TY - JOUR
TI - Prediction of structural mechanical properties of energy-saving materials for solar photovoltaic photo-thermal system based on deep learning
AU - Xue Dijie
AU - Hu Xiaowei
AU - Xu Xinhua
JN - Thermal Science
PY - 2023
VL - 27
IS - 2
SP - 1109
EP - 1116
PT - Article
AB - In order to study the annual operating efficiency of solar  photovoltaic/photo-thermal collectors, this paper proposes a prediction of  structural mechanical properties of energy-saving materials for solar  photovoltaic photo-thermal systems based on deep learning. Based on the test  data of a solar photovoltaic module, the performance of photovoltaic  photo-thermal module is evaluated from the perspectives of the First law of  thermodynamics, the Second law of thermodynamics, power generation  efficiency and heat collection efficiency. The experimental results show  that the working temperature difference increases from 6.8 K to 45.3 K, the  normalized temperature difference increases from to, and the power  generation efficiency decreases from 0.105 to 0.095 by 0.010, the  percentage of change is 9.4%, the heat collection efficiency is reduced from  0.4534 to 0.2120 by 0.2414, and the reduction rate is 53%, compared with the  generation efficiency and heat collection efficiency, the efficiency changes  during the test period are relatively small. In conclusion for  photovoltaic/photo-thermal components, environmental parameters have a  greater impact on the heat collection efficiency.