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dc.contributor.authorÁlvarez, V. 
dc.contributor.authorMazuelas, S. 
dc.contributor.authorLozano, J.A. 
dc.date.accessioned2021-02-05T13:40:27Z
dc.date.available2021-02-05T13:40:27Z
dc.date.issued2020
dc.identifier.issn0885-8950
dc.identifier.urihttp://hdl.handle.net/20.500.11824/1248
dc.description.abstractLoad forecasting is crucial for multiple energy management tasks such as scheduling generation capacity, planning supply and demand, and minimizing energy trade costs. Such relevance has increased even more in recent years due to the integration of renewable energies, electric cars, and microgrids. Conventional load forecasting techniques obtain singlevalue load forecasts by exploiting consumption patterns of past load demand. However, such techniques cannot assess intrinsic uncertainties in load demand, and cannot capture dynamic changes in consumption patterns. To address these problems, this paper presents a method for probabilistic load forecasting based on the adaptive online learning of hidden Markov models. We propose learning and forecasting techniques with theoretical guarantees, and experimentally assess their performance in multiple scenarios. In particular, we develop adaptive online learning techniques that update model parameters recursively, and sequential prediction techniques that obtain probabilistic forecasts using the most recent parameters. The performance of the method is evaluated using multiple datasets corresponding with regions that have different sizes and display assorted time-varying consumption patterns. The results show that the proposed method can significantly improve the performance of existing techniques for a wide range of scenarios.en_US
dc.description.sponsorshipRamon y Cajal Grant RYC-2016-19383 Basque Government under the grant "Artificial Intelligence in BCAM number EXP. 2019/00432" Iberdrola Foundation under the 2019 Research Grantsen_US
dc.formatapplication/pdfen_US
dc.language.isoengen_US
dc.rightsReconocimiento-NoComercial-CompartirIgual 3.0 Españaen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.subjectLoad forecasting, probabilistic load forecasting, online learning, hidden Markov model.en_US
dc.titleProbabilistic Load Forecasting Based on Adaptive Online Learningen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.identifier.doi10.1109/TPWRS.2021.3050837
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/9321099en_US
dc.relation.projectIDES/1PE/SEV-2017-0718en_US
dc.relation.projectIDES/1PE/TIN2017-82626-Ren_US
dc.relation.projectIDES/2PE/PID2019-105058GA-I00en_US
dc.relation.projectIDEUS/BERC/BERC.2018-2021en_US
dc.relation.projectIDEUS/ELKARTEKen_US
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen_US
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersionen_US
dc.journal.titleIEEE-Transactions on Power Systemsen_US


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Reconocimiento-NoComercial-CompartirIgual 3.0 España
Except where otherwise noted, this item's license is described as Reconocimiento-NoComercial-CompartirIgual 3.0 España