  Deep Learning is applied to energy markets to predict extreme loads observed
in energy grids. Forecasting energy loads and prices is challenging due to
sharp peaks and troughs that arise due to supply and demand fluctuations from
intraday system constraints. We propose deep spatio-temporal models and extreme
value theory (EVT) to capture theses effects and in particular the tail
behavior of load spikes. Deep LSTM architectures with ReLU and $\tanh$
activation functions can model trends and temporal dependencies while EVT
captures highly volatile load spikes above a pre-specified threshold. To
illustrate our methodology, we use hourly price and demand data from 4719 nodes
of the PJM interconnection, and we construct a deep predictor. We show that
DL-EVT outperforms traditional Fourier time series methods, both in-and
out-of-sample, by capturing the observed nonlinearities in prices. Finally, we
conclude with directions for future research.
