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Linking building energy consumption with occupants' energy-consuming behaviors in commercial buildings: Non-intrusive occupant load monitoring (NIOLM) : Linking building energy consumption with occupants energy-consuming behaviors in commercial buildings: Non-intrusive occupant load monitoring (NIOLM)
DC Field | Value | Language |
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dc.contributor.author | Rafsanjani, Hamed Nabizadeh | - |
dc.contributor.author | Ahn, Changbum R. | - |
dc.contributor.author | Chen, Jiayu | - |
dc.date.accessioned | 2024-05-17T08:02:45Z | - |
dc.date.available | 2024-05-17T08:02:45Z | - |
dc.date.created | 2024-05-16 | - |
dc.date.created | 2024-05-16 | - |
dc.date.issued | 2018-08 | - |
dc.identifier.citation | Energy and Buildings, Vol.172, pp.317-327 | - |
dc.identifier.issn | 0378-7788 | - |
dc.identifier.uri | https://hdl.handle.net/10371/203256 | - |
dc.description.abstract | Occupants energy-consuming behaviors have a significant influence on overall energy consumption in commercial buildings. Accordingly, understanding and intervening in these behaviors offers a significant opportunity for energy savings in commercial buildings. Current approaches to behavior modification rely on available occupant-specific energy consumption data, but capturing such data is generally expensive. One possible solution to this challenge is to link energy consumption to individual occupants energy-use behaviors in commercial buildings. In this context, this study proposes a non-intrusive occupant load monitoring (NIOLM) approach that couples occupancy-sensing data—captured from existing Wi-Fi infrastructures—with power changes in aggregate building-wide energy data to thereby disaggregate building-wide data down to the individual. This paper describes two case studies that investigate the feasibility of using the NIOLM approach to identify occupant-specific energy consumption information. Tracking eleven occupants energy-use behaviors using NIOLM over a four-month period resulted in an average F-measure of 0.778 and Accuracy of 0.955. The case studies thereby demonstrated that NIOLM successfully tracks individual occupants energy-consuming behaviors at minimal cost by utilizing existing high-resolution metering devices and Wi-Fi network infrastructures in commercial buildings. | - |
dc.language | 영어 | - |
dc.publisher | Elsevier BV | - |
dc.title | Linking building energy consumption with occupants' energy-consuming behaviors in commercial buildings: Non-intrusive occupant load monitoring (NIOLM) | - |
dc.title.alternative | Linking building energy consumption with occupants energy-consuming behaviors in commercial buildings: Non-intrusive occupant load monitoring (NIOLM) | - |
dc.type | Article | - |
dc.identifier.doi | 10.1016/j.enbuild.2018.05.007 | - |
dc.citation.journaltitle | Energy and Buildings | - |
dc.identifier.wosid | 000438181100029 | - |
dc.identifier.scopusid | 2-s2.0-85047166851 | - |
dc.citation.endpage | 327 | - |
dc.citation.startpage | 317 | - |
dc.citation.volume | 172 | - |
dc.description.isOpenAccess | N | - |
dc.contributor.affiliatedAuthor | Ahn, Changbum R. | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.subject.keywordPlus | THERMAL COMFORT | - |
dc.subject.keywordPlus | KNOWLEDGE | - |
dc.subject.keywordPlus | WIFI | - |
dc.subject.keywordAuthor | Non-intrusive approach | - |
dc.subject.keywordAuthor | Data mining | - |
dc.subject.keywordAuthor | Power change | - |
dc.subject.keywordAuthor | Occupant energy-use behavior | - |
dc.subject.keywordAuthor | Commercial buildings | - |
dc.subject.keywordAuthor | Wi-Fi network | - |
dc.subject.keywordAuthor | Aggregate load data | - |
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- College of Engineering
- Department of Architecture & Architectural Engineering
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