E3S Web Conf.
Volume 22, 2017International Conference on Advances in Energy Systems and Environmental Engineering (ASEE17)
|Number of page(s)||8|
|Published online||07 November 2017|
An application of data mining in district heating substations for improving energy performance
1 Shool of Municipal and Environmental Engineering, Harbin Institute of Technology, Harbin 150000, China
2 State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150000, China
* Corresponding Author: email@example.com
Automatic meter reading system is capable of collecting and storing a huge number of district heating (DH) data. However, the data obtained are rarely fully utilized. Data mining is a promising technology to discover potential interesting knowledge from vast data. This paper applies data mining methods to analyse the massive data for improving energy performance of DH substation. The technical approach contains three steps: data selection, cluster analysis and association rule mining (ARM). Two-heating-season data of a substation are used for case study. Cluster analysis identifies six distinct heating patterns based on the primary heat of the substation. ARM reveals that secondary pressure difference and secondary flow rate have a strong correlation. Using the discovered rules, a fault occurring in remote flow meter installed at secondary network is detected accurately. The application demonstrates that data mining techniques can effectively extrapolate potential useful knowledge to better understand substation operation strategies and improve substation energy performance.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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