Machine Learning Strategy to Achieve Maximum Energy Harvesting and Monitoring Method for Solar Photovoltaic Panel Applications

Abstract

The choice of the optimal orientation of the solar panels is by far one of the most important issues in the practical application of solar installations. The use of phase changing materials (PCMs) is an efficient approach of storing solar thermal energy. Because PCMs are isothermal in nature, they provide better density energy storage and the capacity to function across a wide temperature range. Unfortunately, this feature is very rare on various solar power panels; however, ignoring it can reduce the performance of the panels to unacceptable levels. The fact is that the angle of incidence of rays on the surface greatly affects the reflection coefficient and, consequently, the role of unacceptable solar energy. In this paper, a smart energy harvesting model was proposed. In the case of glass, when the angle of incidence varies vertically from its surface to 30, the reflection coefficient is practically unchanged and slightly less than 5%, i.e., more than 95% of the radiation goes inwards. Furthermore, the reflection increase is noticeable, and the area of the reflected radiation by 60 doubles to almost 10%. At an angle of incidence of 70, it reflects 20% of the radiation, and at 80, 40%. For most other objects, the dependence of the reflection magnitude on the angle of incidence is approximately the same.