Conformal Prediction for electricity price forecasting in the day-ahead and real-time balancing market

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dc.contributor.authorO'Connor, Ciaranen
dc.contributor.authorBahloul, Mohameden
dc.contributor.authorRossi, Robertoen
dc.contributor.authorPrestwich, Steven D.en
dc.contributor.authorVisentin, Andreaen
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2025-10-16T10:54:47Z
dc.date.available2025-10-16T10:54:47Z
dc.date.issued2025-08-04en
dc.description.abstractThe integration of renewable energy into electricity markets poses significant challenges to price stability and increases the complexity of market operations. Accurate and reliable electricity price forecasting is crucial for effective market participation, where price dynamics can be significantly more challenging to predict. Probabilistic forecasting, through prediction intervals, efficiently quantifies the inherent uncertainties in electricity prices, supporting better decision-making for market participants. This study explores the enhancement of probabilistic price prediction using Conformal Prediction (CP) techniques, specifically Ensemble Batch Prediction Intervals and Sequential Predictive Conformal Inference. These methods provide precise and reliable prediction intervals, outperforming traditional models in validity metrics. We propose an ensemble approach that combines the efficiency of quantile regression models with the robust coverage properties of time series adapted CP techniques. This ensemble delivers both narrow prediction intervals and high coverage, leading to more reliable and accurate forecasts. We further evaluate the practical implications of CP techniques through a simulated trading algorithm applied to a battery storage system. The ensemble approach demonstrates improved financial returns in energy trading in both the Day-Ahead and Balancing Markets, highlighting its practical benefits for market participants.en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid100571en
dc.identifier.citationO’Connor, C., Bahloul, M., Rossi, R., Prestwich, S. and Visentin, A. (2025) 'Conformal Prediction for electricity price forecasting in the day-ahead and real-time balancing market', Energy and AI, 21,100571 (13pp). https://doi.org/10.1016/j.egyai.2025.100571en
dc.identifier.doi10.1016/j.egyai.2025.100571en
dc.identifier.eissn2666-5468en
dc.identifier.endpage13en
dc.identifier.journaltitleEnergy and AIen
dc.identifier.startpage889en
dc.identifier.urihttps://hdl.handle.net/10468/18045
dc.identifier.volume21en
dc.language.isoenen
dc.publisherElsevieren
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/Centres for Research Training (CRT) Programme/18/CRT/6223/IE/SFI Centre for Research Training in Artificial Intelligence/en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/Research Centres Programme::Phase 2/12/RC/2289_P2/IE/INSIGHT_Phase 2 /en
dc.rights© 2025, the Authors. Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )en
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/en
dc.subjectProbabilistic Electricity Price Forecastingen
dc.subjectConformal Predictionen
dc.subjectArbitrage tradingen
dc.subjectMachine learningen
dc.titleConformal Prediction for electricity price forecasting in the day-ahead and real-time balancing marketen
dc.typeArticle (peer-reviewed)en
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