一种利用多任务学习的短期住宅负荷预测方案

Yu Feng Wang*, Can Bin Xiao, Yan Chen, Qun Jin

*この研究の対応する著者

研究成果: Article査読

抄録

In smart grid regarded as specific embodying of cyber-physical-social system, load forecasting, especially short-term load forecasting for individual electric customers plays an increasingly role in planning and operation of smart power system. Considering the similarity of electricity consumption between users, inspired by multi-task learning, the article puts forward an effective residential load forecasting based on multi-task learning model. In detail, the K-means clustering technology and Pearson correlation coefficient are used to select two similar users. Then these two user's load data are merged as input, the bidirectional long short-term memory network is used as a sharing layer to fully capture the relationship between the data of the two users, and then two fully-connection task-specific output layers are respectively built. Based on real datasets, the proposed scheme is thoroughly compared with several typical deep learning based load forecasting schemes. Experiments show that proposed multi-task learning scheme improves the prediction accuracy compared with the existing deep learning prediction scheme.

寄稿の翻訳タイトルAn Short-Term Residential Load Forecasting Scheme Using Multi-Task Learning
本文言語!!
ページ(範囲)47-52
ページ数6
ジャーナルBeijing Youdian Xueyuan Xuebao/Journal of Beijing University of Posts And Telecommunications
44
3
DOI
出版ステータスPublished - 2021 6

ASJC Scopus subject areas

  • コンピュータ ネットワークおよび通信
  • 電子工学および電気工学

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