Abstract

AbstractDue to the shortage of energy in the world, solar energy has received widespread attention as an inexhaustible new green energy and as one of the main sources of power. Many researchers have studied the various materials and efficiencies of solar cells; however, how to extend the life of solar cells has rarely been studied. At present, the main encapsulating method of solar cells is to seal their surface with films such as ethylene-vinyl acetate and polyvinyl butyral. The main problem that has been encountered is that the erosion of water and oxygen leads to a reduction in the service life and efficiency of solar cells. Inspired by the solar panels of satellites in space, a revolutionary vacuum-glazing encapsulating solution with zero H2O and O2 has been invented. The experimental results have nearly doubled the 30–35-year service life of solar cells, based on deep learning predictions. Therefore, the building integrated photovoltaic can be used for the 70-year life of a building. The method is applicable to various solar cells, such as crystalline Si cells, CIGS, CdTe and perovskite film cells, etc. In practice, the main problems encountered in the encapsulation of vacuum glazing include the following: ensuring that the supporting pillar does not pierce the thin film PV and that it is placed accurately between the band gaps; ensuring that the emission of heat is not conducted in a vacuum; ensuring that the sealing sheet covers cover the exhausting port on the glass accurately; maintaining the vacuum degree for a long time; insulating the edge of the sealing materials, as well as other issues. The above problems have been solved perfectly through machine learning of computer vision and the design structure of the thin film PV.

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