Hybrid photovoltaic/thermal performance prediction based on machine learning algorithms with hyper-parameter tuning

Abstract

A hybrid Photovoltaic/Thermal(PV/T) approach is proposed in this study based on extensive research and a comparative analysis of several hyperparameter tuning methods. The models analyzed are Linear Regression (LR), Random Forest (RF), XGBoost Regression, AdaBoost Regression, Edge Regression, Support Vector Regression (SVR), elastic net, and lasso (L) models. Grid search optimisation approach was used to maximise all of the model's hyperparameters. A detailed analysis is presented as well as the strategies for tweaking the positive and negative hyperparameters. The suggested hybrid PV/T approach is evaluated in two ways. First, the cumulative yield of solar still was obtained. Second, support vector regression, followed by the hyperparameter tuning function was used to provide the maximum accuracy of the PV output. The findings show that RF and SVR achieved the uttermost precision both before and after the use of the hyperparameter tuning approach, with r2 scores of 0.9952, 0.9935, Root Mean Squared Error values of 0.2583 and 0.5087 while utilising grid search optimisation.

Keywords

• Hyperparameter tuning
• solar still
• photovoltaic (PV)
• machine learning
• random forest