Prediction and evaluation of energy and exergy efficiencies of a nanofluid-based photovoltaic-thermal system with a needle finned serpentine channel using random forest machine learning approach.

• A PV/T device with sheet-and-needle finned serpentine tube collector is analyzed. • PV panels are cooled using the water-silver nanofluid. • Random forest machine learning approach is used to develop predictive models. • Thermal, electrical and overall efficiencies are utilized as objective functions. • Utilization of needle fins improve the energy and exergy performance of the system. The Photovoltaic thermal (PVT) collector performance is numerically investigated considering the effect of using needle fins in the serpentine channel with Nanofluid (NF). The influence of increasing the nanoparticle concentration (φ) and Reynolds number (Re) on the energy and exergy features of the PVT device is examined. A comparison is made between the hydrothermal characteristics of the PVT with the finned and plain serpentine channels. The utilization of needle fins improves the thermal efficiency (η th), electrical efficiency (η el), and overall efficiency (η el) by 8.56–10.22%, 0.13–0.24%, 5.12–5.67%, respectively, against the PVT with the plain serpentine channel. Moreover, thermal exergy efficiency (ξ th), electrical exergy efficiency (ξ el), and overall exergy efficiency (ξ ov) by 8.56–1.22%, 0.13–0.24%, and 2.61–2.79%, respectively, versus the PVT with the plain serpentine channel. Moreover, the Random Forest (RF) machine learning approach is used to develop a predictive model for η th , η el , η el , ξ th , ξ el and ξ ov in terms of Re and φ. The outcomes of modeling proved that all the results were in an acceptable level of accuracy and the overall efficiency in both energy and exergy yielded superior precision in comparison with the other targets. [ABSTRACT FROM AUTHOR]