Obtaining mathematical equations for exergy, electricity and energy efficiency: A machine learning approach

In this study, in which the energy, exergy, and electrical efficiency values of the photovoltaic thermal panel are modeled with different machine learning algorithms, mathematical equations that can calculate the efficiency values have been obtained as an innovative approach. Data sets consisting of environmental parameters (temperature, wind speed, solar radiation, humidity) of the environment in which the experiments were carried out were used in the models. Thus, the effects of environmental parameters on collector efficiency values were observed, and mathematical equations were produced using these parameters with the help of the decision tree algorithm and Pace regression. In addition, environ-economic analyzes of the panels were made and the coefficient of performance values were examined. In the experiments, two data sets were obtained. With one of these data sets, the efficiency values were modeled with machine learning algorithms, and the accuracy of the mathematical equations obtained with the other data set was proven. The mean absolute percentage error values of the energy, exergy and electrical efficiency models created with the decision tree are 8.04%, 1.76%, and 1.43%, respectively. Similarly, Pace model error values are 3.83%, 2.54%, and 2.1%. The high accuracy values of the obtained efficiency equations under different experimental conditions show that these equations can be used under different conditions and in different solar energy systems.