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.