Prediction of heat transfer characteristics and energy efficiency of a PVT solar collector with corrugated-tube absorber using artificial neural network and group method data handling models

Abstract

Photovoltaic thermal (PVT) systems offer an attractive prospect to produce thermal and electricity powers when used as the building envelope. The present numerical analysis is performed intending to evaluate the thermal, electrical, and overall efficiencies of a PVT unit with a corrugated serpentine absorber tube filled with the AO/water nanofluid. The influence of Reynolds number () and nanoparticle concentration () on the performance metrics of the system is analyzed. The result indicated that within the range of 0–1%, the increment in from 500 to 2000 diminishes the PV panel temperature by 3.13–3.32%, while pressure drop boosts by 5480.95–5580.06%. The increase in from 0% to 1%, however, declines the PV panel temperature and pumping power by 0.43–0.62% and 1.25–2.97%, respectively. The range of changes in the overall efficiency was 60.38–90.45%, the maximum and minimum of which belong to = 2000&=1% and = 500&=0%, respectively. The results of artificial neural network (ANN) modeling presented an accurate function for estimation of the overall efficiency of the studied PVT unit based on the and with the R-squared coefficient of determination of R = 0.99602.