论文标题
实时位置边际价格预测使用生成对抗网络
Real-time Locational Marginal Price Forecasting Using Generative Adversarial Network
论文作者
论文摘要
在本文中，我们提出了一种无模型的无监督学习方法，以预测批发电力市场的实时位置边际价格（RTLMP）。 By organizing system-wide hourly RTLMP data into a 3-dimensional (3D) tensor consisting of a series of time-indexed matrices, we formulate the RTLMP forecasting problem as a problem of generating the next matrix with forecasted RTLMPs given the historical RTLMP tensor, and propose a generative adversarial network (GAN) model to forecast RTLMPs.所提出的公式保留了历史RTLMP张量格式的系统范围RTLMP之间的时空相关性。提出的GAN模型使用历史RTLMP张量学习了时空相关性，并生成统计上与历史RTLMP张量的RTLMP。所提出的方法仅使用公开可用的历史价格数据来预测系统范围的RTLMP，而无需涉及系统模型的机密信息，例如系统参数，拓扑或操作条件。通过使用历史RTLMP数据（SPP）中的历史RTLMP数据来验证所提出的方法的有效性。
In this paper, we propose a model-free unsupervised learning approach to forecast real-time locational marginal prices (RTLMPs) in wholesale electricity markets. By organizing system-wide hourly RTLMP data into a 3-dimensional (3D) tensor consisting of a series of time-indexed matrices, we formulate the RTLMP forecasting problem as a problem of generating the next matrix with forecasted RTLMPs given the historical RTLMP tensor, and propose a generative adversarial network (GAN) model to forecast RTLMPs. The proposed formulation preserves the spatio-temporal correlations among system-wide RTLMPs in the format of historical RTLMP tensor. The proposed GAN model learns the spatio-temporal correlations using the historical RTLMP tensors and generate RTLMPs that are statistically similar and temporally coherent to the historical RTLMP tensor. The proposed approach forecasts system-wide RTLMPs using only publicly available historical price data, without involving confidential information of system model, such as system parameters, topology, or operating conditions. The effectiveness of the proposed approach is verified through case studies using historical RTLMP data in Southwest Power Pool (SPP).
