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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Energy (Oxford) Ročník 319; s. 135099
Hlavní autoři: Gawusu, Sidique, Zhang, Xiaobing, Yakubu, Sufyan, Debrah, Seth Kofi, Das, Oisik, Bundela, Nishant Singh
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 15.03.2025
Témata:
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
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0360-5442
1873-6785
DOI:10.1016/j.energy.2025.135099