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Neural network approach for global solar irradiance prediction at extremely short-time-intervals using particle swarm optimization algorithm

Forecasting; Multilayer neural networks; Particle swarm optimization (PSO); Solar radiation; Back propagation neural networks; Classical back-propagation; Environmental conditions; Global solar irradiances; Level of predictabilities; Optimization algorithms; Particle swarm algorithm; Particle swarm...

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Main Authors: Aljanad A., Tan N.M.L., Agelidis V.G., Shareef H.
Other Authors: 56119134000
Format: Article
Published: MDPI AG 2023
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spelling my.uniten.dspace-263312023-05-29T17:09:12Z Neural network approach for global solar irradiance prediction at extremely short-time-intervals using particle swarm optimization algorithm Aljanad A. Tan N.M.L. Agelidis V.G. Shareef H. 56119134000 24537965000 7003492499 57189691198 Forecasting; Multilayer neural networks; Particle swarm optimization (PSO); Solar radiation; Back propagation neural networks; Classical back-propagation; Environmental conditions; Global solar irradiances; Level of predictabilities; Optimization algorithms; Particle swarm algorithm; Particle swarm optimization algorithm; Backpropagation Hourly global solar irradiance (GSR) data are required for sizing, planning, and modeling of solar photovoltaic farms. However, operating and controlling such farms exposed to varying environmental conditions, such as fast passing clouds, necessitates GSR data to be available for very short time intervals. Classical backpropagation neural networks do not perform satisfactorily when predicting parameters within short intervals. This paper proposes a hybrid backpropagation neural networks based on particle swarm optimization. The particle swarm algorithm is used as an optimization algorithm within the backpropagation neural networks to optimize the number of hidden layers and neurons used and its learning rate. The proposed model can be used as a reliable model in predicting changes in the solar irradiance during short time interval in tropical regions such as Malaysia and other regions. Actual global solar irradiance data of 5-s and 1-min intervals, recorded by weather stations, are applied to train and test the proposed algorithm. Moreover, to ensure the adaptability and robustness of the proposed technique, two different cases are evaluated using 1-day and 3-days profiles, for two different time intervals of 1-min and 5-s each. A set of statistical error indices have been introduced to evaluate the performance of the proposed algorithm. From the results obtained, the 3-days profile's performance evaluation of the BPNN-PSO are 1.7078 of RMSE, 0.7537 of MAE, 0.0292 of MSE, and 31.4348 of MAPE (%), at 5-s time interval, where the obtained results of 1-min interval are 0.6566 of RMSE, 0.2754 of MAE, 0.0043 of MSE, and 1.4732 of MAPE (%). The results revealed that proposed model outperformed the standalone backpropagation neural networks method in predicting global solar irradiance values for extremely short-time intervals. In addition to that, the proposed model exhibited high level of predictability compared to other existing models. � 2021 MDPI AG. All rights reserved. Final 2023-05-29T09:09:12Z 2023-05-29T09:09:12Z 2021 Article 10.3390/en14041213 2-s2.0-85102196186 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102196186&doi=10.3390%2fen14041213&partnerID=40&md5=17da0894cccb3a46845f009e3a44486f https://irepository.uniten.edu.my/handle/123456789/26331 14 4 1213 All Open Access, Gold, Green MDPI AG 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/
description Forecasting; Multilayer neural networks; Particle swarm optimization (PSO); Solar radiation; Back propagation neural networks; Classical back-propagation; Environmental conditions; Global solar irradiances; Level of predictabilities; Optimization algorithms; Particle swarm algorithm; Particle swarm optimization algorithm; Backpropagation
author2 56119134000
author_facet 56119134000
Aljanad A.
Tan N.M.L.
Agelidis V.G.
Shareef H.
format Article
author Aljanad A.
Tan N.M.L.
Agelidis V.G.
Shareef H.
spellingShingle Aljanad A.
Tan N.M.L.
Agelidis V.G.
Shareef H.
Neural network approach for global solar irradiance prediction at extremely short-time-intervals using particle swarm optimization algorithm
author_sort Aljanad A.
title Neural network approach for global solar irradiance prediction at extremely short-time-intervals using particle swarm optimization algorithm
title_short Neural network approach for global solar irradiance prediction at extremely short-time-intervals using particle swarm optimization algorithm
title_full Neural network approach for global solar irradiance prediction at extremely short-time-intervals using particle swarm optimization algorithm
title_fullStr Neural network approach for global solar irradiance prediction at extremely short-time-intervals using particle swarm optimization algorithm
title_full_unstemmed Neural network approach for global solar irradiance prediction at extremely short-time-intervals using particle swarm optimization algorithm
title_sort neural network approach for global solar irradiance prediction at extremely short-time-intervals using particle swarm optimization algorithm
publisher MDPI AG
publishDate 2023
_version_ 1806424177539809280
score 13.211869

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