Dust Impact Assessment on Micro Solar Voltage: A Deep Learning-Enabled Prediction Approach

Solar energy, a crucial component of global renewable energy solutions, is significantly influenced by environmental factors, specifically, dust deposition on surfaces used in photovoltaic systems to collect sunlight. By understanding the effect of dust buildup on the operation of solar panels, we aim to optimize renewable energy generation. We utilized machine learning techniques to predict solar panel output voltage based on dust distribution data. Employed is a Convolutional Neural Network (CNN) with operations such as convolution, max pooling, and which uses non-linear activation to extract features and make predictions. The model achieved low RMSE and MAE values on unseen test data, explaining around 41.66% of the variance with an R2 score of 0.4166 demonstrating that the proposed system effectively predicts solar panel output voltage.