Dynamic modeling of photovoltaic–thermal systems using polynomial regression

Kamran Moradi, Fereshteh Jafari, Fariba Moghaddam and Qobad Shafiee

Renewable Energy, 2025, vol. 250, issue C

Abstract: This paper presents an autoregressive moving-average dynamic model approach for photovoltaic–thermal (PVT) systems using a machine-learning (ML) technique based on polynomial regression. The main contribution is the development and application of a global dynamic modeling method designed for accurate time-series prediction of the electrical and thermal power outputs of a PVT system. The performance of the proposed method is compared with traditional analytical and another ML-based method known as long short-term memory (LSTM) models to demonstrate its effectiveness and simplicity. The research includes two main parts: implementing the proposed polynomial regression-based modeling method in MATLAB and applying it to a real-world PVT system in Granges, Switzerland. A thorough frequency-domain analysis is performed to validate the model’s accuracy and reliability. The results show that the polynomial regression-based dynamic model offers clear advantages for system analysis, providing a more user-friendly alternative to the complex and often cumbersome analytical methods. Compared to analytical models, ML-based methods achieve more accurate results with lower modeling complexity and greater practical applicability. As a result, the proposed method streamlines the modeling process while improving the efficiency and accuracy of PVT system performance prediction and optimization.

Keywords: Dynamic modeling; Machine-learning; Photovoltaic–thermal system; Polynomial regression; Time-series prediction (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:250:y:2025:i:c:s0960148125009048

DOI: 10.1016/j.renene.2025.123242

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