Foreseeing the Worst: Forecasting Electricity DART Spikes

44 Pages Posted: 23 Jun 2022

See all articles by Rémi Galarneau-Vincent

Rémi Galarneau-Vincent

HEC Montréal, Students

Geneviève Gauthier

Department of decision Sciences and GERAD; Associate member, Oxford-Man Institute (OMI)

Frédéric Godin

Concordia University, Quebec - Department of Mathematics & Statistics

Date Written: June 10, 2022

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

JEL Classification: C53, L94, N72

Suggested Citation

Galarneau-Vincent, Rémi and Gauthier, Genevieve and Godin, Frédéric, Foreseeing the Worst: Forecasting Electricity DART Spikes (June 10, 2022). Available at SSRN: https://ssrn.com/abstract=4133744 or http://dx.doi.org/10.2139/ssrn.4133744

Rémi Galarneau-Vincent

HEC Montréal, Students ( email )

Montreal
Canada

Genevieve Gauthier

Department of decision Sciences and GERAD ( email )

3000 Côte-Sainte-Catherine Road
Montreal, QC H2S1L4
Canada

Associate member, Oxford-Man Institute (OMI) ( email )

Eagle House
Walton Well Road
Oxford, Oxfordshire OX2 6ED
United Kingdom

Frédéric Godin (Contact Author)

Concordia University, Quebec - Department of Mathematics & Statistics ( email )

1455 De Maisonneuve Blvd. W.
Montreal, Quebec H3G 1M8
Canada

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