Predicting Photovoltaic-Thermal Panel Output in Urban Contexts Using Machine Learning Methods DOI Creative Commons

Alireza Nazeri,

Ali Taheri, Zahra Sadat Zomorodian

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 12, 2024

Abstract In recent years, the use of data-driven methods for predicting photovoltaic (PV) panel electricity generation has grown significantly, with most studies relying on databases actual PV performance. This study introduces a comprehensive methodology performance photovoltaic-thermal (PVT) panels, specifically focusing generation, hot water production, and carbon reduction. By leveraging artificial intelligence (AI) machine learning (ML) methods, particularly Artificial Neural Networks (ANN) Random Forest (RF), this research differentiates itself from prior by integrating predictive models both electrical thermal outputs. Additionally, examines effect different installation patterns PVT output. A total 1,575 configurations were modeled across three urban districts in Tehran, results used to train two ML algorithms, which then compared using Pearson correlation coefficient (R²), Root-mean-square deviation (RMSE), Mean Absolute Error (MAE) metrics. The RF algorithm demonstrated superior performance, achieving an R² accuracy 0.91 shorter time. Finally, framework is proposed based findings simulation steps reduction systems.

Language: Английский

The smart green tide: A bibliometric analysis of AI and renewable energy transition DOI
Da Gao, Jiajie Cai, Kai Wu

et al.

Energy Reports, Journal Year: 2025, Volume and Issue: 13, P. 5290 - 5304

Published: May 5, 2025

Language: Английский

Citations

0
A machine learning-based approach for maximizing system profit in a power system by imbalance price curtailment DOI

Shreya Shree Das,

Priyanka Singh, Jayendra Kumar

et al.

Computers & Electrical Engineering, Journal Year: 2024, Volume and Issue: 121, P. 109874 - 109874

Published: Nov. 20, 2024

Language: Английский

Citations

0
Predicting Photovoltaic-Thermal Panel Output in Urban Contexts Using Machine Learning Methods DOI Creative Commons

Alireza Nazeri,

Ali Taheri, Zahra Sadat Zomorodian

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 12, 2024

Abstract In recent years, the use of data-driven methods for predicting photovoltaic (PV) panel electricity generation has grown significantly, with most studies relying on databases actual PV performance. This study introduces a comprehensive methodology performance photovoltaic-thermal (PVT) panels, specifically focusing generation, hot water production, and carbon reduction. By leveraging artificial intelligence (AI) machine learning (ML) methods, particularly Artificial Neural Networks (ANN) Random Forest (RF), this research differentiates itself from prior by integrating predictive models both electrical thermal outputs. Additionally, examines effect different installation patterns PVT output. A total 1,575 configurations were modeled across three urban districts in Tehran, results used to train two ML algorithms, which then compared using Pearson correlation coefficient (R²), Root-mean-square deviation (RMSE), Mean Absolute Error (MAE) metrics. The RF algorithm demonstrated superior performance, achieving an R² accuracy 0.91 shorter time. Finally, framework is proposed based findings simulation steps reduction systems.

Language: Английский

Citations

0