PV monitoring and fault detection: Evaluation of machine learning for prediction of PV soiling
PV monitoring and fault detection: Evaluation of machine learning for prediction of PV soiling in Northern Cape, South-Africa Gard Inge Rosvold Published 2017 Engineering Renewable energy sources, and thus PV are experiencing exponential growth due to most current energy production still relies on fossil fuels, and energy demands are steadily increasing. If the performance of PV could be increased, the result will be more production per installation. One significant performance loss for PV is soiling on the modules. Research has been done to statistically indicate optimal cleaning intervals. Some attempts using conventional methods to predict soiling have been conducted as well, suggesting environmental features like wind and humidity are relevant factors for predicting soiling. With the increase in popularity and availability of machine learning – is it possible to use machine learning to predict soiling? If it is possible, this could lead to quick and precise implementation of algori