PLS-CNN-BiLSTM: An End-to-End Algorithm-Based Savitzky–Golay Smoothing and Evolution Strategy for Load Forecasting

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 Savitzk...

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Bibliographic Details
Published in:Energies (Basel) 2020-10, Vol.13 (20), p.5464
Main Authors: Massaoudi, Mohamed, S. Refaat, Shady, Abu-Rub, Haitham, Chihi, Ines, Oueslati, Fakhreddine S.
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Alternative energy sources
Bidirectional Long Short-Term Memory (BiLSTM)
Consumption
Convolutional Neural Network (CNN)
Data smoothing
evolution strategy
Evolutionary algorithms
Forecasting
Long short-term memory
Natural language
Neural networks
Partial Least Square (PLS) method
Savitzky–Golay
Short-Term Load Forecasting (STLF)
Smoothing
Time series
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en13205464