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dc.creatorBeltrán, S. (Sergio)-
dc.creatorCastro, A. (Alain)-
dc.creatorIrizar, I. (Ion)-
dc.creatorNaveran, G. (Gorka)-
dc.creatorYeregui, I. (Imanol)-
dc.date.accessioned2022-04-13T10:47:06Z-
dc.date.available2022-04-13T10:47:06Z-
dc.date.issued2022-
dc.identifier.citationBeltrán, S. (Sergio); Castro, A. (Alain); Irizar, I. (Ion); et al. "Framework for collaborative intelligence in forecasting day-ahead electricity price". Applied Energy. (306), 2022, 118049es
dc.identifier.issn0306-2619-
dc.identifier.urihttps://hdl.handle.net/10171/63389-
dc.description.abstractElectricity price forecasting in wholesale markets is an essential asset for deciding bidding strategies and operational schedules. The decision making process is limited if no understanding is given on how and why such electricity price points have been forecast. The present article proposes a novel framework that promotes human–machine collaboration in forecasting day-ahead electricity price in wholesale markets. The framework is based on a new model architecture that uses a plethora of statistical and machine learning models, a wide range of exogenous features, a combination of several time series decomposition methods and a collection of time series characteristics based on signal processing and time series analysis methods. The model architecture is supported by open-source automated machine learning platforms that provide a baseline reference used for comparison purposes. The objective of the framework is not only to provide forecasts, but to promote a human-in-the-loop approach by providing a data story based on a collection of model-agnostic methods aimed at interpreting the mechanisms and behavior of the new model architecture and its predictions. The framework has been applied to the Spanish wholesale market. The forecasting results show good accuracy on mean absolute error (1.859, 95% HDI [0.575, 3.924] EUR (MWh)−1) and mean absolute scaled error (0.378, 95% HDI [0.091, 0.934]). Moreover, the framework demonstrates its human-centric capabilities by providing graphical and numeric explanations that augments understanding on the model and its electricity price point forecasts.es_ES
dc.description.sponsorshipThis work has been supported by the Basque Government by means of the ‘‘Hazitek Program for Research and Business Development’’, under OptiCogen project (grant agreement N◦ ZL-2018/00488). It has also been supported by the Spanish Ministry of Science and Innovation by means of the ‘‘Cervera Grants Program for Technological Centres’’, under MIRAGED project (grant agreement N◦ CER-20190001)es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectAugmented analyticses_ES
dc.subjectAutomated machine learninges_ES
dc.subjectEnsemble modelses_ES
dc.subjectExplainable artificial intelligencees_ES
dc.subjectTime series decompositiones_ES
dc.subjectTime series hybrid modelses_ES
dc.titleFramework for collaborative intelligence in forecasting day-ahead electricity pricees_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.description.noteThis is an open access article under the CC BY-NC-ND licensees_ES
dc.identifier.doi10.1016/j.apenergy.2021.118049-
dadun.citation.number306es_ES
dadun.citation.publicationNameApplied Energyes_ES
dadun.citation.startingPage118049es_ES

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