一种利用多任务学习的短期住宅负荷预测方案

Translated title of the contribution: An Short-Term Residential Load Forecasting Scheme Using Multi-Task Learning

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

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Translated title of the contributionAn Short-Term Residential Load Forecasting Scheme Using Multi-Task Learning
Original languageChinese (Traditional)
Pages (from-to)47-52
Number of pages6
JournalBeijing Youdian Xueyuan Xuebao/Journal of Beijing University of Posts And Telecommunications
Volume44
Issue number3
DOIs
Publication statusPublished - 2021 Jun

Keywords

  • Bidirectional long short-term memory
  • Cyber-physical-social system
  • Load forecasting
  • Multi-task learning
  • Smart grid

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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