dc.contributor.author | Motuzienė, Violeta | |
dc.contributor.author | Bielskus, Jonas | |
dc.contributor.author | Lapinskienė, Vilūnė | |
dc.contributor.author | Rynkun, Genrika | |
dc.contributor.author | Bernatavičienė, Jolita | |
dc.date.accessioned | 2023-09-18T16:10:33Z | |
dc.date.available | 2023-09-18T16:10:33Z | |
dc.date.issued | 2022 | |
dc.identifier.issn | 2210-6707 | |
dc.identifier.uri | https://etalpykla.vilniustech.lt/handle/123456789/112100 | |
dc.description.abstract | Buildings’ occupancy is one of the important factors causing the energy performance and sustainability gap in buildings. Better occupancy prediction decreases this gap both in the design stage and in the use phase of the building. Machine learning-based models proved to be very accurate and fast for occupancy prediction when buildings are exploited under normal conditions. Meanwhile, during the Covid-19 pandemic occupancy of the offices has dramatically changed. The study presents 2 office buildings’ long-term monitoring results for different periods of the pandemic. It aims to analyse actual occupancies during the pandemic and its influence on the ELM (Extreme Learning Machine) based occupancy-forecasting models’ reliability. The results show much lower actual occupancies in the offices than given in standards and methodologies; it is still low even when quarantines are cancelled. Average peak occupancy within the whole measured period is: for Building A – 12–20% and for Building B – 2–23%. The daily occupancy schedules differ for both offices as they belong to different industries. ELM-SA model has shown low accuracies during pandemic periods as a result of lower occupancies – R2 = 0.27–0.56. | eng |
dc.format | PDF | |
dc.format.extent | p. 1-12 | |
dc.format.medium | tekstas / txt | |
dc.language.iso | eng | |
dc.relation.isreferencedby | Science Citation Index Expanded (Web of Science) | |
dc.relation.isreferencedby | Scopus | |
dc.relation.isreferencedby | INSPEC | |
dc.relation.isreferencedby | EI Compendex Plus | |
dc.relation.isreferencedby | ScienceDirect | |
dc.source.uri | https://doi.org/10.1016/j.scs.2021.103557 | |
dc.title | Office buildings occupancy analysis and prediction associated with the impact of the COVID-19 pandemic | |
dc.type | Straipsnis Web of Science DB / Article in Web of Science DB | |
dcterms.references | 59 | |
dc.type.pubtype | S1 - Straipsnis Web of Science DB / Web of Science DB article | |
dc.contributor.institution | Vilniaus Gedimino technikos universitetas | |
dc.contributor.institution | Vilniaus universitetas | |
dc.contributor.faculty | Aplinkos inžinerijos fakultetas / Faculty of Environmental Engineering | |
dc.subject.researchfield | T 006 - Energetika ir termoinžinerija / Energy and thermoengineering | |
dc.subject.researchfield | T 002 - Statybos inžinerija / Construction and engineering | |
dc.subject.researchfield | T 007 - Informatikos inžinerija / Informatics engineering | |
dc.subject.studydirection | E13 - Energijos inžinerija / Energy engineering | |
dc.subject.studydirection | E05 - Statybos inžinerija / Civil engineering | |
dc.subject.vgtuprioritizedfields | AE0303 - Pastatų energetika / Building energetics | |
dc.subject.ltspecializations | L102 - Energetika ir tvari aplinka / Energy and a sustainable environment | |
dc.subject.en | occupancy | |
dc.subject.en | prediction | |
dc.subject.en | ELM | |
dc.subject.en | long-term monitoring | |
dc.subject.en | pandemic | |
dc.subject.en | Covid-19 | |
dcterms.sourcetitle | Sustainable cities and society | |
dc.description.volume | vol. 77 | |
dc.publisher.name | Elsevier | |
dc.publisher.city | Amsterdam | |
dc.identifier.doi | 000760316300007 | |
dc.identifier.doi | 10.1016/j.scs.2021.103557 | |
dc.identifier.elaba | 112866440 | |