Unscented Kalman Filtering for in-situ Bulk Identification of District Heating Meter Temperature Offsets and Service Pipe Insulation Level Detection

Unscented Kalman Filtering is applied to district heating meter data and GIS-data from a utility network to simultaneously identify temperature offsets in utility meters and service pipe insulation. Implementing estimation of the temperature in the main pipe allows for more accurate results, which also respect physical constraints. Unscented Transformations allow for easier implementation of the algorithm compared to existing solutions, as linearization is avoided. Correcting for potential offsets in the temperature measurement of the utility meter allows for a better estimation of the insulation level of the service pipe, improving the ability for the utilities to pinpoint their efforts in optimizing their renovation activities over several areas of interest.

Authors

Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2025-04-07
DOI
https://doi.org/10.1051/epjconf/202532307002/pdf
Primary Topic
Integrated Energy Systems Optimization
Type
article
Field-Weighted Citation Impact
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article

Unscented Kalman Filtering for in-situ Bulk Identification of District Heating Meter Temperature Offsets and Service Pipe Insulation Level Detection

Gertjan Kok, Peter Friis Østergaard, Thomas S. Daugbjerg
Springer Link (Chiba Institute of Technology)
Integrated Energy Systems Optimization
article

Unscented Kalman Filtering for in-situ Bulk Identification of District Heating Meter Temperature Offsets and Service Pipe Insulation Level Detection

Gertjan Kok, Peter Friis Østergaard, Thomas S. Daugbjerg
article en

Abstract

Unscented Kalman Filtering is applied to district heating meter data and GIS-data from a utility network to simultaneously identify temperature offsets in utility meters and service pipe insulation. Implementing estimation of the temperature in the main pipe allows for more accurate results, which also respect physical constraints. Unscented Transformations allow for easier implementation of the algorithm compared to existing solutions, as linearization is avoided. Correcting for potential offsets in the temperature measurement of the utility meter allows for a better estimation of the insulation level of the service pipe, improving the ability for the utilities to pinpoint their efforts in optimizing their renovation activities over several areas of interest.

Springer Link (Chiba Institute of Technology)
Openalex Percentile: Top 37%
Integrated Energy Systems Optimization
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Unscented Kalman Filtering for in-situ Bulk Identification of District Heating Meter Temperature Offsets and Service Pipe Insulation Level Detection — Gertjan Kok, Peter Friis Østergaard, et al. · Springer Link (Chiba Institute of Technology) (2025) | TGRS Research Map | TGRS