Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Fault detection with hourly district energy data: Probabilistic methods and heuristics for automated detection and ranking of anomalies
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0002-4133-3317
2013 (English)Report (Other academic)
Abstract [en]

This project is motivated by the difficulties experienced by district energy utilities to detect faults in large-scale district energy systems. Faults that remain undetected can be costly and the industry loose credibility when customers detect faults and receive incorrect bills. Faults are common in district energy systems due to the high number of substations and instrumentation components. Also, the standard energy-metering instrumentation is designed for low cost and billing, not for automated fault detection. Large variations in building dynamics, building subsystems, human behaviour and the environment make the system complex to model and analyse. Therefore, conventional methods for fault detection are not applicable and the use of ad hoc methods for fault detection often result in numerous false alarms that are costly to analyse and manage. There is a growing interest among the utilities to develop services and functions that are based on data with high temporal resolution. Energy metering regulations are also expected to become more demanding in the future, which drives technology standards towards high-resolution data. This trend results in high rates of streaming data at the management level, which is more challenging to validate. Therefore, more efficient methods for fault detection are needed.This project deals with probabilistic methods for automated anomaly detection that are useful for the identification of faults in large-scale district energy systems. These methods are compatible with the information that is available in modern energy meter data management systems. We focus on methods and heuristics that can be applied automatically with a minimum of human assistance to enable cost-efficient analysis of data. With these methods, operators do not have to rely on ad hoc tests or manual inspection of graphs to detect anomalies in the data. Instead, operators can focus on the analysis of a subset of substations that are identified as abnormal. Intraday and intraweek variations in the thermal load are accounted for by automatically grouping hours of the week with similar thermal load characteristics. Alternatively, intraweek cycles can be accounted for by grouping days of the week with similar characteristics. Robust regression is used to model variable relationships with historical data. A robust outlier detection method is used to determine if variables deviate from the expectation defined by a regression model. Robust statistical methods are used to score outliers, so that outstanding substations can be identified automatically with a ranking procedure. The regression models can also be used for imputation of missing energy metering data, which is a common problem that is not always solved in an accurate way. We also present methods for the detection of long-term drift, which can be costly and otherwise difficult to detect, and the detection of poor precision in measurement data, which for example can result from oversized flow valves, misconfiguration and noise. In addition to fault detection, the proposed methods can be useful also for maintenance scheduling because substations that behave in a way that is consistent with the historical record can be given a lower maintenance priority compared to substations with abnormal behaviour.The proposed methods are studied using hourly data from a population of about one thousand district heating substations. Sample code of key functions is provided. We find that substations with documented faults, unknown faults and abnormal characteristics can be identified in about 5% of the substations. The lack of a well-defined dataset makes the development and evaluation of methods for fault detection challenging, and the fact that historical energy metering data includes abnormal data is often ignored in the literature. The proposed methods need to be implemented in a full-scale district energy management system under the supervision of experienced operators before the effects on the fault detection rate and cost efficiency can be properly evaluated. However, we are convinced that the proposed algorithms can be implemented in present data management systems and that they offer significant advantages over the methods that are commonly used today.

Place, publisher, year, edition, pages
Stockholm: Svensk Fjärrvärme , 2013. , 120 p.
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Industrial Electronics
Identifiers
URN: urn:nbn:se:ltu:diva-22021Local ID: 12f0f9e8-d223-4442-88f8-f3c1af26f95dISBN: 978-91-7381-125-5 (electronic)OAI: oai:DiVA.org:ltu-22021DiVA: diva2:995069
Note
Godkänd; 2013; 20131009 (fresan)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2017-11-24Bibliographically approved

Open Access in DiVA

fulltext(1221 kB)16 downloads
File information
File name FULLTEXT01.pdfFile size 1221 kBChecksum SHA-512
d2e9908445c1ca34ea3c2c08a37d69ad7a76d10d2ef1fc541436c8831a071395814a621b245938288f4a40bacbf6f940c72faff0b91015c1ce31b48bf5eb0640
Type fulltextMimetype application/pdf
fulltext(8098 kB)82 downloads
File information
File name FULLTEXT02.pdfFile size 8098 kBChecksum SHA-512
c6f25bfded41bcb4019a31560677ef172210446ed96a205b76780549ddcd51e8a503023fd157c838876a2f2544f196bb5a9b7be225f07dcbfcf631df71f34069
Type fulltextMimetype application/pdf

Other links

http://www.svenskfjarrvarme.se/Fjarrsyn/Forskning--Resultat/Ny-kunskapresultat/Rapporter/Teknik/Fault-detection-with-hourly-district-data/

Search in DiVA

By author/editor
Sandin, FredrikGustafsson, JonasDelsing, Jerker
By organisation
Embedded Internet Systems Lab
Other Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 98 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 154 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf