  Non-availability of reliable and sustainable electric power is a major
problem in the developing world. Renewable energy sources like solar are not
very lucrative in the current stage due to various uncertainties like weather,
storage, land use among others. There also exists various other issues like
mis-commitment of power, absence of intelligent fault analysis, congestion,
etc. In this paper, we propose a novel deep learning-based system for
predicting faults and selecting power generators optimally so as to reduce
costs and ensure higher reliability in solar power systems. The results are
highly encouraging and they suggest that the approaches proposed in this paper
have the potential to be applied successfully in the developing world.
