Stacked LSTM Sequence-to-Sequence Autoencoder with Feature Selection for Daily Solar Radiation Prediction: A Review and New Modeling Results

We review the latest modeling techniques and propose new hybrid SAELSTM framework based on Deep Learning (DL) to construct prediction intervals for daily Global Solar Radiation (GSR) using the Manta Ray Foraging Optimization (MRFO) feature selection to select model parameters. Features are employed...

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Bibliographic Details
Published in:	Energies (Basel) Vol. 15; no. 3; p. 1061
Main Authors:	Ghimire, Sujan, Deo, Ravinesh C., Wang, Hua, Al-Musaylh, Mohanad S., Casillas-Pérez, David, Salcedo-Sanz, Sancho
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.02.2022
Subjects:	Accuracy Alternative energy sources autoencoder Climate change COVID-19 deep learning Feature selection Genetic algorithms LSTM network Neural networks Radiation Renewable resources sequence to sequence (Seq2Seq) model Solar energy solar energy monitoring sustainable renewable energy Wavelet transforms Queensland Australia Australia
ISSN:	1996-1073, 1996-1073
Online Access:	Get full text
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