研究目的
Developing more efficient systems and utilizing other energy resources are taking more significance since the amount of available fossil fuel resources are facing a decreasing slope. There are several renewable energy sources that can be exploited to satisfy the energy sector demands. However, solar energy is considering more attention since it is available almost everywhere, and also it is regarded as clean energy with no harmful effect on the environment.
研究成果
Machine learning methods of MLP-ANN, RBF-ANN, ANFIS, and LSSVM are utilized to establish a mathematical framework for improving the efficiency of PV/T collector through modeling the input parameters of inlet temperature, flow rate, heat, solar radiation, and sun heat. The proposed LSSVM model outperfoms other models. Based on the sensitivity analysis, the water inlet temperature has the most effect on the efficiency of the PV/T system since it has the most significant relevancy factor.
研究不足
The calculation of the thermal efficiency by conventional solution methods results in solving complicated mathematical differential equations that are time consuming. The use of machine learning methods is considered to provide accurate prediction of the studied process by saving time and cost compared to laboratory methods.
1：Experimental Design and Method Selection
Machine learning methods of MLP-ANN, RBF-ANN, ANFIS, and LSSVM were developed in Matlab 2018 software to model the efficiency of the PV/T system. Inlet temperature, flow rate, heat, solar radiation, and heat of the sun were considered as input variables.
2：Sample Selection and Data Sources
An overall number of 98 data points were utilized in the models above to forecast the desired objective. The data classified into two subclasses of train and test, which 75% of the data considered as training and the remaining belong to the test subclass.
3：List of Experimental Equipment and Materials
PV panel with 36 cells, aperture area and nominal efficiency of the panel (under standard condition) are 0.7 m2 and 12.5%, respectively. Also it has an open circuit voltage (Voc) of 22.2 V and short circuit current (Isc) of 5.5A. Water is circulated with a pump and it’s flow rate is controlled with a manual ball valve and measured by a rotameter in range of 0.5–4 liters per minute. Inlet and outlet temperature are measured with a K-type thermocouple (with accuracy of ± 0.1°C).
4：Experimental Procedures and Operational Workflow
The system was tested on sunny summer days almost in the noon to have the constant and maximum amount of solar irradiations. The variations of solar irradiance during the tests on different days are illustrated.
5：Data Analysis Methods
Different statistical criteria such as R-squared, Root Mean Squared Error (RMSE) and etc. are applicable to evaluate the confidence, reliability and accuracy of the models.
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