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.
@article{arxiv.1711.02810,
title = {Deep Fault Analysis and Subset Selection in Solar Power Grids},
author = {Biswarup Bhattacharya and Abhishek Sinha},
journal= {arXiv preprint arXiv:1711.02810},
year = {2017}
}
Comments
Presented at NIPS 2017 Workshop on Machine Learning for the Developing World