Automated Statistical Methods for Fault Detection in District Heating Customer Installations

Sara Månsson, Kristin Davidsson, Patrick Lauenburg and Marcus Thern
Additional contact information
Sara Månsson: Department of Energy Sciences, Faculty of Engineering, Lund University, P.O.Box 118, SE-221 00 Lund, Sweden
Kristin Davidsson: Department of Energy Sciences, Faculty of Engineering, Lund University, P.O.Box 118, SE-221 00 Lund, Sweden
Patrick Lauenburg: Department of Energy Sciences, Faculty of Engineering, Lund University, P.O.Box 118, SE-221 00 Lund, Sweden
Marcus Thern: Department of Energy Sciences, Faculty of Engineering, Lund University, P.O.Box 118, SE-221 00 Lund, Sweden

Energies, 2018, vol. 12, issue 1, 1-18

Abstract: In order to develop more sustainable district heating systems, the district heating sector is currently trying to increase the energy efficiency of these systems. One way of doing so is to identify customer installations in the systems that have poor cooling performance. This study aimed to develop an algorithm that was able to detect the poorly performing installations automatically using meter readings from the installations. The algorithm was developed using statistical methods and was tested on a data set consisting of data from 3000 installations located in a district heating system in Sweden. As many as 1273 installations were identified by the algorithm as having poor cooling performance. This clearly shows that it is of major interest to the district heating companies to identify the installations with poor cooling performance rapidly and automatically, in order to rectify them as soon as possible.

Keywords: automatic fault detection; district heating; substation performance (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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