论文标题
预测实时位置边际价格：一种基于GAN的视频预测方法
Predicting Real-Time Locational Marginal Prices: A GAN-Based Video Prediction Approach
论文作者
论文摘要
在本文中，我们提出了一种无监督的数据驱动方法来预测实时位置边际价格（RTLMP）。所提出的方法建立在用于将系统范围的异质市场数据流组织为市场数据图像和视频形式的一般数据结构。利用这种一般数据结构，将系统范围的RTLMP预测问题作为视频预测问题提出。提出了一个基于生成对抗网络（GAN）的视频预测模型，以了解历史RTLMPS之间的时空相关性，并在接下来的一个小时内预测系统范围的RTLMP。采用自回旋移动平均值（ARMA）校准方法来提高预测准确性。提出的RTLMP预测方法将公共市场数据作为输入，而无需任何有关系统拓扑，模型参数或市场操作细节的机密信息。使用ISO新英格兰（ISO-NE）和西南电力池（SPP）的公共市场数据进行案例研究表明，所提出的方法能够学习RTLMPS之间的时空相关性并进行准确的RTLMP预测。
In this paper, we propose an unsupervised data-driven approach to predict real-time locational marginal prices (RTLMPs). The proposed approach is built upon a general data structure for organizing system-wide heterogeneous market data streams into the format of market data images and videos. Leveraging this general data structure, the system-wide RTLMP prediction problem is formulated as a video prediction problem. A video prediction model based on generative adversarial networks (GAN) is proposed to learn the spatio-temporal correlations among historical RTLMPs and predict system-wide RTLMPs for the next hour. An autoregressive moving average (ARMA) calibration method is adopted to improve the prediction accuracy. The proposed RTLMP prediction method takes public market data as inputs, without requiring any confidential information on system topology, model parameters, or market operating details. Case studies using public market data from ISO New England (ISO-NE) and Southwest Power Pool (SPP) demonstrate that the proposed method is able to learn spatio-temporal correlations among RTLMPs and perform accurate RTLMP prediction.
