  This paper presents a generative artificial intelligence approach to
probabilistic forecasting of electricity market signals, such as real-time
locational marginal prices and area control error signals. Inspired by the
Wiener-Kallianpur innovation representation of nonparametric time series, we
propose a weak innovation autoencoder architecture and a novel deep learning
algorithm that extracts the canonical independent and identically distributed
innovation sequence of the time series, from which samples of future time
series are generated. The validity of the proposed approach is established by
proving that, under ideal training conditions, the generated samples have the
same conditional probability distribution as that of the ground truth. Three
applications involving highly dynamic and volatile time series in real-time
market operations are considered: (i) locational marginal price forecasting for
self-scheduled resources such as battery storage participants, (ii)
interregional price spread forecasting for virtual bidders in interchange
markets, and (iii) area control error forecasting for frequency regulations.
Numerical studies based on market data from multiple independent system
operators demonstrate the superior performance of the proposed generative
forecaster over leading classical and modern machine learning techniques under
both probabilistic and point forecasting metrics.
