A dynamic anomaly detection method of building energy consumption based on data mining technology

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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Energy (Oxford) Ročník 263; s. 125575
Hlavní autoři: Lei, Lei, Wu, Bing, Fang, Xin, Chen, Li, Wu, Hao, Liu, Wei
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 15.01.2023
Témata:
algorithms
Building energy consumption
Dynamic anomaly detection
electric energy consumption
energy conservation
energy efficiency
K-medoids algorithm
KNN algorithm
Particle swarm optimization
Semi -supervised algorithm
KNN algorithm
Semi-supervised algorithm
Building energy consumption
Particle swarm optimization
K-medoids algorithm
Dynamic anomaly detection
ISSN:0360-5442, 1873-6785
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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 proposes 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 algorithm 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 algorithm 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 consumption data of two different forms of outliers. It can provide reliable data support for adjusting building management strategies. •A new anomaly detection method for building energy consumption.•The k-medoids clustering algorithm is optimized by PSO.•LOF algorithm is used to realize point anomaly detection.•The collective anomaly detection is realized by DTW distance.•The silhouette coefficient is used to evaluate anomaly detection methods.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0360-5442
1873-6785
DOI:10.1016/j.energy.2022.125575