished - 2020 7月 1

ASJC Scopus subject areas

  • 電子工学および電気工学


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