Performance Analysis of Shading Effect over PV Module using Deep Learning Techniques

The increasing adoption of solar energy systems necessitates a comprehensive understanding of factors influencing their efficiency. Shading is a critical aspect that can significantly impact the power output of solar modules. In this research, we employ deep learning techniques to quantify the effects of shading on solar module performance. By leveraging convolutional neural networks (CNNs) on dataset of solar module output collected from shaded and unshaded solar modules, we develop a model capable of accurately predicting the percentage of shading in real-time.The methodology involves simulation of solar modules to collect dataset under varying shading conditions. These dataset serves as input to the deep learning model, which is trained to recognize patterns indicative of shading. The trained model demonstrates robust performance in estimating shading percentages, providing a non-intrusive and efficient means of assessing the impact of shading on solar energy production.