英文标题：
《Deep Learning for Energy Markets》
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作者：
Michael Polson and Vadim Sokolov
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最新提交年份：
2019
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英文摘要：
  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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中文摘要：
深度学习应用于能源市场，以预测在能源网格中观察到的极端负荷。预测能源负荷和价格具有挑战性，因为由于日间系统约束引起的供需波动，会出现尖峰和低谷。我们提出了深层时空模型和极值理论（EVT）来捕捉这些影响，尤其是负载尖峰的尾部行为。具有ReLU和$\\tanh$激活功能的深层LSTM体系结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波动性负载峰值。为了说明我们的方法，我们使用PJM互连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。我们发现，DL-EVT通过捕获价格中观察到的非线性，在样本内和样本外都优于传统的傅立叶时间序列方法。最后，我们总结了未来的研究方向。
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分类信息：

一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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