عنوان	Dynamic modeling of photovoltaic–thermal systems using polynomial regression
نوع پژوهش	مقاله چاپ‌شده در مجلات علمی
کلیدواژه‌ها	Machine-learning, Photovoltaic–thermal system, Polynomial regression, Time-series prediction
چکیده	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 validat 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.
پژوهشگران	کامران مرادی (نفر اول)، فرشته جعفری (نفر دوم)، فریبا مقدم (نفر سوم)، قباد شفیعی (نفر چهارم)