  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.
