# Deep Learning for Energy Markets

arXiv: https://arxiv.org/abs/1808.05527 · HTML: https://mlsanity.com/paper/1808.05527/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2018.

## Built on by (most cited first, 2 of 2)

- [DeepExtrema: A Deep Learning Approach for Forecasting Block Maxima in Time Series Data](https://mlsanity.com/paper/2205.02441/) (2022)
- [Exceedance Probability Forecasting via Regression for Significant Wave Height Prediction](https://mlsanity.com/paper/2206.09821/) (2022)

## Abstract (arXiv)

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.