Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting

Ning An, Weigang Zhao*, Jianzhou Wang, Duo Shang, Erdong Zhao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

171 Citations (Scopus)

Abstract

For accurate electricity demand forecasting, this paper proposes a novel approach, MFES, that combines a multi-output FFNN (feedforward neural network) with EMD (empirical mode decomposition)-based signal filtering and seasonal adjustment. In electricity demand forecasting, noise signals, caused by various unstable factors, often corrupt demand series. To reduce these noise signals, MFES first uses an EMD-based signal filtering method which is fully data-driven. Secondly, MFES removes the seasonal component from the denoised demand series and models the resultant series using FFNN model with a multi-output strategy. This multi-output strategy can overcome the limitations of common multi-step-ahead forecasting approaches, including error amplification and the neglect of dependency between inputs and outputs. At last, MFES obtains the final prediction by restoring the season indexes back to the FFNN forecasts. Using the half-hour electricity demand series of New South Wales in Australia, this paper demonstrates that the proposed MFES model improves the forecasting accuracy noticeably comparing with existing models.

Original languageEnglish
Pages (from-to)279-288
Number of pages10
JournalEnergy
Volume49
Issue number1
DOIs
Publication statusPublished - 1 Jan 2013
Externally publishedYes

Keywords

  • EMD-based signal filtering
  • Electricity demand forecasting
  • Feedforward neural network
  • Multi-output forecasting
  • Seasonal adjustment

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