https://orcid.org/0000-0002-7540-7935
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ABSTRACT
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.
Highlights
Investigation of energy, electrical and exergy efficiency values of PV-T solar collector
Modeling of efficiency values of PV-T with computational intelligence method
Derivation of mathematical equations for all efficiency values of PV-T with the help of Elastic.Net algorithm using datasets consisting of environmental parameters only
Average 2% error between the results of the efficiency equations and the experimental values.
Nomenclature Subscripts
| A | = | PV-T surface area (m2) |
| Cp | = | specific heat coefficient (J kg−1 K−1) |
| COP | = | coefficient of performance |
| e | = | exergy of any point in the system () |
| Ėx | = | exergy (W) |
| EIF | = | environmental impact factor |
| ESI | = | environmental sustainably index |
| G | = | solar radiation (W m−2 K−1) |
| I | = | current (A) |
| IP | = | Improvement potential (kW) |
| ṁ | = | mass flow rate (kg s−1) |
| P | = | electrical power (W) |
| Q | = | heat transfer rate (W) |
| T | = | temperature (°C) |
| WER | = | wasted exergy ratio |
| ƞ | = | efficiency |
| a | = | air |
| ds | = | destruction |
| e | = | environment |
| en | = | energy |
| ex | = | exergy |
| elec | = | electrical |
| f | = | fan |
| i | = | inlet |
| max | = | maximum |
| PV | = | photovoltaic |
| o | = | outlet |
| th | = | thermal |
| u | = | useful |
Disclosure statement
No potential conflict of interest was reported by the authors.