Forecasting Electricity Market Signals via Generative AI

• URL: http://arxiv.org/abs/2403.05743v4
• Date: Fri, 28 Jun 2024 03:17:12 GMT
• Title: Forecasting Electricity Market Signals via Generative AI
• Authors: Xinyi Wang, Qing Zhao, Lang Tong,
• Abstract summary: This paper presents a generative artificial intelligence approach to probabilistic forecasting of electricity market 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. 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.
• Score: 41.99446024585741
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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