Learning of occupancy patterns in education facilities based on mediated data of building technologies
dc.contributor.author | Meier, Andreas | |
dc.contributor.mentor | Wache, Holger | |
dc.date.accessioned | 2023-12-22T16:03:54Z | |
dc.date.available | 2023-12-22T16:03:54Z | |
dc.date.issued | 2019 | |
dc.description.abstract | Increasing energy consumption of buildings is a growing public concern on an international level. For example, almost three quarter of the electricity demand of the USA is created in buildings and further countries show a similar tendency. Recent research papers hint that energy consumption of buildings is influenced by different contributing factors, such as buildings technology, occupancy patterns or interactions. Particularly, educational buildings imply a high energy demand and are as such not comparable to other non-residential buildings, for example offices, due to different building functionalities and usage attitudes. Adjusting the operation schedule of building services (like HVAC) in accordance of occupancy patterns provides a considerable energy saving rate. Therefore, there is a need for a precise and continual framework of occupancy assessment and prediction for classrooms in educational buildings in order to forecast the operation schedule of building services.... | |
dc.identifier.uri | https://irf.fhnw.ch/handle/11654/40422 | |
dc.language.iso | en | |
dc.publisher | Hochschule für Wirtschaft FHNW | |
dc.spatial | Olten | |
dc.subject.ddc | 330 - Wirtschaft | |
dc.title | Learning of occupancy patterns in education facilities based on mediated data of building technologies | |
dc.type | 11 - Studentische Arbeit | |
dspace.entity.type | Publication | |
fhnw.InventedHere | Yes | |
fhnw.PublishedSwitzerland | Yes | |
fhnw.StudentsWorkType | Master | |
fhnw.affiliation.hochschule | Hochschule für Wirtschaft FHNW | de_CH |
fhnw.affiliation.institut | Master of Science | |
relation.isMentorOfPublication | 9a5348f4-47b3-437d-a1f9-7cf66011e883 | |
relation.isMentorOfPublication.latestForDiscovery | 9a5348f4-47b3-437d-a1f9-7cf66011e883 |