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PLS-CNN-BiLSTM: An End-to-End Algorithm-Based Savitzky–Golay Smoothing and Evolution Strategy for Load Forecasting
| Content Provider | MDPI |
|---|---|
| Author | Massaoudi, Mohamed Refaat, Shady S. Abu-Rub, Haitham Chihi, Ines Oueslati, Fakhreddine |
| Copyright Year | 2020 |
| Description | 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. |
| Starting Page | 5464 |
| e-ISSN | 19961073 |
| DOI | 10.3390/en13205464 |
| Journal | Energies |
| Issue Number | 20 |
| Volume Number | 13 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-10-19 |
| Access Restriction | Open |
| Subject Keyword | Energies Industrial Engineering Bidirectional Long Short-term Memory (bilstm) Convolutional Neural Network (cnn) Evolution Strategy Partial Least Square (pls) Method Savitzky–golay Short-term Load Forecasting (stlf) |
| Content Type | Text |
| Resource Type | Article |
