Optimizing photovoltaic thermal solar systems efficiency through advanced artificial intelligence driven thermal management techniques

• Published in: Applied thermal engineering Vol. 247; p. 123029
• Main Authors: Shaban, Warda M., Kabeel, A.E., El Hadi Attia, Mohammed, Talaat, Fatma M.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15-06-2024
• Subjects: Artificial neural network; Deep neural network; Photovoltaic thermal (PV/T); Solar energy; Deep neural network; Artificial neural network; Photovoltaic thermal (PV/T); Solar energy
• ISSN: 1359-4311
• DOI: 10.1016/j.applthermaleng.2024.123029

•This study aims to experimentally establish and evaluate a solar collector Photovoltaic Thermal (PV/T) bi-fluid system that utilizes dual exchangers for simultaneous cooling with water and air.•A novel approach has been developed to forecast the efficiency of PV/T systems utilizing AI.•Proposed model was based on Optimized Deep Neural Networks (ODNNs). According to the experimental results the proposed system outperforms the different ML algorithms in terms of MAE, MSE, and R2 as it introduces values of 0.0075, 0.00251, and 0.9972.
Photovoltaic Thermal (PV/T) solar systems have the capacity to convert solar radiation into both electrical and thermal energy. Solar cells convert solar radiation into electricity, whereas solar thermal collectors gather excess energy and use it to heat cooling fluids and extract leftover heat from photovoltaic cells. Optimizing the design of the PV/T cooling system is critical for increasing overall system efficiency. This research describes a revolutionary AI approach for determining the ideal design for PV/T cooling systems. The suggested system has three major stages: data collection (DC), data preprocessing (DP), and prediction (PS). In the DC step, the collected data is preprocessed before being input into the suggested prediction model. The PS uses the Optimized Deep Neural Network (ODNN) to forecast water, air, and electrical efficiency. To improve the model's effectiveness, a new met-heuristic method, the Sand Cat Swarm Optimization (SCSO), is used in conjunction with the Deep Neural Network (DNN). The SCSO approach improves the DNN's hyperparameters to maximize prediction accuracy. The results show that ODNN outperforms in terms of Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R2), with values of approximately 0.0075, 0.00251, and 0.9972, respectively, demonstrating the effectiveness of the proposed AI-driven approach in optimizing PV/T cooling system design. In addition to predicting water, air, and electricity efficiency, the proposed AI-driven approach in this study shows the capacity to optimize the operational efficiency of a system. When combining SCSO with DNN, it shows its effectiveness in dynamically adjusting hyperparameters, contributing to the robustness of the model across many scenarios. Furthermore, the study focuses on the optimized PV/T cooling system's possible real-world applications, emphasizing its role in supporting sustainable energy solutions and solving the developing issues of solar energy consumption. The results confirm the suggested AI system's versatility and reliability, positioning it as a promising tool for improving solar energy technologies.