Model-based outlier detection in district heating systems

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dc.contributor.authorGaray Martínez, Roberto
dc.contributor.authorSiddique, Muhammad Talha
dc.contributor.authorLópez Garde, Juan Manuel
dc.date.accessioned2025-06-04T07:43:31Z
dc.date.available2025-06-04T07:43:31Z
dc.date.issued2024
dc.date.updated2025-06-04T07:43:31Z
dc.descriptionPonencia presentada en la 28th International Conference on Knowledge Based and Intelligent information and Engineering Systems (KES 2024), celebrada en Sevilla, entre el 11 y el 13 de septiembre de 2024es
dc.description.abstractData-driven methods are increasingly popular for building energy performance assessment. For these to be useful, it is required that good quality data is created through the filtering of outliers and imputation of missing information. In the field of energy use in buildings, there is a clear sensitivity of heat load to outdoor climate, which needs to be considered when identifying outliers and developing imputation methods. We propose to use a well-known changepoint model to define the sensitivity of the data to climate, further segmented by time of the week. Then we use the residuals of the model to identify outliers, where those observation with residuals substantially out of the normality expectations are identified as outliers. Then missing data is repaired by means of linear imputation techniques, considering the patterns for same times of the week in the dataset. As a result of the full process, we were able to identify 5% of outliers, which resulted in the improvement of model metrics in the range of 20% mean absolute error (MAE) and slightly better R2 values.en
dc.description.sponsorshipThis study has been carried out in the context of the ATELIER project. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 864374. The research leading to this study received funding from the Basque Goverment grant for Research Groups, “Grupos de investigación del Sistema Universitario Vasco, Departamento de Educación, Universidades e Investigación” (Research group: IT1677-22)en
dc.identifier.citationGaray-Martinez, R., Siddique, M. T., & Lopez-Garde, J. M. (2024). Model-based outlier detection in district heating systems. Procedia Computer Science, 246(C), 2071-2079. https://doi.org/10.1016/J.PROCS.2024.09.646
dc.identifier.doi10.1016/J.PROCS.2024.09.646
dc.identifier.eissn1877-0509
dc.identifier.urihttp://hdl.handle.net/20.500.14454/2927
dc.language.isoeng
dc.publisherElsevier B.V.
dc.rights© 2024 The Authors
dc.subject.otherBuilding energy performance
dc.subject.otherData-driven model
dc.subject.otherOutlier detection
dc.titleModel-based outlier detection in district heating systemsen
dc.typeconference paper
dcterms.accessRightsopen access
oaire.citation.endPage2079
oaire.citation.issueC
oaire.citation.startPage2071
oaire.citation.titleProcedia Computer Science
oaire.citation.volume246
oaire.licenseConditionhttps://creativecommons.org/licenses/by-nc-nd/4.0/
oaire.versionVoR
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