  Dust storms are common in arid zones on the earth and others planets such as
Mars. The impact of dust storms on solar radiation has significant implications
for solar power plants and autonomous vehicles powered by solar panels. This
paper deals with the analysis of solar radiation and power output of a rooftop
photovoltaic plant during a dust storm and proposes a forecasting methodology
using deep learning network. The increased aerosol content due to dust storms
increases the diffuse component of the solar radiation. This effect persists
for a long duration and can impact the quality of forecasting of solar
radiation. Deep learning networks that capture long range structure can improve
the quality of solar radiation forecasting during dust storms. These results
can help explain the sudden drop in power output of solar plants due to dust
storms originating in another continent. They can shed light on mysterious
cleaning events in autonomous vehicles powered by solar panels to be used in
space missions.
