论文标题
深度学习预测师，具有外源变量的日期边际价格
A Deep Learning Forecaster with Exogenous Variables for Day-Ahead Locational Marginal Price
论文作者
论文摘要
已经提出了几种方法，以预测放松管制的能源市场的日期临时位置边际价格（DALMP）。深度学习的兴起激发了其在能源价格预测中的使用，但是大多数深度学习方法无法适应外源变量，这些变量在DALMP的峰值和山谷中产生了重大影响。对发电机的准确预测对发电机至关重要，因为他们面临的最重要的决定之一是是否要损失出售电力，以防止发生关闭和启动成本，或者以生产成本竞标并面临关闭的风险。在本文中，我们提出了一个深度学习模型，该模型既结合了DALMP的历史和外源变量的影响（例如，预测负载，天气数据）。 PJM独立系统运营商（ISO）的数值研究说明了所提出的模型在支持基于风险的关闭决策分析的同时如何优于传统时间序列技术。
Several approaches have been proposed to forecast day-ahead locational marginal price (daLMP) in deregulated energy markets. The rise of deep learning has motivated its use in energy price forecasts but most deep learning approaches fail to accommodate for exogenous variables, which have significant influence in the peaks and valleys of the daLMP. Accurate forecasts of the daLMP valleys are of crucial importance for power generators since one of the most important decisions they face is whether to sell power at a loss to prevent incurring in shutdown and start-up costs, or to bid at production cost and face the risk of shutting down. In this article we propose a deep learning model that incorporates both the history of daLMP and the effect of exogenous variables (e.g., forecasted load, weather data). A numerical study at the PJM independent system operator (ISO) illustrates how the proposed model outperforms traditional time series techniques while supporting risk-based analysis of shutdown decisions.
