Enhancing the missing data imputation of primary substation load demand records

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dc.contributor.authorBorges Hernández, Cruz E.
dc.contributor.authorKamara Esteban, Oihane
dc.contributor.authorCastillo Calzadilla, Tony
dc.contributor.authorMartín Andonegui, Cristina
dc.contributor.authorAlonso Vicario, Ainhoa
dc.date.accessioned2025-10-03T12:04:38Z
dc.date.available2025-10-03T12:04:38Z
dc.date.issued2020-06-23
dc.date.updated2025-10-03T12:04:38Z
dc.description.abstractThe daily analysis of loads is one of the most important activities for power utilities in order to be able to meet the energy demand. This analysis not only includes short-term forecasting but it also encompasses the completion of missing load data, known as imputation. In this work we show that adding information of attached or bordering primary substation helps to improve the prediction accuracy in a single substation, since its neighbours may share common weather-related (e.g. temperature, humidity, wind direction, etc.) and human-related (e.g. work-calendar, holidays, cultural consumption patterns, etc.) data. In order to validate these approaches, we test the forecasting and imputation neighbouring methodology on a wide variety of datasets. Results confirm that, given a primary substation, the addition of information from surrounding substations does improve the forecasting and imputation errors.en
dc.description.sponsorshipThis work was partially supported by : 1. Fundación Iberdrola, Spain, grant for Research in Energy and the Environment call 2019. 2. SentientThings: towards augmented everyday objects that self-adapt and co-educate users to underpin their proenvironmental behaviour through persuasive technologies, TIN2017-90042-R. 3. GREENSOUL: Eco-aware Persuasive Networked Data Devices for Useren
dc.identifier.citationBorges, C. E., Kamara-Esteban, O., Castillo-Calzadilla, T., Andonegui, C. M., & Alonso-Vicario, A. (2020). Enhancing the missing data imputation of primary substation load demand records. Sustainable Energy, Grids and Networks, 23. https://doi.org/10.1016/J.SEGAN.2020.100369
dc.identifier.doi10.1016/J.SEGAN.2020.100369
dc.identifier.eissn2352-4677
dc.identifier.urihttps://hdl.handle.net/20.500.14454/3852
dc.language.isoeng
dc.publisherElsevier Ltd
dc.rights©2020 The Authors
dc.subject.otherData imputation
dc.subject.otherLoad consumption
dc.subject.otherMissing data treatment
dc.subject.otherShort-term load forecasting
dc.subject.otherSubstation datasets
dc.titleEnhancing the missing data imputation of primary substation load demand recordsen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.titleSustainable Energy, Grids and Networks
oaire.citation.volume23
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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