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

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
Main Authors: Mohamed Massaoudi, Shady S. Refaat, Haitham Abu-Rub, Ines Chihi, Fakhreddine S. Oueslati
Format: Article
Language:English
Published: MDPI AG 2020-10-01
Series:Energies
Subjects:
Bidirectional Long Short-Term Memory (BiLSTM)
Convolutional Neural Network (CNN)
evolution strategy
Partial Least Square (PLS) method
Savitzky–Golay
Short-Term Load Forecasting (STLF)
Online Access:https://www.mdpi.com/1996-1073/13/20/5464

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