  Sustainable and economical generation of electrical power is an essential and
mandatory component of infrastructure in today's world. Optimal generation
(generator subset selection) of power requires a careful evaluation of various
factors like type of source, generation, transmission & storage capacities,
congestion among others which makes this a difficult task. We created a grid to
simulate various conditions including stimuli like generator supply, weather
and load demand using Siemens PSS/E software and this data is trained using
deep learning methods and subsequently tested. The results are highly
encouraging. As per our knowledge, this is the first paper to propose a working
and scalable deep learning model for this problem.
