Deep Learning for Energy Markets

TL;DR

This paper introduces deep spatio-temporal models combined with extreme value theory to improve prediction of extreme load spikes in energy markets, outperforming traditional methods.

Contribution

It proposes a novel integration of deep LSTM architectures with EVT for modeling and predicting volatile energy load spikes and prices.

Findings

DL-EVT outperforms Fourier time series methods in accuracy.

Deep models effectively capture nonlinearities in energy prices.

Method successfully predicts extreme load events.

Abstract

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.