Optimizing solar photovoltaic system performance: Insights and strategies for enhanced efficiency

This study analyzes the performance and predictive modeling of solar photovoltaic (PV) systems at the Bui Generating Station in Ghana using the XGBoost (Extreme Gradient Boosting) algorithm. The predictive model, validated through Monte Carlo simulations, demonstrates measured stability across pertu...

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Vydané v:	Energy (Oxford) Ročník 319; s. 135099
Hlavní autori:	Gawusu, Sidique, Zhang, Xiaobing, Yakubu, Sufyan, Debrah, Seth Kofi, Das, Oisik, Bundela, Nishant Singh
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 15.03.2025
Predmet:	algorithms automation Byggkonstruktion energy Energy policy Ghana light intensity Monte Carlo simulations Predictive modeling relative humidity Renewable energy optimization solar collectors Solar photovoltaic systems Structural Engineering temperature XGBoost algorithm Ghana Renewable energy optimization XGBoost algorithm Predictive modeling Energy policy Monte Carlo simulations Solar photovoltaic systems
ISSN:	0360-5442, 1873-6785
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Abstract	This study analyzes the performance and predictive modeling of solar photovoltaic (PV) systems at the Bui Generating Station in Ghana using the XGBoost (Extreme Gradient Boosting) algorithm. The predictive model, validated through Monte Carlo simulations, demonstrates measured stability across perturbation scenarios. Distribution analysis confirms appropriate parameter bounds, while error analysis demonstrates consistent pattern preservation across simulation scenarios. The study quantifies the relative influences of environmental factors, particularly the interplay between temperature, irradiance, and humidity (correlations ranging from −0.33 to 0.36). These findings provide insights for system operation while acknowledging the complex, often weak coupling between environmental parameters. Seasonal performance analysis reveals distinct optimization windows, with the Post-Rainy season showing the highest stability (PR: 0.986 ± 0.082) and optimal enhancement potential. Sensitivity analysis identifies critical operational thresholds, including performance transitions at 80 % relative humidity and optimal temperature ranges below 32 °C, where each 1 °C reduction yields 0.45 % efficiency gain. The study establishes specific optimization strategies including automated cleaning systems triggered at 85 % peak irradiance, yielding 2.5 % efficiency improvement, and enhanced inverter response protocols during peak generation periods, achieving a 3.2 % performance gain. These findings inform practical implementation frameworks for performance optimization, contributing to improved energy generation efficiency and system reliability. •Solar PV energy predictions validated using Monte Carlo simulations.•Optimized strategies include automated cleaning systems triggered at 85 % peak irradiance.•Efficiency improvements of 2.5 %, with enhanced inverter response during peak generation.•Achieved an overall performance gain of 3.2 %.•Results support energy efficiency, reliability, and sustainability in Ghana.
AbstractList	This study analyzes the performance and predictive modeling of solar photovoltaic (PV) systems at the Bui Generating Station in Ghana using the XGBoost (Extreme Gradient Boosting) algorithm. The predictive model, validated through Monte Carlo simulations, demonstrates measured stability across perturbation scenarios. Distribution analysis confirms appropriate parameter bounds, while error analysis demonstrates consistent pattern preservation across simulation scenarios. The study quantifies the relative influences of environmental factors, particularly the interplay between temperature, irradiance, and humidity (correlations ranging from −0.33 to 0.36). These findings provide insights for system operation while acknowledging the complex, often weak coupling between environmental parameters. Seasonal performance analysis reveals distinct optimization windows, with the Post-Rainy season showing the highest stability (PR: 0.986 ± 0.082) and optimal enhancement potential. Sensitivity analysis identifies critical operational thresholds, including performance transitions at 80 % relative humidity and optimal temperature ranges below 32 °C, where each 1 °C reduction yields 0.45 % efficiency gain. The study establishes specific optimization strategies including automated cleaning systems triggered at 85 % peak irradiance, yielding 2.5 % efficiency improvement, and enhanced inverter response protocols during peak generation periods, achieving a 3.2 % performance gain. These findings inform practical implementation frameworks for performance optimization, contributing to improved energy generation efficiency and system reliability. This study analyzes the performance and predictive modeling of solar photovoltaic (PV) systems at the Bui Generating Station in Ghana using the XGBoost (Extreme Gradient Boosting) algorithm. The predictive model, validated through Monte Carlo simulations, demonstrates measured stability across perturbation scenarios. Distribution analysis confirms appropriate parameter bounds, while error analysis demonstrates consistent pattern preservation across simulation scenarios. The study quantifies the relative influences of environmental factors, particularly the interplay between temperature, irradiance, and humidity (correlations ranging from −0.33 to 0.36). These findings provide insights for system operation while acknowledging the complex, often weak coupling between environmental parameters. Seasonal performance analysis reveals distinct optimization windows, with the Post-Rainy season showing the highest stability (PR: 0.986 ± 0.082) and optimal enhancement potential. Sensitivity analysis identifies critical operational thresholds, including performance transitions at 80 % relative humidity and optimal temperature ranges below 32 °C, where each 1 °C reduction yields 0.45 % efficiency gain. The study establishes specific optimization strategies including automated cleaning systems triggered at 85 % peak irradiance, yielding 2.5 % efficiency improvement, and enhanced inverter response protocols during peak generation periods, achieving a 3.2 % performance gain. These findings inform practical implementation frameworks for performance optimization, contributing to improved energy generation efficiency and system reliability. •Solar PV energy predictions validated using Monte Carlo simulations.•Optimized strategies include automated cleaning systems triggered at 85 % peak irradiance.•Efficiency improvements of 2.5 %, with enhanced inverter response during peak generation.•Achieved an overall performance gain of 3.2 %.•Results support energy efficiency, reliability, and sustainability in Ghana.
ArticleNumber	135099
Author	Zhang, Xiaobing Bundela, Nishant Singh Yakubu, Sufyan Debrah, Seth Kofi Gawusu, Sidique Das, Oisik
Author_xml	– sequence: 1 givenname: Sidique orcidid: 0000-0003-1877-1092 surname: Gawusu fullname: Gawusu, Sidique email: gawususidique@gmail.com organization: School of Energy and Power and Engineering, Nanjing University of Science and Technology, Nanjing, China – sequence: 2 givenname: Xiaobing surname: Zhang fullname: Zhang, Xiaobing email: zhangxb680504@163.com organization: School of Energy and Power and Engineering, Nanjing University of Science and Technology, Nanjing, China – sequence: 3 givenname: Sufyan orcidid: 0000-0001-8662-2887 surname: Yakubu fullname: Yakubu, Sufyan organization: Department of Electrical and Electronic Engineering, Bolgatanga Technical University, Bolgatanga, Ghana – sequence: 4 givenname: Seth Kofi surname: Debrah fullname: Debrah, Seth Kofi organization: Department of Nuclear Engineering, School of Nuclear and Allied Sciences, Atomic Energy, University of Ghana, Legon, Accra, Ghana – sequence: 5 givenname: Oisik surname: Das fullname: Das, Oisik organization: Structural and Fire Engineering Division, Department of Civil, Environmental and Natural Resources Engineering, Luleå University of Technology, Luleå, 97187, Sweden – sequence: 6 givenname: Nishant Singh surname: Bundela fullname: Bundela, Nishant Singh organization: Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, 21211, United States
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Cites_doi	10.3390/en16227618 10.1016/j.solener.2018.08.071 10.1016/j.energy.2024.130503 10.1016/j.comnet.2019.01.026 10.1016/j.rser.2018.11.012 10.1016/j.esd.2024.101421 10.1016/j.renene.2012.10.009 10.3390/su151511888 10.1016/j.rser.2018.03.065 10.1016/j.rser.2016.05.077 10.1007/s10668-024-05015-4 10.1016/j.solener.2019.01.037 10.1016/j.est.2022.104018 10.1016/j.jenvman.2024.122463 10.1007/978-3-031-52677-0_2 10.1007/s40708-017-0065-7 10.1016/j.pecs.2004.02.004 10.1016/j.enconman.2015.07.083 10.1016/j.jclepro.2018.10.239 10.1177/0975087819898581 10.1016/j.scitotenv.2022.161329 10.1016/j.rser.2024.114342 10.1016/j.solcom.2024.100071 10.1016/j.ins.2021.05.055 10.1016/j.jclepro.2020.125465 10.3390/app14167074 10.1016/j.eswa.2023.122686 10.3390/su12176915 10.1016/j.jclepro.2019.04.107 10.3390/en15082790 10.1007/s00366-021-01393-9 10.1016/j.applthermaleng.2024.123029 10.7717/peerj-cs.127 10.1016/j.scitotenv.2020.136848 10.3390/app10175975 10.3390/en17040906 10.1155/2024/9411326 10.1016/j.rser.2020.110202 10.1016/j.renene.2022.03.120 10.1016/j.solener.2015.06.017 10.1007/s13369-022-06560-8 10.1016/j.enbuild.2012.08.011 10.1016/j.apenergy.2014.04.016 10.1016/j.enconman.2019.111799 10.1016/j.ijepes.2021.106830 10.3390/rs16040665 10.1016/j.energy.2024.132194
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Snippet	This study analyzes the performance and predictive modeling of solar photovoltaic (PV) systems at the Bui Generating Station in Ghana using the XGBoost...
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SubjectTerms	algorithms automation Byggkonstruktion energy Energy policy Ghana light intensity Monte Carlo simulations Predictive modeling relative humidity Renewable energy optimization solar collectors Solar photovoltaic systems Structural Engineering temperature XGBoost algorithm
Title	Optimizing solar photovoltaic system performance: Insights and strategies for enhanced efficiency
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