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

• DOI: 10.1016/j.enganabound.2023.03.009
• Publication Date: 2023-03-21T05:23:58Z
• Authors: Yuanlei Si; Frantisek Brumercik; Chunsheng Yang; Adam Glowacz; Zhenjun Ma; Patrick Siarry; Maciej Sulowicz; Munish Kumar Gupta; Zhixiong Li
• Keywords: serpentine channel; 0211 other engineering and technologies; photovoltaic thermal; Serpentine channel; Random forest technique; 02 engineering and technology; Nanofluid; [INFO] Computer Science [cs]; Photovoltaic thermal; Needle fin; machine learning; Machine learning; nanofluid; needle fin; random forest technique

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 (ηₜₕ), electrical efficiency (ηₑₗ), and overall efficiency (ηₑₗ) 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 (ξₜₕ), electrical exergy efficiency (ξₑₗ), and overall exergy efficiency (ξₒᵥ) 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 ηₜₕ, ηₑₗ, ηₑₗ, ξₜₕ, ξₑₗ and ξₒᵥ 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.