Solar technologies like flat plate solar collectors are being widely used for low-grade thermal energy for household purposes. These days, photovoltaic thermal (PV/T) collectors are also gaining momentum as source of combined heat and electric power. Commonly used base fluid in PV/T collector is water which have low thermal conductance, and thus, addition of nanoparticles in base fluid will lead to the enhancement in overall thermal conductance. Keeping this as main focus, a research has been carried out to evaluate the performance of PV/T system with different nanoparticles. For that, the simulation was carried out by performing grid test and then simulated on ANSYS to obtain results. For the same, nanofluids with 20 nm particle dimensions and 299 K inlet temperature were loaded with 0.5, 1 and 1.5% particle volume fraction with different Reynolds numbers varying from 250 to 1500. The simulated model was validated with the literature, and obtained results showed that the heat transfer coefficient (HTC) without any nanoparticles ranges from 245.5 to 519.8 W/m 2 K for Reynolds number of 250-1500, respectively. On other hand, with nanoparticles, the HTC increases and ranges between 250.6-529.20 W/m 2 K, 255.42-539.8 W/m 2 K and 261.1-550.8 W/m 2 K for 0.5%, 1.0% and 1.5% volume fraction, respectively, for Reynolds number of 250-1500. In the end, it is concluded that the simulation results are in good agreement with the literature.

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This study aims to estimate the performance of photovoltaic/thermal (PV/T) collector using alumina water‐based nanofluid with geothermal cooling through machine learning (ML) approach. A mathematical model is developed for the first law of thermodynamic analysis of nanofluid in PV/T system integrated with geothermal cooling and is validated with experimental results. Further, a machine learning‐based approach has been employed to simulate the cooling performance of a nanofluid cooling based PV/T system. In the study, Multi‐layer perceptron (MLP) is proposed for estimating the thermal and electrical performance of PV/T system based on design parameters like nanofluid concentration, Reynolds number, and time. The same is then compared with other state‐of‐the‐art machine learning techniques and it is evaluated based on various quality metrics such as mean square error (MSE), root mean square error (RMSE), and R2 test. The designed network is compared with the other ML algorithms available in literature like linear regression (LR), support vector machine (SVM) and decision tree (DT). The proposed MLP network is provided a significant outcome with an average accuracy of 98% and predicted PV panel temperature of 32.1–36.5°C for 0–18 sequences. It was also observed that electrical efficiency of PV/T system improved from 10.51% to 10.66% for 0–18 sequences through MLP predictions.

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