Optimized machine learning approach for intelligent dust contamination prediction in solar photovoltaic systems
Solar photovoltaic systems suffer significant losses in their performance due to contamination by dust, bird droppings and other pollutants, resulting in reduced power output and greater regular maintenance requirements. Thus, the key requirement for the proper functioning of a PV system is correct prediction of contamination which allows performing timely maintenance for better working of the PV system. In this paper, an approach based on the combination of machine learning and particle swarm optimization presented with the aim to predict contamination conditions in a solar PV system.
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