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

Sujan Ghimire, Ravinesh C. Deo, Hua Wang, Mohanad S. Al-Musaylh, David Casillas-Pérez and Sancho Salcedo-Sanz
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Sujan Ghimire: School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4300, Australia
Ravinesh C. Deo: School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4300, Australia
Hua Wang: Institute of Sustainable Industries and Liveable Cities, Victoria University, Melbourne, VIC 3122, Australia
Mohanad S. Al-Musaylh: Management Technical College, Southern Technical University, Basrah 61001, Iraq
David Casillas-Pérez: Department of Signal Processing and Communications, Universidad Rey Juan Carlos, 28942 Fuenlabrada, Spain
Sancho Salcedo-Sanz: Department of Signal Processing and Communications, Universidad de Alcalá, 28805 Alcalá de Henares, Spain

Energies, 2022, vol. 15, issue 3, 1-39

Abstract: 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 as potential inputs for Long Short-Term Memory and a seq2seq SAELSTM autoencoder Deep Learning (DL) system in the final GSR prediction. Six solar energy farms in Queensland, Australia are considered to evaluate the method with predictors from Global Climate Models and ground-based observation. Comparisons are carried out among DL models (i.e., Deep Neural Network) and conventional Machine Learning algorithms (i.e., Gradient Boosting Regression, Random Forest Regression, Extremely Randomized Trees, and Adaptive Boosting Regression). The hyperparameters are deduced with grid search, and simulations demonstrate that the DL hybrid SAELSTM model is accurate compared with the other models as well as the persistence methods. The SAELSTM model obtains quality solar energy prediction intervals with high coverage probability and low interval errors. The review and new modelling results utilising an autoencoder deep learning method show that our approach is acceptable to predict solar radiation, and therefore is useful in solar energy monitoring systems to capture the stochastic variations in solar power generation due to cloud cover, aerosols, ozone changes, and other atmospheric attenuation factors.

Keywords: LSTM network; sequence to sequence (Seq2Seq) model; autoencoder; solar energy monitoring; sustainable renewable energy; deep learning (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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