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dc.contributor.authorMuñoz, Miguel Ángel
dc.contributor.authorMorales, Juan Miguel
dc.contributor.authorPineda, Salvador
dc.date.accessioned2020-02-26T10:53:41Z
dc.date.available2020-02-26T10:53:41Z
dc.date.created2020
dc.date.issued2020-02-26
dc.identifier.urihttps://hdl.handle.net/10630/19333
dc.descriptionM. A. Muñoz, J. M. Morales, and S. Pineda, Feature-driven Improvement of Renewable Energy Forecasting and Trading, IEEE Transactions on Power Systems, 2020.en_US
dc.description.abstractInspired from recent insights into the common ground of machine learning, optimization and decision-making, this paper proposes an easy-to-implement, but effective procedure to enhance both the quality of renewable energy forecasts and the competitive edge of renewable energy producers in electricity markets with a dual-price settlement of imbalances. The quality and economic gains brought by the proposed procedure essentially stem from the utilization of valuable predictors (also known as features) in a data-driven newsvendor model that renders a computationally inexpensive linear program. We illustrate the proposed procedure and numerically assess its benefits on a realistic case study that considers the aggregate wind power production in the Danish DK1 bidding zone as the variable to be predicted and traded. Within this context, our procedure leverages, among others, spatial information in the form of wind power forecasts issued by transmission system operators (TSO) in surrounding bidding zones and publicly available in online platforms. We show that our method is able to improve the quality of the wind power forecast issued by the Danish TSO by several percentage points (when measured in terms of the mean absolute or the root mean square error) and to significantly reduce the balancing costs incurred by the wind power producer.en_US
dc.description.sponsorshipEuropean Research Council (ERC) under the EU Horizon 2020 research and innovation programme (grant agreement No. 755705) Spanish Ministry of Economy, Industry, and Competitiveness through project ENE2017-83775-P.en_US
dc.language.isoengen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectEnergías renovablesen_US
dc.subject.otherMercados eléctricosen_US
dc.subject.otherOptimizaciónen_US
dc.subject.otherTécnicas de predicciónen_US
dc.subject.otherMachine learningen_US
dc.titleFeature-driven improvement of renewable energy forecasting and tradingen_US
dc.typeinfo:eu-repo/semantics/preprinten_US
dc.centroEscuela de Ingenierías Industrialesen_US
dc.identifier.doi10.1109/TPWRS.2020.2975246


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