PLS-CNN-BiLSTM: An end-to-end algorithm-based savitzky-golay smoothing and evolution strategy for load forecasting

Mohamed Massaoudi*, Shady S. Refaat, Haitham Abu-Rub, Ines Chihi, Fakhreddine S. Oueslati

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

44 Citations (Scopus)

Abstract

This paper proposes an effective deep learning framework for Short-Term Load Forecasting (STLF) of multivariate time series. The proposed model consists of a hybrid Convolutional neural network-Bidirectional Long Short-Term Memory (CBiLSTM) based on the Evolution Strategy (ES) method and the Savitzky-Golay (SG) filter (SG-CBiLSTM). The adopted methodology incorporates the virtue of different prepossessing blocks to enhance the performance of the CBiLSTM model. In particular, a data-augmentation strategy is employed to synthetically improve the feature representation of the CBiLSTM model. The augmented data is forwarded to the Partial Least Square (PLS) method to select the most informative features above the predefined threshold. Next, the SG algorithm is computed for smoothing the load to enhance the learning capabilities of the underlying system. The structure of the SG-CBiLSTM for the ISO New England dataset is optimized using the ES technique. Finally, the CBiLSTM model generates output forecasts. The proposed approach demonstrates a remarkable improvement in the performance of the original CBiLSTM model. Furthermore, the experimental results strongly confirm the high effectiveness of the proposed SG-CBiLSTM model compared to the state-of-the-art techniques.

Original languageEnglish
Article number5464
JournalEnergies
Volume13
Issue number20
DOIs
Publication statusPublished - 19 Oct 2020
Externally publishedYes

Keywords

  • Bidirectional Long Short-Term Memory (BiLSTM)
  • Convolutional Neural Network (CNN)
  • Evolution strategy
  • Partial Least Square (PLS) method
  • Savitzky-Golay
  • Short-Term Load Forecasting (STLF)

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