Assessing tolerance-based robust short-term load forecasting in buildings

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dc.contributor.authorBorges Hernández, Cruz E.
dc.contributor.authorPeña Landaburu, Yoseba Koldobika
dc.contributor.authorFernández Andrés, Iván
dc.contributor.authorPrieto, Juan
dc.contributor.authorBretos, Oscar
dc.date.accessioned2026-09-24T14:35:42Z
dc.date.available2026-09-24T14:35:42Z
dc.date.issued2013
dc.date.updated2026-09-24T14:35:42Z
dc.description.abstractShort-term load forecasting (STLF) in buildings differs from its broader counterpart in that the load to be predicted does not seem to be stationary, seasonal and regular but, on the contrary, it may be subject to sudden changes and variations on its consumption behaviour. Classical STLF methods do not react fast enough to these perturbations (i.e., they are not robust) and the literature on building STLF has not yet explored this area. Hereby, we evaluate a well-known post-processing method (Learning Window Reinitialization) applied to two broadly-used STLF algorithms (Autoregressive Model and Support Vector Machines) in buildings to check their adaptability and robustness. We have tested the proposed method with real-world data and our results state that this methodology is especially suited for buildings with non-regular consumption profiles, as classical STLF methods are enough to model regular-profiled ones. © 2013 by the authors.en
dc.description.sponsorshipThis work was partially supported by ENERGOS CEN2009-1048 project (funded by Spanish CENIT R&D Programme); ITEA2 NEMO&CODED IDI-20110864 and IMPONET TSI-020400-2010-0103 projects (funded by Spanish Industry, Tourism and Commerce Ministry)en
dc.identifier.citationBorges, C. E., Penya, Y. K., Ferńandez, I., Prieto, J., & Bretos, O. (2013). Assessing tolerance-based robust short-term load forecasting in buildings. Energies, 6(4), 2110-2129. https://doi.org/10.3390/EN6042110
dc.identifier.doi10.3390/EN6042110
dc.identifier.eissn1996-1073
dc.identifier.urihttps://hdl.handle.net/20.500.14454/6693
dc.language.isoeng
dc.publisherMDPI AG
dc.rights©2013 by the authors
dc.subject.otherArtificial intelligence
dc.subject.otherShort term load forecasting
dc.subject.otherStatistical methods
dc.titleAssessing tolerance-based robust short-term load forecasting in buildingsen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.endPage2129
oaire.citation.issue4
oaire.citation.startPage2110
oaire.citation.titleEnergies
oaire.citation.volume6
oaire.licenseConditionhttps://creativecommons.org/licenses/by/3.0/
oaire.versionVoR
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