Design and Performance Analyses of Evacuated U-Tube Solar Collector Using Data-Driven Machine Learning Models

Abstract

Abstract In this study, the implementation of data-driven machine learning (ML) models for design analysis and performance prediction of evacuated U-tube solar collectors (ETSCs) is investigated for the first time. Although evacuated U-tube solar collectors are widely investigated both numerically and experimentally, the implementation of data-driven machine learning models as an efficient predictive tool was not explored for the ETSC. So, to fill this literature gap, seven ML models such as linear regression with repeated K-fold cross-validation (LR), K-nearest neighbors (KNNs), principal component analysis (PCA), partial least-square regression-I (PLSR-I), partial least-square regression-II (PLSR-II), support vector regression (SVR), and stochastic gradient descent regression (SGDR) are employed using three hundred experimental data points and are reported in the literature. The heat transfer fluid outlet temperature (Thtf,o), thermal energy gained by heat transfer fluid (Q˙htf), and ETSC efficiency (ɳETSC) are considered as output/performance parameters. The outcome of the predicted results suggests that the SGDR ML model is superior in predicting all the performance parameters showing R2 values of 0.98, 0.981, and 0.99 for “Thtf,o,” “Q˙htf,” and “ɳETSC,” respectively. Moreover, the KNN ML model showed weaker performance for predicting the output parameters. In addition, it is observed that the SGDR ML model has a low training time of 0.45 s when compared to other ML models. For the given operating range, the predicted optimal performance parameters such as “Thtf,o,” “Q˙htf,” and “ɳETSC” obtained from the SGDR ML model are 45 °C, 0.44 kW, and 71%, respectively. Furthermore, the recommendations and shortcomings associated with the ML models for the design and performance optimization of ETSC are also presented in detail.