Electrical efficiency of the photovoltaic/thermal collectors cooled by nanofluids: Machine learning simulation and optimization by evolutionary algorithm

• Published in: Energy reports Vol. 8; pp. 24 - 36
• Main Authors: Cao, Yan, Kamrani, Elham, Mirzaei, Saeid, Khandakar, Amith, Vaferi, Behzad
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2022; Elsevier
• Subjects: Electrical efficiency enhancement; Machine learning; Nanofluids; Photovoltaic/thermal collector; Electrical efficiency enhancement; Photovoltaic/thermal collector; Nanofluids; Machine learning
• ISSN: 2352-4847; 2352-4847
• DOI: 10.1016/j.egyr.2021.11.252

Photovoltaic/thermal (PV/T) are high-tech devices to transform solar radiation into electrical and thermal energies. Nano-coolants are recently considered to enhance the efficiency of PV/T systems. There is no accurate model to predict/optimize the PV/T systems’ electrical efficiency cooled by nano-coolants. Therefore, this research employs machine-learning approaches to simulate PV/T system electrical performance cooled by water-based nanofluids. The best topology of artificial neural networks, least-squares support vector regression, and adaptive neuro-fuzzy inference systems (ANFIS) are found by trial-and-error and statistical analyses. The ANFIS is found as the best method for simulation of the electrical performance of the considered solar system. This approach predicted 200 experimental datasets with the absolute average relative deviation (AARD) of 13.6%, mean squared error (MSE) of 2.548, and R2=0.9534. Furthermore, the ANFIS model predicts a new external database containing 63 samples with the AARD=15.21%. The optimization stage confirms that 30 lit/hr of water-silica nano-coolant (3wt%, 12.5 nm) at radiation intensity of 788.285 W/m2 is the condition that maximizes electrical efficiency. In this optimum condition, the enhancement in the PV/T electrical efficiency is 27.7%. Finally, the fabricated ANFIS model has been utilized for generating several pure simulation predictions that have never been published before. •The electrical efficiency of PV cells cooled by nanofluids is modeled and optimized.•Effect of nanofluids properties, coolant flow rate, and solar radiation is studied.•ANFIS is the best intelligent model in terms of four statistical indices.•Differential evolution algorithm maximizes the electrical efficiency of PV systems.•The maximum electrical efficiency of 27.7% is achieved at the optimum point.