Modelling Temperature-Induced Performance Losses in Rooftop Solar Panels Using Machine Learning
DOI:
https://doi.org/10.11113/ijic.v16n1-2.695Keywords:
PV Performance, Temperature Dependences, Machine Learning, Linear Regression, Energy LossAbstract
This study investigates the critical impact of temperature in the performance of rooftop solar photovoltaic (PV) systems. Particularly focusing on energy generation efficiency losses when temperatures exceed 28°C. High temperatures increase the internal resistance of PV cells, reducing their ability to convert sunlight into electricity, the research identifies an optimal operating temperature range for solar panels between 24°C and 26°C, where efficiency is maximized. To predict these temperature-dependent performance losses, machine learning models, are employed. A comparation between Linear regression in capturing the complex, non-linear relationships between temperature, irradiance, and energy output. While Linear Regression showed a perfect R2 score of 1.0 indicative of overfitting. The Random Forest model achieved a robust R2 of 0.8987, demonstrating superior generalizability and accuracy of real- world applications. This research validates the effectiveness of machine learning techniques for more reliable solar energy forecasting and optimization and highlights the necessity of considering thermal management in solar system design, especially in hot climates.
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