Higher Order Mining for Monitoring District Heating Substations We propose a higher order mining (HOM) approach for modelling, monitoring and analyzing district heating (DH) substationsu0027 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 substationu0027s operational behaviour is modeled by extracting weekly patterns and performing clustering analysis. The substationu0027s 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 suboptimal behaviours of DH substations. In addition, the proposed method can facilitate domain experts in the interpretation and understanding of the substationsu0027 behaviour and performance by providing different data analysis and visualization techniques.

Nuclear Clustering Algorithm on State Grids IT Operation Log With the continuous evolution of the IT infrastructure of State Grid Data Center and the growing operational data in the electric power system, how to quickly and automatically cluster the operation log in the data center of State Grid has become a key issue in the IT operation and maintenance of the data center. As the most commonly used algorithms in data mining, a clustering algorithm from data mining is adopted to handle the operational log data of State Grid IT data center, which can be used to effectively discover the changes of the topology structure during the operation of the IT infrastructure. Specifically, because the traditional sequential clustering algorithm lacks the ability to discover potential links in logs, this paper proposes a selfdestructive nuclear clustering algorithm SDNmeans, which aims at the business and data characteristics of the IT infrastructure system of State Grid data center, in order to effectively classify the operational log data of State Grid IT data center during the operation of State Grid. Through the analysis of the running logs of State Grid data center with obvious time series characteristics, the proposed SDNmeans algorithm can effectively outperform the existing approaches on the operation of the data center of State Grid.