Assessing the impact of soiling on photovoltaic efficiency using supervised learning techniques

Abstract

The accumulation of dust and other particles on solar panels, known as soiling, is a significant factor that affects their performance, leading to reduced efficiency if not addressed properly. In this study, we propose a new methodology to estimate soiling on solar photovoltaic panels. To address this issue, we utilised data from the University of Jaén and satellite information from NASA. We applied five different machine learning models, including Linear Regression, Random Forest, Decision Tree, Multilayer Perceptron and Long Short-Term memory neural networks to estimate the extent of soiling on the panels. The input data consisted of weather data, as well as operational data of the solar panels. Our results showed that the MLP model had the lowest average error of 0.0003, indicating its effectiveness in estimating the extent of soiling on the panels. This is significantly lower compared to previous proposals in the literature, which had an average error of 0.026. This study demonstrates the effectiveness of using machine learning methods to forecast soiling on photovoltaic panels accurately. The implications of our findings are essential for optimising energy production and improving the efficiency of solar power systems.