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.

中文翻译：

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能源市场的深度学习

深度学习应用于能源市场，以预测在能源电网中观察到的极端负载。由于盘中系统限制导致供需波动而出现急剧的高峰和低谷，因此预测能源负荷和价格具有挑战性。我们提出了深度时空模型和极值理论 (EVT) 来捕捉这些影响，特别是负载尖峰的尾部行为。具有 ReLU 和 $\tanh$ 激活函数的深度 LSTM 架构可以对趋势和时间依赖性进行建模，而 EVT 可以捕获高于预先指定阈值的高度不稳定的负载峰值。为了说明我们的方法，我们使用来自 PJM 互连的 4719 个节点的每小时价格和需求数据，并构建了一个深度预测器。我们表明 DL-EVT 在样本内和样本外都优于传统的傅立叶时间序列方法，通过捕捉观察到的价格非线性。最后，我们总结了未来研究的方向。