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Towards Electricity Price and Electric Load Forecasting Using Multi-task Deep Learning

  • Yali Liu
  • , Tingting Chai
  • , Zhaoxin Zhang*
  • , Gang Long
  • *Corresponding author for this work
  • Faculty of Computing, Harbin Institute of Technology
  • China Information Technology Security Evaluation Center

Research output: Contribution to journalConference articlepeer-review

Abstract

The continuous development of the power Internet of Things (IOT) has enabled power market participants to obtain a large amount of data. Simultaneously, the power IOT has an increasing demand for power load and electricity price forecasting; Since the forecasting of electricity load and electricity price is a single task, and the model calculation accuracy is not high, this brings great challenges to the accurate forecasting of electricity load and electricity price. In this paper, two power load and electricity price forecasting models via multi-task deep learning are established perform high-precision joint forecasting of power load and electricity price Experimental results demonstrate that the prediction results of the proposed deep learning models are superior to the other compared approaches in terms of the main task and the auxiliary task, and show superior prediction performance, verifying the practicability and superiority of the power load and electricity price multi-task forecasting model.

Original languageEnglish
Article number012048
JournalJournal of Physics: Conference Series
Volume2171
Issue number1
DOIs
StatePublished - 24 Jan 2022
Externally publishedYes
Event2nd International Conference on Computer, Big Data and Artificial Intelligence, ICCBDAI 2021 - Virtual, Online
Duration: 12 Nov 202114 Nov 2021

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