Higher Order Mining for Monitoring District Heating Substations

Abstract

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.

A Higher Order Mining Approach for the Analysis of Real-World Datasets

“…This paper extends our previous work [10]. In the current study, the data cleaning and preparation are improved by utilizing more suitable data preprocessing methods for time series data.…”

“…Nevertheless, the alphabet size can be adjusted based on the available data. In the previous study [10], we considered four categories; i.e., the same as the number of season periods. However, due to the risk of losing information, the fifth category of medium is added.…”

Multi-view Data Mining Approach for Behaviour Analysis of Smart Control Valve

“…The SE ranges between 0 to 1. A well-performed sub-station has a SE close to 1 (100%), however, due to the generation of domestic hot water, it can go higher than 1 [21].…”

Opportunities for Machine Learning in District Heating

“…While data collection has traditionally been a costly endeavour, developments in sensor technology and networking have lowered such costs with the help of technology such as Internet of Things (IoT) [9]. Data about utilities, such as water, electricity and heating, although often collected for billing, can and are being employed to create additional value, e.g., detecting deviating and sub-optimal behaviours of DH substations [10].…”