Probabilistic Forecasting of Real-Time Electricity Market Signals via Interpretable Generative AI
This addresses the need for accurate and interpretable forecasting in electricity markets, which is crucial for grid operators and traders, though it appears incremental as it builds on existing generative and innovation-based approaches.
The paper tackles probabilistic forecasting of real-time electricity market signals by introducing WIAE-GPF, a generative AI model that generates future samples of multivariate time series, and it consistently outperforms classical and cutting-edge methods in tests using U.S. data.
This paper introduces a generative AI approach to probabilistic forecasting of real-time electricity market signals, including locational marginal prices, interregional price spreads, and demand-supply imbalances. We present WIAE-GPF, a Weak Innovation AutoEncoder-based Generative Probabilistic Forecasting architecture that generates future samples of multivariate time series. Unlike traditional black-box models, WIAE-GPF offers interpretability through the Wiener-Kallianpur innovation representation for nonparametric time series, making it a nonparametric generalization of the Wiener/Kalman filter-based forecasting. A novel learning algorithm with structural convergence guarantees is proposed, ensuring that, under ideal training conditions, the generated forecast samples match the ground truth conditional probability distribution. Extensive tests using publicly available data from U.S. independent system operators under various point and probabilistic forecasting metrics demonstrate that WIAE-GPF consistently outperforms classical methods and cutting-edge machine learning techniques.