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Performance optimization of the parabolic trough power plant using a dual-stage ensemble algorithm

The flexibility of future power production systems must be maximized in order to offset the unpredictability of non-dispatchable energy from renewable sources. The absence of state policies and strategies to encourage investment in exploiting solar energy is why it is not widely used in Africa. In l...

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Main Authors:	Elfeky K.E., Hosny M., Mohammed A.G., Chu W., Khatwa S.A., Wang Q.
Other Authors:	56979298200
Format:	Article
Published:	Elsevier Ltd 2025
Subjects:	Adaptive boosting Electric load dispatching Investments Learning systems Machine learning Meteorology Neural networks Power generation Solar energy Ensemble learning Levelized cost of electricities Levelized cost of electricity meteorological data Machine learning models Machine-learning Meteorological data Parabolic trough Parabolic trough plant Parabolic trough power plants Performance optimizations Nameplates
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id	my.uniten.dspace-36491
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spelling	my.uniten.dspace-364912025-03-03T15:42:41Z Performance optimization of the parabolic trough power plant using a dual-stage ensemble algorithm Elfeky K.E. Hosny M. Mohammed A.G. Chu W. Khatwa S.A. Wang Q. 56979298200 57192874374 57219281767 50261332200 12141122800 55521034600 Adaptive boosting Electric load dispatching Investments Learning systems Machine learning Meteorology Neural networks Power generation Solar energy Ensemble learning Levelized cost of electricities Levelized cost of electricity meteorological data Machine learning models Machine-learning Meteorological data Parabolic trough Parabolic trough plant Parabolic trough power plants Performance optimizations Nameplates The flexibility of future power production systems must be maximized in order to offset the unpredictability of non-dispatchable energy from renewable sources. The absence of state policies and strategies to encourage investment in exploiting solar energy is why it is not widely used in Africa. In light of this, this research first aims to carry out a techno-economic assessment of parabolic trough power (PTP) plants in six cities in Egypt. For this purpose, a 100 MW nameplate capability has been simulated using the system advisor model simulation environment. Finally, four machine learning models are proposed, including artificial neural network, Gaussian process regression, regression neural network, and least square boosting in conjunction with a generalized additive model (GAM) as a meta-model, in order to develop a generalized model to predict the PTP performance based on the data of ten different cities at Africa. Utilizing these longstanding machine learning models for feature extraction, EnsGAM is tailored to the optimal predictors The findings indicate that, with a capacity factor of 55.4 % and an annual energy output of 484.7 GWh, the Benban location produces the most energy. In addition, Benban exhibits the shortest simple payback period?10.1 years?while Kuraymat displays the longest?11.5 years. The findings showed that EnsGAM performs noticeably better than all comparison techniques, producing the highest correlation coefficients/Willmott's agreement index for power generation and maximum discharge energy of 0.9463/0.9724 and 0.958/0.9778, respectively. ? 2024 Elsevier Ltd Final 2025-03-03T07:42:41Z 2025-03-03T07:42:41Z 2024 Article 10.1016/j.applthermaleng.2024.123419 2-s2.0-85193449446 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193449446&doi=10.1016%2fj.applthermaleng.2024.123419&partnerID=40&md5=1f3b26cb99f5fa858c0e956ee089fe72 https://irepository.uniten.edu.my/handle/123456789/36491 249 123419 Elsevier Ltd 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	Adaptive boosting Electric load dispatching Investments Learning systems Machine learning Meteorology Neural networks Power generation Solar energy Ensemble learning Levelized cost of electricities Levelized cost of electricity meteorological data Machine learning models Machine-learning Meteorological data Parabolic trough Parabolic trough plant Parabolic trough power plants Performance optimizations Nameplates
spellingShingle	Adaptive boosting Electric load dispatching Investments Learning systems Machine learning Meteorology Neural networks Power generation Solar energy Ensemble learning Levelized cost of electricities Levelized cost of electricity meteorological data Machine learning models Machine-learning Meteorological data Parabolic trough Parabolic trough plant Parabolic trough power plants Performance optimizations Nameplates Elfeky K.E. Hosny M. Mohammed A.G. Chu W. Khatwa S.A. Wang Q. Performance optimization of the parabolic trough power plant using a dual-stage ensemble algorithm
description	The flexibility of future power production systems must be maximized in order to offset the unpredictability of non-dispatchable energy from renewable sources. The absence of state policies and strategies to encourage investment in exploiting solar energy is why it is not widely used in Africa. In light of this, this research first aims to carry out a techno-economic assessment of parabolic trough power (PTP) plants in six cities in Egypt. For this purpose, a 100 MW nameplate capability has been simulated using the system advisor model simulation environment. Finally, four machine learning models are proposed, including artificial neural network, Gaussian process regression, regression neural network, and least square boosting in conjunction with a generalized additive model (GAM) as a meta-model, in order to develop a generalized model to predict the PTP performance based on the data of ten different cities at Africa. Utilizing these longstanding machine learning models for feature extraction, EnsGAM is tailored to the optimal predictors The findings indicate that, with a capacity factor of 55.4 % and an annual energy output of 484.7 GWh, the Benban location produces the most energy. In addition, Benban exhibits the shortest simple payback period?10.1 years?while Kuraymat displays the longest?11.5 years. The findings showed that EnsGAM performs noticeably better than all comparison techniques, producing the highest correlation coefficients/Willmott's agreement index for power generation and maximum discharge energy of 0.9463/0.9724 and 0.958/0.9778, respectively. ? 2024 Elsevier Ltd
author2	56979298200
author_facet	56979298200 Elfeky K.E. Hosny M. Mohammed A.G. Chu W. Khatwa S.A. Wang Q.
format	Article
author	Elfeky K.E. Hosny M. Mohammed A.G. Chu W. Khatwa S.A. Wang Q.
author_sort	Elfeky K.E.
title	Performance optimization of the parabolic trough power plant using a dual-stage ensemble algorithm
title_short	Performance optimization of the parabolic trough power plant using a dual-stage ensemble algorithm
title_full	Performance optimization of the parabolic trough power plant using a dual-stage ensemble algorithm
title_fullStr	Performance optimization of the parabolic trough power plant using a dual-stage ensemble algorithm
title_full_unstemmed	Performance optimization of the parabolic trough power plant using a dual-stage ensemble algorithm
title_sort	performance optimization of the parabolic trough power plant using a dual-stage ensemble algorithm
publisher	Elsevier Ltd
publishDate	2025
_version_	1825816184320163840
score	13.244413

Performance optimization of the parabolic trough power plant using a dual-stage ensemble algorithm

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