A context-aware method for building occupancy prediction.
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| Title: | A context-aware method for building occupancy prediction. |
|---|---|
| Authors: | Adamopoulou, Anna A.1 adamanna@iti.gr, Tryferidis, Athanasios M.1 thanasic@iti.gr, Tzovaras, Dimitrios K.1 dimitrios.tzovaras@iti.gr |
| Source: | Energy & Buildings. Jan2016, Vol. 110, p229-244. 16p. |
| Subjects: | Energy consumption of buildings, Prediction models, Markov processes, Box-Jenkins forecasting, Support vector machines, Algorithms |
| Abstract: | In this paper a building occupancy prediction method is presented, which is based on the spatio-temporal analysis of historical data (occupancy modelling) and further relies heavily on current contextual information, being therefore suitable for providing real-time prediction. Two different algorithmic approaches are proposed, based on Markov models, revealing how context awareness adds the capability of rapidly adjusting to current conditions and capturing unexpected events, as opposed to capturing only typical occupancy fluctuation expected on a regular basis. Both proposed approaches are evaluated against accurate real-life data collected from a tertiary building, achieving notable results which outperform currently used methods. [ABSTRACT FROM AUTHOR] |
| Copyright of Energy & Buildings is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 111495824 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A context-aware method for building occupancy prediction. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Adamopoulou%2C+Anna+A%2E%22">Adamopoulou, Anna A.</searchLink><relatesTo>1</relatesTo><i> adamanna@iti.gr</i><br /><searchLink fieldCode="AR" term="%22Tryferidis%2C+Athanasios+M%2E%22">Tryferidis, Athanasios M.</searchLink><relatesTo>1</relatesTo><i> thanasic@iti.gr</i><br /><searchLink fieldCode="AR" term="%22Tzovaras%2C+Dimitrios+K%2E%22">Tzovaras, Dimitrios K.</searchLink><relatesTo>1</relatesTo><i> dimitrios.tzovaras@iti.gr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Energy+%26+Buildings%22">Energy & Buildings</searchLink>. Jan2016, Vol. 110, p229-244. 16p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Energy+consumption+of+buildings%22">Energy consumption of buildings</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Markov+processes%22">Markov processes</searchLink><br /><searchLink fieldCode="DE" term="%22Box-Jenkins+forecasting%22">Box-Jenkins forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In this paper a building occupancy prediction method is presented, which is based on the spatio-temporal analysis of historical data (occupancy modelling) and further relies heavily on current contextual information, being therefore suitable for providing real-time prediction. Two different algorithmic approaches are proposed, based on Markov models, revealing how context awareness adds the capability of rapidly adjusting to current conditions and capturing unexpected events, as opposed to capturing only typical occupancy fluctuation expected on a regular basis. Both proposed approaches are evaluated against accurate real-life data collected from a tertiary building, achieving notable results which outperform currently used methods. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Energy & Buildings is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.enbuild.2015.10.003 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 229 Subjects: – SubjectFull: Energy consumption of buildings Type: general – SubjectFull: Prediction models Type: general – SubjectFull: Markov processes Type: general – SubjectFull: Box-Jenkins forecasting Type: general – SubjectFull: Support vector machines Type: general – SubjectFull: Algorithms Type: general Titles: – TitleFull: A context-aware method for building occupancy prediction. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Adamopoulou, Anna A. – PersonEntity: Name: NameFull: Tryferidis, Athanasios M. – PersonEntity: Name: NameFull: Tzovaras, Dimitrios K. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2016 Type: published Y: 2016 Identifiers: – Type: issn-print Value: 03787788 Numbering: – Type: volume Value: 110 Titles: – TitleFull: Energy & Buildings Type: main |
| ResultId | 1 |
