Foreseeing the worst: Forecasting electricity DART spikes

Rémi Galarneau-Vincent, Geneviève Gauthier and Frédéric Godin

Energy Economics, 2023, vol. 119, issue C

Abstract: Statistical learning models are proposed for the prediction of the probability of a spike in the electricity DART (day-ahead minus real-time price) spread. Assessing the likelihood of DART spikes is of paramount importance for virtual bidders, among others. The model’s performance is evaluated on historical data for the Long Island zone of the New York Independent System Operator (NYISO). A tailored feature set encompassing novel engineered features is designed. Such a set of features makes it possible to achieve excellent predictive performance and discriminatory power. Results are shown to be robust to the choice of the predictive algorithm. Lastly, the benefits of forecasting the spikes are illustrated through a trading exercise, confirming that trading strategies employing the model predicted probabilities as a signal generate consistent profits.

Keywords: Power markets; Spikes prediction; DART spreads; NYISO; Predictive analytics; Statistical learning (search for similar items in EconPapers)
JEL-codes: C53 L94 N72 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:119:y:2023:i:c:s0140988323000191

DOI: 10.1016/j.eneco.2023.106521

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