Higher order mining for monitoring district heating substations
| Autor: | Niklas Lavesson, Christian Johansson, Shahrooz Abghari, Håkan Grahn, Jens Brage, Veselka Boeva |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Outlier Detection
Clustering algorithms Computer science 020209 energy media_common.quotation_subject 02 engineering and technology Anomaly detection Minimum spanning tree Advanced Analytics Fiber optics computer.software_genre Trees (mathematics) Fault detection and isolation District Heating Substations Sequential-pattern mining Higher-order Minimum spanning trees Heating substations Cluster analysis Clustering Analysis 020204 information systems Consensus clustering 0202 electrical engineering electronic engineering information engineering Data Mining Visualization technique media_common Creative visualization Data visualization Computer Sciences Minimum Spanning Tree Data analysis techniques Fault Detection Fault tree analysis Higher Order Mining Datavetenskap (datalogi) District heating Data analysis Data mining Raw data computer |
| Zdroj: | DSAA |
| Popis: | We propose a higher order mining (HOM) approach for modelling, monitoring and analyzing district heating (DH) substations' operational behaviour and performance. HOM is concerned with mining over patterns rather than primary or raw data. The proposed approach uses a combination of different data analysis techniques such as sequential pattern mining, clustering analysis, consensus clustering and minimum spanning tree (MST). Initially, a substation's operational behaviour is modeled by extracting weekly patterns and performing clustering analysis. The substation's performance is monitored by assessing its modeled behaviour for every two consecutive weeks. In case some significant difference is observed, further analysis is performed by integrating the built models into a consensus clustering and applying an MST for identifying deviating behaviours. The results of the study show that our method is robust for detecting deviating and sub-optimal behaviours of DH substations. In addition, the proposed method can facilitate domain experts in the interpretation and understanding of the substations' behaviour and performance by providing different data analysis and visualization techniques. © 2019 IEEE. open access |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|