Predicting green hydrogen production using electrolyzers driven by photovoltaic panels and wind turbines based on machine learning techniques: A pathway to on-site hydrogen refuelling stations

International Journal of Hydrogen Energy, Journal Year: 2025, Volume and Issue: 101, P. 1421 - 1438

Published: Jan. 8, 2025

Language: Английский

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Thermal models for mono/bifacial modules in ground/floating photovoltaic systems: A review

Renewable and Sustainable Energy Reviews, Journal Year: 2025, Volume and Issue: 216, P. 115627 - 115627

Published: March 30, 2025

Language: Английский

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Experimental and numerical modeling of photovoltaic modules temperature under varying ambient conditions

Energy Conversion and Management, Journal Year: 2024, Volume and Issue: 312, P. 118563 - 118563

Published: May 18, 2024

Language: Английский

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Experimentally validated thermal modeling for temperature prediction of photovoltaic modules under variable environmental conditions

Renewable Energy, Journal Year: 2024, Volume and Issue: 231, P. 120922 - 120922

Published: July 4, 2024

Language: Английский

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Temperature-dependent performance prediction for cerium oxynitride solid-state symmetric supercapacitor using machine learning

Journal of Energy Storage, Journal Year: 2025, Volume and Issue: 113, P. 115562 - 115562

Published: Jan. 29, 2025

Language: Английский

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Multi-step short-term forecasting of photovoltaic power utilizing TimesNet with enhanced feature extraction and a novel loss function

Applied Energy, Journal Year: 2025, Volume and Issue: 388, P. 125645 - 125645

Published: March 10, 2025

Language: Английский

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A CFD Approach to Thermal Analysis of Soiled Fixed Roof Mount and Tracking Solar Photovoltaic Arrays

International Journal of Energy Research, Journal Year: 2025, Volume and Issue: 2025(1)

Published: Jan. 1, 2025

The efficiency of solar photovoltaic (PV) energy conversion is significantly impacted by temperature, and soiling remains a critical factor influencing module performance. Alternative solutions, including cleaning, antisoiling coatings, the use tracking systems, implementation thermal mitigation strategies, have been explored to minimize effects impacts on cell This study approached problem from different perspective employing three‐dimensional (3D) computational fluid dynamics (CFD) model analyze correlation between PV temperature. simulations incorporated varying dust thermophysical properties, installation geometries, environmental conditions using user‐defined functions (UDFs). Key findings revealed strong relationships density, specific heat capacity, conductivity, mediated density. Maximum temperature rises were observed with low density dust, elevating temperatures up 3.15%. Fixed configurations maintained lower 1.7% compared systems. Dust averaged 1.15% higher than underlying cell, while directly soiled cells exhibited 1.93% increase clean modules. Higher tilt angles experienced enhanced wind turbulence, reducing temperatures, whereas collectors oriented prevailing winds showed minimal when aligned parallel azimuth. highlighted dual role conductivity in transfer, where values acted as insulators, high facilitated efficient dissipation. Soiling‐induced contributed maximum 12% reduction, emphasizing importance mitigating these effects. Tracking although susceptible demonstrated potential reduce improve overall efficiency. These provide actionable insights for optimizing performance under diverse operational conditions.

Language: Английский

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Prediction of Photovoltaic Panel Cell Temperatures: Application of Empirical and Machine Learning Models

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135764 - 135764

Published: March 1, 2025

Language: Английский

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Effect of electrical operating conditions on thermal behavior of PV modules: Numerical and experimental analysis

Solar Energy Materials and Solar Cells, Journal Year: 2025, Volume and Issue: 287, P. 113625 - 113625

Published: April 8, 2025

Language: Английский

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Optimizing Bifacial Solar Modules with Trackers: Advanced Temperature Prediction Through Symbolic Regression

Energies, Journal Year: 2025, Volume and Issue: 18(8), P. 2019 - 2019

Published: April 15, 2025

Accurate temperature prediction in bifacial photovoltaic (PV) modules is critical for optimizing solar energy systems. Conventional models face challenges to balance accuracy, interpretability, and computational efficiency. This study addresses these limitations by introducing a symbolic regression (SR) framework based on genetic algorithms model nonlinear relationships between environmental variables module without predefined structures. High-resolution data, including radiation, ambient temperature, wind speed, PV were collected at 5 min intervals over year from 19.9 MW plant with trackers San Marcos, Colombia. The SR performance was compared multiple linear regression, normal operating cell (NOCT), empirical models. outperformed others achieving root mean squared error (RMSE) of 4.05 °C, coefficient determination (R2) 0.91, Spearman’s rank correlation 0.95, absolute (MAE) 2.25 °C. Its hybrid structure combines dependencies trigonometric terms capturing radiation dynamics. effectively balances accuracy providing information modeling

Language: Английский

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