  Ensuring sustainability demands more efficient energy management with
minimized energy wastage. Therefore, the power grid of the future should
provide an unprecedented level of flexibility in energy management. To that
end, intelligent decision making requires accurate predictions of future energy
demand/load, both at aggregate and individual site level. Thus, energy load
forecasting have received increased attention in the recent past, however has
proven to be a difficult problem. This paper presents a novel energy load
forecasting methodology based on Deep Neural Networks, specifically Long Short
Term Memory (LSTM) algorithms. The presented work investigates two variants of
the LSTM: 1) standard LSTM and 2) LSTM-based Sequence to Sequence (S2S)
architecture. Both methods were implemented on a benchmark data set of
electricity consumption data from one residential customer. Both architectures
where trained and tested on one hour and one-minute time-step resolution
datasets. Experimental results showed that the standard LSTM failed at
one-minute resolution data while performing well in one-hour resolution data.
It was shown that S2S architecture performed well on both datasets. Further, it
was shown that the presented methods produced comparable results with the other
deep learning methods for energy forecasting in literature.
