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