dc.contributor.author | Bielskus, Jonas | |
dc.contributor.author | Motuzienė, Violeta | |
dc.contributor.author | Vilutienė, Tatjana | |
dc.contributor.author | Indriulionis, Audrius | |
dc.date.accessioned | 2023-09-18T20:31:39Z | |
dc.date.available | 2023-09-18T20:31:39Z | |
dc.date.issued | 2020 | |
dc.identifier.other | (SCOPUS_ID)85090095182 | |
dc.identifier.uri | https://etalpykla.vilniustech.lt/handle/123456789/150704 | |
dc.description.abstract | Despite increasing energy efficiency requirements, the full potential of energy efficiency is still unlocked; many buildings in the EU tend to consume more energy than predicted. Gathering data and developing models to predict occupants' behaviour is seen as the next frontier in sustainable design. Measurements in the analysed open-space office showed accordingly 3.5 and 2.7 times lower occupancy compared to the ones given by DesignBuilder's and EN 16798-1. This proves that proposed occupancy patterns are only suitable for typical open-space offices. The results of the previous studies and proposed occupancy prediction models have limited applications and limited accuracies. In this paper, the hybrid differential evolution online sequential extreme learning machine (DE-OSELM) model was applied for building occupants' presence prediction in open-space office. The model was not previously applied in this area of research. It was found that prediction using experimentally gained indoor and outdoor parameters for the whole analysed period resulted in a correlation coefficient R2 = 0.72. The best correlation was found with indoor CO2 concentration-R2 = 0.71 for the analysed period. It was concluded that a 4 week measurement period was sufficient for the prediction of the building's occupancy and that DE-OSELM is a fast and reliable model suitable for this purpose. | eng |
dc.format | PDF | |
dc.format.extent | p. 1-20 | |
dc.format.medium | tekstas / txt | |
dc.language.iso | eng | |
dc.relation.isreferencedby | CABI - CAB Abstracts | |
dc.relation.isreferencedby | Chemical abstracts | |
dc.relation.isreferencedby | Genamics Journal Seek | |
dc.relation.isreferencedby | RePec | |
dc.relation.isreferencedby | DOAJ | |
dc.relation.isreferencedby | INSPEC | |
dc.relation.isreferencedby | Science Citation Index Expanded (Web of Science) | |
dc.relation.isreferencedby | Scopus | |
dc.source.uri | https://www.mdpi.com/1996-1073/13/15/4033 | |
dc.source.uri | https://doi.org/10.3390/en13154033 | |
dc.title | Occupancy prediction using differential evolution online sequential extreme learning machine model | |
dc.type | Straipsnis Web of Science DB / Article in Web of Science DB | |
dcterms.accessRights | This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). | |
dcterms.license | Creative Commons – Attribution – 4.0 International | |
dcterms.references | 60 | |
dc.type.pubtype | S1 - Straipsnis Web of Science DB / Web of Science DB article | |
dc.contributor.institution | Vilniaus Gedimino technikos universitetas | |
dc.contributor.faculty | Aplinkos inžinerijos fakultetas / Faculty of Environmental Engineering | |
dc.contributor.faculty | Statybos fakultetas / Faculty of Civil Engineering | |
dc.contributor.faculty | Verslo vadybos fakultetas / Faculty of Business Management | |
dc.subject.researchfield | T 006 - Energetika ir termoinžinerija / Energy and thermoengineering | |
dc.subject.researchfield | T 002 - Statybos inžinerija / Construction and engineering | |
dc.subject.vgtuprioritizedfields | SD0404 - Statinių skaitmeninis modeliavimas ir tvarus gyvavimo ciklas / BIM and Sustainable lifecycle of the structures | |
dc.subject.ltspecializations | L102 - Energetika ir tvari aplinka / Energy and a sustainable environment | |
dc.subject.en | open-space office | |
dc.subject.en | occupancy prediction | |
dc.subject.en | energy-performance gap | |
dc.subject.en | online sequential extreme learning machine | |
dc.subject.en | DE-OSELM method | |
dc.subject.en | differential evolution | |
dcterms.sourcetitle | Energies | |
dc.description.issue | iss. 15 | |
dc.description.volume | vol. 13 | |
dc.publisher.name | MDPI | |
dc.publisher.city | Basel | |
dc.identifier.doi | 2-s2.0-85090095182 | |
dc.identifier.doi | 85090095182 | |
dc.identifier.doi | 1 | |
dc.identifier.doi | 000567325800001 | |
dc.identifier.doi | 10.3390/en13154033 | |
dc.identifier.elaba | 69273908 | |