Short-term residential load forecasting based on resident behaviour learning

Weicong Kong, Zhao Yang Dong, David J. Hill, Fengji Luo, Yan Xu

Research output: Contribution to journalArticleResearchpeer-review

515 Citations (Scopus)

Abstract

Residential load forecasting has been playing an increasingly important role in modern smart grids. Due to the variability of residents' activities, individual residential loads are usually too volatile to forecast accurately. An LSTM based deep learning forecasting framework with appliance consumption sequences is proposed to address such volatile problem. It is shown that the forecasting accuracy can be notably improved by including appliance measurements in the training data. The effectiveness of the proposed method is validated through extensive comparison studies on a real-world dataset.

Original languageEnglish
Pages (from-to)1087-1088
Number of pages2
JournalIEEE Transactions on Power Systems
Volume33
Issue number1
DOIs
Publication statusPublished - Jan 2018
Externally publishedYes

Keywords

  • Deep learning
  • Meter-level load forecasting
  • Recurrent neural network
  • Short-Term load forecasting

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