Automated Statistical Methods for Fault Detection in District Heating Customer Installations

Månsson, Sara; Davidsson, Kristin; Lauenburg, Patrick; Thern, Marcus

doi:10.3390/en12010113

Cited by 13 publications

(15 citation statements)

References 9 publications

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“…The first step was to develop an initial suggestion for the workflow. This was done by studying the results in research papers [22][23][24][25] to identify relevant aspects of successful fault handling processes. One key aspect investigated in studies [22,23] is to use fault detection methods based on analysis of customer data capable of detecting faults in customer installations rapidly and with high accuracy.…”

Section: Methodsmentioning

confidence: 99%

“…This was done by studying the results in research papers [22][23][24][25] to identify relevant aspects of successful fault handling processes. One key aspect investigated in studies [22,23] is to use fault detection methods based on analysis of customer data capable of detecting faults in customer installations rapidly and with high accuracy. The results from [24] further showed that the DH utilities are interested in using such methods.…”

Section: Methodsmentioning

confidence: 99%

“…This study, therefore, suggests such a workflow for fault handling. The workflow is partly based on the results and conclusions from studies [22][23][24][25], and partly on the results from a workshop conducted with DH experts from several utilities. This methodology made it possible to complement the results from the research papers with expert knowledge from the industry.…”

Section: Introductionmentioning

confidence: 99%

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A Fault Handling Process for Faults in District Heating Customer Installations

et al. 2021

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Faults in district heating (DH) customer installations cause high return temperatures, which have a negative impact on both current and future district heating systems. Thus, there is a need to detect and correct these faults soon after they occur to minimize their impact on the system. This paper, therefore, suggests a fault handling process for the detection and elimination of faults in DH customer installations. The fault handling process is based on customer data analysis since many faults manifest in customer data. The fault handling process was based on an analysis of the results from the previous fault handling studies, as well as conducting a workshop with experts from the DH industry. During the workshop, different organizational and technical challenges related to fault handling were discussed. The results include a presentation of how the utilities are currently working with fault handling. The results also present an analysis of different organizational aspects that would have to be improved to succeed in fault handling. The paper also includes a suggestion for how a fault handling process based on fault detection using data analysis may be designed. This process may be implemented by utilities in both current and future DH systems that interested in working more actively with faults in their customer installations.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Fault Handling Process for Faults in District Heating Customer Installations

et al. 2021

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“…Many district heating operators gather hourly values to centralized databases from their heat billing meters (Gadd and Werner, 2015;Sandin et al, 2013). These hourly readings are a valuable information source for fault detection and energy performance assessment of the district heating substations and the buildings they serve (Gadd and Werner, 2015;Mansson et al, 2019;Sandin et al, 2013). In Sweden, district heating operators are required to share daily meter readings to their customers (EIFS 2014:2), while hourly values only must be provided if these are used for billing.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty in Hourly Readings from District Heat Billing Meters

Lundström¹,

Dahlquist²

2020

Linköping Electronic Conference Proceedings

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Hourly energy readings from heat billing meters are valuable data source for the energy performance assessment of district heating substations and the buildings they serve. The quality of such analyses is bounded by the accuracy of the hourly readings. Thus, assessing the accuracy of the hourly heat meter readings is a necessary (but often overlooked) first step to ensure qualitative subsequent analyses. Due to often limited bandwidth capacity hourly readings are quantized before transmission, which can cause severe information loss. In this paper we study 266 Swedish heat meters and assess the quantization effect by information entropy ranking. Further, a detailed comparison is conducted with three heat meters with typically occurring quantization errors. Uncertainty due to the quantization effect is compared with the uncertainty due to typical accuracy of the meter instrumentation. A method to conflate information from both energy readings and energy calculated from flow and temperature readings is developed. The developed conflation method is shown to be able to decrease uncertainty for heat meters with severely quantized energy readings. However, it is concluded that a preferable approach is to work with the heat meter infrastructure to ensure the future recorded readings holds high enough quality to be useful for energy performance assessments with hourly or subhourly readings.

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“…[1,2]. These hourly readings are a valuable information source for fault detection and energy performance assessment of the district heating substations and the buildings they serve [1][2][3]. In Sweden, district heating operators are required to share daily meter readings to their customers [4], while hourly values only must be provided if these are used for billing.…”

mentioning

confidence: 99%