Machine learning predictive models for optimal design of photovoltaic/thermal collector with nanofluids based geothermal cooling

Abstract

AbstractThis 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.