The use of Internet of Things technology and artificial intelligence tools in analyzing the impact of environmental pollution on the performance of photovoltaic panels

Abstract

The impact of pollution on the performance of photovoltaic (PV) panels and the risk of hot spots is an important issue in the context of optimizing renewable energy systems. Dust, leaves, and bird droppings cause uneven illumination of the panel surface, leading to a decrease in efficiency and local overheating of the cells, which can result in permanent damage. The use of Internet of Things (IoT) technology in PV panel monitoring enables continuous tracking of pollution levels and their impact on system performance. Smart sensors located in various places and on the drones provide real-time data on temperature and air pollution levels of various chemical compounds. The collected information is sent to artificial intelligence-based systems, which analyze patterns and identify potential threats, such as the formation of hot spots or a drop in module performance. Authors method, based on a proprietary structure and selection of deep LSTM network parameters, outperforms other specified machine learning methods in terms of relative prediction accuracy for dust. Proposed algorithm also predicts more accurately than other machine learning methods. Thermal cameras combined with AI algorithms can accurately detect temperature anomalies on the surface of the panels and predict future problems. This allows to optimize cleaning schedule and make maintenance decisions based on actual data rather than periodic inspections.