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 and Fakhreddine S. Oueslati
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Mohamed Massaoudi: Department of Electrical and Computer Engineering, Texas A and M University at Qatar, Doha 3263, Qatar
Shady S. Refaat: Department of Electrical and Computer Engineering, Texas A and M University at Qatar, Doha 3263, Qatar
Haitham Abu-Rub: Department of Electrical and Computer Engineering, Texas A and M University at Qatar, Doha 3263, Qatar
Ines Chihi: Laboratory of Energy Applications and Renewable Energy Efficiency (LAPER), El Manar University, Tunis 1002, Tunisia
Fakhreddine S. Oueslati: Unité de Recherche de Physique des Semi-Conducteurs et Capteurs, Carthage University, Tunis 2070, Tunisia

Energies, 2020, vol. 13, issue 20, 1-29

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.

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) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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