Advanced

Automatic Identification of Poorly Performing Substations and Meter Devices. The Future of District Heating Analysis

Davidsson, Kristin LU and Månsson, Sara LU (2016) MVK920 20161
Department of Energy Sciences
Abstract
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... (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)
Abstract (Swedish)
Den svenska fjärrvärmesektorn står idag inför många utmaningar på grund av konkurrens från andra uppvärmingsalternativ
kombinerat med ett minskande värmebehov i byggnader. För att öka dess konkurrenskraft måste fjärrvärmebolagen
utveckla sätt att hålla produktionskostnaden för fjärrvärme på en mer eller mindre konstant nivå. Ett sätt att göra
detta är att öka fjärrvärmesystemens effektivitet. För att kunna göra detta måste företagen identifiera de undercentraler
som har en negativ effekt på fjärrvärmesystemets sammanlagda prestanda. Ett exempel på sådana undercentraler är undercentraler
som har dålig avkylning, vilket innebär att de inte kan utvinna den mängd värme från fjärrvärmesystemet
som de är dimensionerade för. För att... (More)
Den svenska fjärrvärmesektorn står idag inför många utmaningar på grund av konkurrens från andra uppvärmingsalternativ
kombinerat med ett minskande värmebehov i byggnader. För att öka dess konkurrenskraft måste fjärrvärmebolagen
utveckla sätt att hålla produktionskostnaden för fjärrvärme på en mer eller mindre konstant nivå. Ett sätt att göra
detta är att öka fjärrvärmesystemens effektivitet. För att kunna göra detta måste företagen identifiera de undercentraler
som har en negativ effekt på fjärrvärmesystemets sammanlagda prestanda. Ett exempel på sådana undercentraler är undercentraler
som har dålig avkylning, vilket innebär att de inte kan utvinna den mängd värme från fjärrvärmesystemet
som de är dimensionerade för. För att kompensera för den låga utvinningen av värme måste en större mängd varmt
vatten passera genom undercentralerna. Som ett resultat av detta blir fjärrvärmesystemet ineffektivt, vilket leder till
ökade produktionskostnader.

För att kunna hitta undercentraler med dålig avkylning måste mätdata från dessa undersökas och analyseras. Idag utför
de flesta fjärrvärmebolag denna analys manuellt, vilket är både tidskrävande och ineffektivt. Därför är många företag
nu intresserade av att utveckla automatiska metoder för att identifiera dåligt fungerande undercentraler.

Syftet med den här studien är att utveckla ett program som automatiskt analyserar 3000 undercentraler och identifierar
de undercentraler som har dålig avkylning. 3000 undercentraler genererar en stor mängd data och för att kunna utföra
en korrekt analys måste datan som analyseras vara av god kvalitet och inte innehålla några avvikande mätvärden. På
grund av dessa krav på mätdatan utvecklar denna studie även ett program som undersöker mätdata och identifierar
samt hanterar möjliga avvikelser. För att identifiera avvikelserna används timvisa mätarställningar då dessa innehåller
en stor mängd information om mätarnas prestanda. Ett antal ofta förekommande avvikelser identifieras och hanteras
utefter vilken påverkan de har på mätdatan innan de timvisa mätarställningarna omvandlas till dagliga förbrukningsvärden.
Dessa värden används sedan i analysen av undercentraler för att identifiera dåligt fungerande undercentraler.

För att identifiera undercentraler med dålig avkylning måste ett referensfall skapas som baseras på de dagliga förbrukningsvärdena
för undercentraler med bra avkylning. Varje undercentrals dagliga värden jämförs sedan med referensfallet
och om värdena avviker med mer än en förbestämd toleransnivå, anses undercentralen ha dålig avkylning. Detta utförs
för tre olika signaturer som baseras på värden för energi, avkylning och returtemperatur.

Båda programmen producerar listor som innehåller ID-nummer för undercentraler med dålig avkylning respektive
dåligt fungerande mätare. Listorna som innehåller dåligt fungerande undercentraler skapar en sammanställd lista över
resultatet från de tre signaturerna och ordnar dem efter deras överflöde. Överflöde är en kvantitet som beskriver hur stor
mermängd vatten som passerar genom undercentralerna på grund av deras dåliga avkylning. Listorna som innehåller
dåligt fungerande mätare är uppdelade enligt varje identifierad avvikelse och ordnade efter det sammanlagda antalet
avvikelser.

Resultatlistorna visar att programmen kan identifiera dåligt fungerande undercentraler och mätare. På grund av den
stora mängden data har det inte varit möjligt att manuellt validera hela resultatet. Istället har några stickprov undersökts
som visar att de identifierade avvikelserna i de flesta fallen faktiskt är avvikelser i mätdatan. Stickproven har dock även
visat att programmen inte kan hantera alla olika typer av avvikelser och svårigheter i mätdatan.

Denna studie identifierar dåligt fungerande utrustning i ett fjärrvärmesystem. Resultatet från båda programmen bör ses
som en fingervisning om vilken utrustning som bör undersökas vidare för att förbättra fjärrvärmesystemets prestanda.
Studien undersöker inte den totala inverkan på systemets effektivitet och utför inte heller några kostnadsanalyser för
att undersöka de ekonomiska fördelarna som kan uppstå på grund av en förbättrad systemprestanda. (Less)
Please use this url to cite or link to this publication:
author
Davidsson, Kristin LU and Månsson, Sara LU
supervisor
organization
course
MVK920 20161
year
type
H2 - Master's Degree (Two Years)
subject
keywords
District heating, district heating system, district heating substations, district heating meter devices, poor cooling performance, meter reading abnormalities.
report number
ISRN LUTMDN/TMHP-16/5366-SE
ISSN
0282-1990
language
English
id
8883256
date added to LUP
2016-06-20 11:39:18
date last changed
2016-06-20 11:39:18
@misc{8883256,
  abstract     = {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.},
  author       = {Davidsson, Kristin and Månsson, Sara},
  issn         = {0282-1990},
  keyword      = {District heating,district heating system,district heating substations,district heating meter devices,poor cooling performance,meter reading abnormalities.},
  language     = {eng},
  note         = {Student Paper},
  title        = {Automatic Identification of Poorly Performing Substations and Meter Devices. The Future of District Heating Analysis},
  year         = {2016},
}