Automatic identification of poorly performing substations and meter devices : The future of district heating
(2016) ISRN LUTMDN/TMHP-16/5366-SE.- Abstract
- The district heating sector in Sweden is today facing several challenges due to competition from other heating alterna-tives as well as decreasing heat demand in buildings. To increase its competitiveness, the district heating companieshave to find ways to keep their production costs at a more or less constant level. One way of doing this is to increasethe efficiency of the district heating systems. To be able to do this, the district heating companies need to identifysubstations which have a negative effect on the overall efficiency of the system. An example of these substations aresubstations with poor cooling performance which means that they do not extract as much heat from the district heatingsystem as they are supposed to do. To... (More)
- The district heating sector in Sweden is today facing several challenges due to competition from other heating alterna-tives as well as decreasing heat demand in buildings. To increase its competitiveness, the district heating companieshave to find ways to keep their production costs at a more or less constant level. One way of doing this is to increasethe efficiency of the district heating systems. To be able to do this, the district heating companies need to identifysubstations which have a negative effect on the overall efficiency of the system. An example of these substations aresubstations with poor cooling performance which means that they do not extract as much heat from the district heatingsystem as they are supposed to do. To compensate for this, more hot water needs to pass through the poorly performingsubstations. As a result, the district heating system will be inefficient and this leads to increased production costs.To be able to find the substations with poor cooling performance, the meter reading data of the substations has to beinvestigated and analysed. Today, most district heating companies perform these analyses manually which is both time-consuming and ineffecient. Many companies are now interested in developing automatic methods to identify poorlyperforming substations.The purpose of this study is to develop a substation analysis program which automatically can identify poorly perform-ing substations out of a total number of 3 000 district heating substations. The large amount of substations generate alarge data set, and in order to be able to perform correct analyses, the data which is analysed has to be of good qualityand not contain any abnormalities. Because of this, this study also aims to develop an investigation of data programwhich can identify, and handle, potential abnormalities. To be able to identify abnormalities in the meter reading data,hourly meter readings are used since these contains a large amount of information about the meter devices’ perfor-mance. A number of common abnormalities are identified and handled according to their impact on the data set, beforeconverting the hourly meter readings into daily consumption values. These values are then used in the substation anal-ysis program in order to identify the poorly performing substations.The first step to identify substations with poor cooling performance is to create a reference case based on the dailyconsumption values for substations with good cooling performance. The daily consumption values for each substationare then compared to the reference case. If the values differ with more than a prescribed tolerance, the substation isdeclared as poorly performing. This procedure is performed for three different signatures based on energy, cooling andreturn temperature values.The output from both programs are lists containing ID numbers for poorly performing substations and meter devicesrespectively. The lists containing poorly performing substations compiles the result from the three analysis signaturesand rank them according to their overflow. The overflow is a quantity which describes how much excess water passesthrough the substation in question due to the poor cooling performance. The lists containing poorly performing meterdevices are presented for each identified abnormality and ranked according to the total number of abnormality occa-sions.The output lists show that the programs can identify poorly performing substations and meter devices. Due to thelarge amount of data, it has not been possible to manually validate the entire result. Instead, some samples have beeninvestigated which shows that in most cases, the abnormalities identified in the data investigation program are correctlyidentified. However, this investigation also shows that the programs can not cover all different types of abnormalitiesand difficulties of the data set.This study identifies poorly performing equipment of one district heating system. The results from the programs shouldbe considered as an indication of what equipment should be investigated further in order to improve the system perfor-mance. It does not investigate the overall efficiency of the impact of the equipment on the system, and neither analysethe economical benefits which may arise from an overall improvement of the system performance. (Less)
- Abstract (Swedish)
- The district heating sector in Sweden is today facing several challenges due to competition from other heating alternatives
as well as decreasing heat demand in buildings. To increase its competitiveness, the district heating companies
have to find ways to keep their production costs at a more or less constant level. One way of doing this is to increase
the efficiency of the district heating systems. To be able to do this, the district heating companies need to identify
substations which have a negative effect on the overall efficiency of the system. An example of these substations are
substations with poor cooling performance which means that they do not extract as much heat from the district heating
system as they... (More) - The district heating sector in Sweden is today facing several challenges due to competition from other heating alternatives
as well as decreasing heat demand in buildings. To increase its competitiveness, the district heating companies
have to find ways to keep their production costs at a more or less constant level. One way of doing this is to increase
the efficiency of the district heating systems. To be able to do this, the district heating companies need to identify
substations which have a negative effect on the overall efficiency of the system. An example of these substations are
substations with poor cooling performance which means that they do not extract as much heat from the district heating
system as they are supposed to do. To compensate for this, more hot water needs to pass through the poorly performing
substations. As a result, the district heating system will be inefficient and this leads to increased production costs.
To be able to find the substations with poor cooling performance, the meter reading data of the substations has to be
investigated and analysed. Today, most district heating companies perform these analyses manually which is both timeconsuming
and ineffecient. Many companies are now interested in developing automatic methods to identify poorly
performing substations.
The purpose of this study is to develop a substation analysis program which automatically can identify poorly performing
substations out of a total number of 3 000 district heating substations. The large amount of substations generate a
large data set, and in order to be able to perform correct analyses, the data which is analysed has to be of good quality
and not contain any abnormalities. Because of this, this study also aims to develop an investigation of data program
which can identify, and handle, potential abnormalities. To be able to identify abnormalities in the meter reading data,
hourly meter readings are used since these contains a large amount of information about the meter devices’ performance.
A number of common abnormalities are identified and handled according to their impact on the data set, before
converting the hourly meter readings into daily consumption values. These values are then used in the substation analysis
program in order to identify the poorly performing substations.
The first step to identify substations with poor cooling performance is to create a reference case based on the daily
consumption values for substations with good cooling performance. The daily consumption values for each substation
are then compared to the reference case. If the values differ with more than a prescribed tolerance, the substation is
declared as poorly performing. This procedure is performed for three different signatures based on energy, cooling and
return temperature values.
The output from both programs are lists containing ID numbers for poorly performing substations and meter devices
respectively. The lists containing poorly performing substations compiles the result from the three analysis signatures
and rank them according to their overflow. The overflow is a quantity which describes how much excess water passes
through the substation in question due to the poor cooling performance. The lists containing poorly performing meter
devices are presented for each identified abnormality and ranked according to the total number of abnormality occasions.
The output lists show that the programs can identify poorly performing substations and meter devices. Due to the
large amount of data, it has not been possible to manually validate the entire result. Instead, some samples have been
investigated which shows that in most cases, the abnormalities identified in the data investigation program are correctly
identified. However, this investigation also shows that the programs can not cover all different types of abnormalities
and difficulties of the data set.
This study identifies poorly performing equipment of one district heating system. The results from the programs should
be considered as an indication of what equipment should be investigated further in order to improve the system performance.
It does not investigate the overall efficiency of the impact of the equipment on the system, and neither analyse
the economical benefits which may arise from an overall improvement of the system performance. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/b54600cf-16c9-426b-a281-507e3610662e
- author
- Månsson, Sara LU and Davidsson, Kristin
- supervisor
- organization
- publishing date
- 2016-06-20
- type
- Thesis
- publication status
- published
- subject
- keywords
- District Heating, Meter reading abnormalities, Poor cooling performance, District heating meter device, District heating substations, District heating system
- volume
- ISRN LUTMDN/TMHP-16/5366-SE
- pages
- 64 pages
- publisher
- LTH, Lund University
- language
- English
- LU publication?
- yes
- id
- b54600cf-16c9-426b-a281-507e3610662e
- alternative location
- http://lup.lub.lu.se/student-papers/record/8883256
- date added to LUP
- 2019-01-29 15:03:37
- date last changed
- 2019-04-04 14:43:21
@misc{b54600cf-16c9-426b-a281-507e3610662e,
abstract = {{The district heating sector in Sweden is today facing several challenges due to competition from other heating alterna-tives as well as decreasing heat demand in buildings. To increase its competitiveness, the district heating companieshave to find ways to keep their production costs at a more or less constant level. One way of doing this is to increasethe efficiency of the district heating systems. To be able to do this, the district heating companies need to identifysubstations which have a negative effect on the overall efficiency of the system. An example of these substations aresubstations with poor cooling performance which means that they do not extract as much heat from the district heatingsystem as they are supposed to do. To compensate for this, more hot water needs to pass through the poorly performingsubstations. As a result, the district heating system will be inefficient and this leads to increased production costs.To be able to find the substations with poor cooling performance, the meter reading data of the substations has to beinvestigated and analysed. Today, most district heating companies perform these analyses manually which is both time-consuming and ineffecient. Many companies are now interested in developing automatic methods to identify poorlyperforming substations.The purpose of this study is to develop a substation analysis program which automatically can identify poorly perform-ing substations out of a total number of 3 000 district heating substations. The large amount of substations generate alarge data set, and in order to be able to perform correct analyses, the data which is analysed has to be of good qualityand not contain any abnormalities. Because of this, this study also aims to develop an investigation of data programwhich can identify, and handle, potential abnormalities. To be able to identify abnormalities in the meter reading data,hourly meter readings are used since these contains a large amount of information about the meter devices’ perfor-mance. A number of common abnormalities are identified and handled according to their impact on the data set, beforeconverting the hourly meter readings into daily consumption values. These values are then used in the substation anal-ysis program in order to identify the poorly performing substations.The first step to identify substations with poor cooling performance is to create a reference case based on the dailyconsumption values for substations with good cooling performance. The daily consumption values for each substationare then compared to the reference case. If the values differ with more than a prescribed tolerance, the substation isdeclared as poorly performing. This procedure is performed for three different signatures based on energy, cooling andreturn temperature values.The output from both programs are lists containing ID numbers for poorly performing substations and meter devicesrespectively. The lists containing poorly performing substations compiles the result from the three analysis signaturesand rank them according to their overflow. The overflow is a quantity which describes how much excess water passesthrough the substation in question due to the poor cooling performance. The lists containing poorly performing meterdevices are presented for each identified abnormality and ranked according to the total number of abnormality occa-sions.The output lists show that the programs can identify poorly performing substations and meter devices. Due to thelarge amount of data, it has not been possible to manually validate the entire result. Instead, some samples have beeninvestigated which shows that in most cases, the abnormalities identified in the data investigation program are correctlyidentified. However, this investigation also shows that the programs can not cover all different types of abnormalitiesand difficulties of the data set.This study identifies poorly performing equipment of one district heating system. The results from the programs shouldbe considered as an indication of what equipment should be investigated further in order to improve the system perfor-mance. It does not investigate the overall efficiency of the impact of the equipment on the system, and neither analysethe economical benefits which may arise from an overall improvement of the system performance.}},
author = {{Månsson, Sara and Davidsson, Kristin}},
keywords = {{District Heating; Meter reading abnormalities; Poor cooling performance; District heating meter device; District heating substations; District heating system}},
language = {{eng}},
month = {{06}},
publisher = {{LTH, Lund University}},
title = {{Automatic identification of poorly performing substations and meter devices : The future of district heating}},
url = {{http://lup.lub.lu.se/student-papers/record/8883256}},
volume = {{ISRN LUTMDN/TMHP-16/5366-SE}},
year = {{2016}},
}