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Investigating the Power of LSTM-Based Models in Solar Energy Forecasting

Solar is a significant renewable energy source. Solar energy can provide for the world�s energy needs while minimizing global warming from traditional sources. Forecasting the output of renewable energy has a considerable impact on decisions about the operation and management of power systems. It is...

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Main Authors:	Jailani N.L.M., Dhanasegaran J.K., Alkawsi G., Alkahtani A.A., Phing C.C., Baashar Y., Capretz L.F., Al-Shetwi A.Q., Tiong S.K.
Other Authors:	58297401800
Format:	Review
Published:	Multidisciplinary Digital Publishing Institute (MDPI) 2024
Subjects:	deep learning hybrid model long short-term memory photovoltaic power forecasting renewable energy solar irradiance forecasting Brain Global warming Learning systems Long short-term memory Mean square error Natural resources Solar energy Solar power generation Solar radiation Deep learning Hybrid model Photovoltaic power Photovoltaic power forecasting Power Power forecasting Renewable energies Renewable energy source Solar irradiance forecasting Solar irradiances Forecasting
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id	my.uniten.dspace-34234
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spelling	my.uniten.dspace-342342024-10-14T11:18:33Z Investigating the Power of LSTM-Based Models in Solar Energy Forecasting Jailani N.L.M. Dhanasegaran J.K. Alkawsi G. Alkahtani A.A. Phing C.C. Baashar Y. Capretz L.F. Al-Shetwi A.Q. Tiong S.K. 58297401800 58296523900 57191982354 55646765500 57884999200 56768090200 6602660867 57004922700 15128307800 deep learning hybrid model long short-term memory photovoltaic power forecasting renewable energy solar irradiance forecasting Brain Global warming Learning systems Long short-term memory Mean square error Natural resources Solar energy Solar power generation Solar radiation Deep learning Hybrid model Photovoltaic power Photovoltaic power forecasting Power Power forecasting Renewable energies Renewable energy source Solar irradiance forecasting Solar irradiances Forecasting Solar is a significant renewable energy source. Solar energy can provide for the world�s energy needs while minimizing global warming from traditional sources. Forecasting the output of renewable energy has a considerable impact on decisions about the operation and management of power systems. It is crucial to accurately forecast the output of renewable energy sources in order to assure grid dependability and sustainability and to reduce the risk and expense of energy markets and systems. Recent advancements in long short-term memory (LSTM) have attracted researchers to the model, and its promising potential is reflected in the method�s richness and the growing number of papers about it. To facilitate further research and development in this area, this paper investigates LSTM models for forecasting solar energy by using time-series data. The paper is divided into two parts: (1) independent LSTM models and (2) hybrid models that incorporate LSTM as another type of technique. The Root mean square error (RMSE) and other error metrics are used as the representative evaluation metrics for comparing the accuracy of the selected methods. According to empirical studies, the two types of models (independent LSTM and hybrid) have distinct advantages and disadvantages depending on the scenario. For instance, LSTM outperforms the other standalone models, but hybrid models generally outperform standalone models despite their longer data training time requirement. The most notable discovery is the better suitability of LSTM as a predictive model to forecast the amount of solar radiation and photovoltaic power compared with other conventional machine learning methods. � 2023 by the authors. Final 2024-10-14T03:18:33Z 2024-10-14T03:18:33Z 2023 Review 10.3390/pr11051382 2-s2.0-85160800465 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85160800465&doi=10.3390%2fpr11051382&partnerID=40&md5=648aa3e5dd02a1c040406f5bc234d207 https://irepository.uniten.edu.my/handle/123456789/34234 11 5 1382 All Open Access Gold Open Access Multidisciplinary Digital Publishing Institute (MDPI) Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
topic	deep learning hybrid model long short-term memory photovoltaic power forecasting renewable energy solar irradiance forecasting Brain Global warming Learning systems Long short-term memory Mean square error Natural resources Solar energy Solar power generation Solar radiation Deep learning Hybrid model Photovoltaic power Photovoltaic power forecasting Power Power forecasting Renewable energies Renewable energy source Solar irradiance forecasting Solar irradiances Forecasting
spellingShingle	deep learning hybrid model long short-term memory photovoltaic power forecasting renewable energy solar irradiance forecasting Brain Global warming Learning systems Long short-term memory Mean square error Natural resources Solar energy Solar power generation Solar radiation Deep learning Hybrid model Photovoltaic power Photovoltaic power forecasting Power Power forecasting Renewable energies Renewable energy source Solar irradiance forecasting Solar irradiances Forecasting Jailani N.L.M. Dhanasegaran J.K. Alkawsi G. Alkahtani A.A. Phing C.C. Baashar Y. Capretz L.F. Al-Shetwi A.Q. Tiong S.K. Investigating the Power of LSTM-Based Models in Solar Energy Forecasting
description	Solar is a significant renewable energy source. Solar energy can provide for the world�s energy needs while minimizing global warming from traditional sources. Forecasting the output of renewable energy has a considerable impact on decisions about the operation and management of power systems. It is crucial to accurately forecast the output of renewable energy sources in order to assure grid dependability and sustainability and to reduce the risk and expense of energy markets and systems. Recent advancements in long short-term memory (LSTM) have attracted researchers to the model, and its promising potential is reflected in the method�s richness and the growing number of papers about it. To facilitate further research and development in this area, this paper investigates LSTM models for forecasting solar energy by using time-series data. The paper is divided into two parts: (1) independent LSTM models and (2) hybrid models that incorporate LSTM as another type of technique. The Root mean square error (RMSE) and other error metrics are used as the representative evaluation metrics for comparing the accuracy of the selected methods. According to empirical studies, the two types of models (independent LSTM and hybrid) have distinct advantages and disadvantages depending on the scenario. For instance, LSTM outperforms the other standalone models, but hybrid models generally outperform standalone models despite their longer data training time requirement. The most notable discovery is the better suitability of LSTM as a predictive model to forecast the amount of solar radiation and photovoltaic power compared with other conventional machine learning methods. � 2023 by the authors.
author2	58297401800
author_facet	58297401800 Jailani N.L.M. Dhanasegaran J.K. Alkawsi G. Alkahtani A.A. Phing C.C. Baashar Y. Capretz L.F. Al-Shetwi A.Q. Tiong S.K.
format	Review
author	Jailani N.L.M. Dhanasegaran J.K. Alkawsi G. Alkahtani A.A. Phing C.C. Baashar Y. Capretz L.F. Al-Shetwi A.Q. Tiong S.K.
author_sort	Jailani N.L.M.
title	Investigating the Power of LSTM-Based Models in Solar Energy Forecasting
title_short	Investigating the Power of LSTM-Based Models in Solar Energy Forecasting
title_full	Investigating the Power of LSTM-Based Models in Solar Energy Forecasting
title_fullStr	Investigating the Power of LSTM-Based Models in Solar Energy Forecasting
title_full_unstemmed	Investigating the Power of LSTM-Based Models in Solar Energy Forecasting
title_sort	investigating the power of lstm-based models in solar energy forecasting
publisher	Multidisciplinary Digital Publishing Institute (MDPI)
publishDate	2024
_version_	1814061110664364032
score	13.222552

Investigating the Power of LSTM-Based Models in Solar Energy Forecasting

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