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A dynamic anomaly d...
A dynamic anomaly detection method of building energy consumption based on data mining technology
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- Lei, Lei (författare)
- Zhejiang Sci Tech Univ, Sch Civil Engn & Architecture, Hangzhou 310018, Peoples R China.
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- Wu, Bing (författare)
- Guangxi Vocat & Tech Coll Commun, Coll Civil Engn & Architecture, 1258 Kunlun Ave, Nanning 530216, Peoples R China.
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- Fang, Xin (författare)
- Alibaba Grp, Alibaba Cloud, 969 West Wen Yi Rd, Hangzhou 311121, Peoples R China.
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- Chen, Li (författare)
- Alibaba Grp, Alibaba Cloud, 969 West Wen Yi Rd, Hangzhou 311121, Peoples R China.
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- Wu, Hao (författare)
- Alibaba Grp, Alibaba Cloud, 969 West Wen Yi Rd, Hangzhou 311121, Peoples R China.
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- Liu, Wei, Assistant Professor, 1987- (författare)
- KTH,Hållbara byggnader
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Zhejiang Sci Tech Univ, Sch Civil Engn & Architecture, Hangzhou 310018, Peoples R China Guangxi Vocat & Tech Coll Commun, Coll Civil Engn & Architecture, 1258 Kunlun Ave, Nanning 530216, Peoples R China. (creator_code:org_t)
- Elsevier BV, 2023
- 2023
- Engelska.
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Ingår i: Energy. - : Elsevier BV. - 0360-5442 .- 1873-6785. ; 263, s. 125575-
- Relaterad länk:
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https://doi.org/10.1...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Due to the equipment failure and inappropriate operation strategy, it is often difficult to achieve energy-efficient building. Anomaly detection of building energy consumption is one of the important approaches to improve building energy-saving. The great amounts of energy consumption data collected by building energy monitoring platforms (BEMS) provides potentials in using data mining technology for anomaly detection. This study pro-poses a dynamic anomaly detection algorithm for building energy consumption data, which realizes the dynamic detection of point anomalies and collective anomalies. The algorithm integrates unsupervised clustering algo-rithm with supervised algorithm to establish a semi-supervised matching mechanism, which avoids the influence of error label and improves the efficiency of anomaly detection. A particle swarm optimization (PSO) is used to optimize the unsupervised clustering algorithm. This investigation tests the effectiveness of the proposed algo-rithm and evaluates the performance of the energy consumption clustering algorithm by using the annual electricity consumption data of an experimental building in a university. The results show that the clustering accuracy of the algorithm can reach more than 80%, and it can effectively detect the building energy con-sumption data of two different forms of outliers. It can provide reliable data support for adjusting building management strategies.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Miljöanalys och bygginformationsteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Environmental Analysis and Construction Information Technology (hsv//eng)
Nyckelord
- Building energy consumption
- Dynamic anomaly detection
- Semi -supervised algorithm
- Particle swarm optimization
- K-medoids algorithm
- KNN algorithm
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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