dc.contributor.author | Clauß, John | |
dc.contributor.author | Caetano, Luis | |
dc.contributor.author | Svinndal, Åsmund Bror | |
dc.date.accessioned | 2024-06-11T07:10:19Z | |
dc.date.available | 2024-06-11T07:10:19Z | |
dc.date.created | 2024-06-03T08:34:58Z | |
dc.date.issued | 2024 | |
dc.identifier.issn | 0378-7788 | |
dc.identifier.uri | https://hdl.handle.net/11250/3133431 | |
dc.description.abstract | Data-driven applications in buildings using AI and machine learning have generated a lot of interest, but scaling these applications is challenging due to the uniqueness of each building. During the process of implementing a data-driven predictive heating control in a full-scale real-life office building in Norway, 24 practical challenges were encountered. In this work, those practical challenges are presented, discussed and attributed to four main categories: i) physical limitations, ii) data acquisition and communication, iii) data and model definition and iv) building occupants. Detailed examples for the challenges are provided and more than 15 lessons-learned with regards to developing and implementing data-driven services for building operation are presented. Furthermore, this work discusses how the practical challenges impact the choice of a data-driven approach to control the operation of an office building heating system in a predictive manner and how the practical challenges influence the creation of variation in the measurement data needed to identify a model during normal building operation. Finally, it is shown that a substantial number of practical challenges that were encountered during the operational phase are rooted in the design and construction phase of a building project or from rehabilitation during the operational phase. This highlights the fact that the possible use of data-driven services for building operation should be considered during the tendering and design phase to minimize the number of challenges regarding the widespread implementation of data-driven services for building operation, especially regarding predictive
control. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Elsevier | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.subject | Predictive heating control | en_US |
dc.subject | Data-driven applications | en_US |
dc.subject | Building control | en_US |
dc.subject | Office buildings | en_US |
dc.subject | Energy flexibility | en_US |
dc.subject | Data-driven services | en_US |
dc.subject | Practical challenges | en_US |
dc.title | Impact of practical challenges on the implementation of data-driven services for building operation: Insights from a real-life case study | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.rights.holder | © 2024 The Authors | en_US |
dc.source.volume | 316 | en_US |
dc.source.journal | Energy and Buildings | en_US |
dc.identifier.doi | 10.1016/j.enbuild.2024.114310 | |
dc.identifier.cristin | 2272760 | |
dc.relation.project | Norges forskningsråd: 317442 | en_US |
dc.source.articlenumber | 114310 | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 2 | |