Improving the Efficiency of Photovoltaic Panels Using Machine Learning Approach

Photovoltaic (PV) solar panels account for a major portion of the smart grid capacity. On other hand, accumulation dust is significant challenge PV-based systems. The results in reduction amount energy produced. Because country’s low wind velocity and rainfall, frequent cleaning necessary either by manual or automated means. Cleaning activities should only be initiated when absolutely essential to reduce maintenance costs increase power output that have been projected affected accumulation. In this paper, we develop deep belief network model detect particles installed as large unit. study takes into various input metrics includes irradiance, temperature level, level on panels. These are used estimation present atmosphere how often can cleaned at regular intervals. simulation conducted test efficacy estimated terms accuracy, precision, recall, F-measure. show proposed achieves higher accuracy rate more than 99% methods.