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DC Field | Value | Language |
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dc.contributor.author | Mesa-Jiménez, JJ | - |
dc.contributor.author | Stokes, L | - |
dc.contributor.author | Yang, Q | - |
dc.contributor.author | Livina, VN | - |
dc.date.accessioned | 2024-03-29T19:13:40Z | - |
dc.date.available | 2024-03-29T19:13:40Z | - |
dc.date.issued | 2020-10-05 | - |
dc.identifier | ORCiD: J J Mesa-Jiménez https://orcid.org/0000-0003-0822-2700 | - |
dc.identifier | ORCiD: Qingping Yang https://orcid.org/0000-0002-2557-8752 | - |
dc.identifier | 045003 | - |
dc.identifier.citation | Mesa-Jiménez, J.J. et al. (2020) 'Machine learning for BMS analysis and optimisation', Engineering Research Express, 2 (4), 045003, pp. 1 - 17. doi: 10.1088/2631-8695/abbb85. | en_US |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/28658 | - |
dc.description.abstract | In large buildings, linking heating, cooling or ventilation systems between themselves and to physical spaces is a very time-consuming task that requires highly skilled engineering knowledge, as all these systems are interconnected and they have a certain influence to each other (ventilation systems are often connected to heating and cooling), which often makes task of locating the sources of error or anomalies very time consuming and difficult as they are performed manually. A different approach would be to work out relationships and equipment linkage from time series data provided by the sensors, thus inferring equipment links from which anomalies can be traced back to the source more easily. This paper proposes a data-based solution to obtain equipment relationships based on cross-correlations to relate Air Handling Units (AHUs) to their respective areas of operation. We also propose a methodology, in particular for AHUs, to identify whether or not to trust correlations based on the difference between supply and return temperature. A case study is presented based a large building with 16 AHU systems. | en_US |
dc.description.sponsorship | Department for Business, Energy and Industrial Strategy of the United Kingdom; College of Engineering, Design and Physical Sciences of Brunel University London | - |
dc.format.extent | 1 - 17 | - |
dc.format.medium | Electronic | - |
dc.language | English | - |
dc.language.iso | en | en_US |
dc.publisher | IOP Publishing | en_US |
dc.rights | Copyright © 2020 The Author(s). Published by IOP Publishing Ltd. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence (https://creativecommons.org/licenses/by/4.0). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0 | - |
dc.title | Machine learning for BMS analysis and optimisation | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1088/2631-8695/abbb85 | - |
dc.relation.isPartOf | Engineering Research Express | - |
pubs.issue | 4 | - |
pubs.publication-status | Published | - |
pubs.volume | 2 | - |
dc.identifier.eissn | 2631-8695 | - |
dc.rights.license | https://creativecommons.org/licenses/by/4.0/legalcode.en | - |
dc.rights.holder | The Author(s) | - |
Appears in Collections: | Dept of Mechanical and Aerospace Engineering Research Papers |
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FullText.pdf | Copyright © 2020 The Author(s). Published by IOP Publishing Ltd. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence (https://creativecommons.org/licenses/by/4.0). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. | 1.33 MB | Adobe PDF | View/Open |
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