Abstract:
With the introduction of renewable energy sources (RES), the world has entered
into a discussion where complete shift from conventional energy sources (CES)
to renewable energy sources is being considered a more viable option. However,
conventional energy sources provide a capacity factor (CP) of over 72% (Coal,
LNG, Furnace oil power plants) whereas renewable energy sources have not been
able to provide a CP over 25%. Moreover, CES are mature technologies with
extensive research and industrial applications available for use which are over 200
years old. RES, however, is only 40-50 years old with not much research and
industrial applications to completely replace CES. So, a complete cutover from
CES to RES is not possible at the given time. But the world is going through an
energy crisis at present and a quick solution is required. A possible solution is to
make a hybrid grid with inclusion of both CES and RES into the grid and smartly
manage the available energy as per demand. But to enable the grid to make such
important decisions, we need to forecast the demand which will help our grid
make better decision in energy distribution. Machine Learning (ML) and Neural
Network (NN) techniques will enable our grid to forecast the demand and
empower the grid to make such important decisions.
