Master Level Thesis

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Master Level Thesis

Energy Efficient Built Environment

No.11, Jun 2019

Energy efficiency trends in large

clusters of residential buildings

Master thesis 15 credits, 2019 Energy Efficient Built Environment Author: Viktor Unéus Supervisor: Mats Rönnelid Examiner: Ewa Wäckelgård Course Code: EG3020 Examination date: 2019-06-04

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Abstract

The aim of this thesis work is to analyse the trends in heat use among Borlänge Energis district heating customer over the last 20 years. Several reports show that in general the buildings stock get more and more efficient, both in Sweden and other European countries, but will the same trend be seen among Borlänge Energis customer?

Data of delivered heat to 324 customers, both single-family houses and multi-family houses, for the period of 1998-2018 is used in this study. The heating that is assumed for domestic hot water is calculated and the heat used for heating is temperature corrected so the heat needed for a normal year could be calculated. The investigated customers are divided into different groups representing various types of buildings with different building years. From this data it’s possible to see trends in heat usage in kWh/building, and year for various types of buildings over the period.

Other studies on how trends for heating usage in buildings have report heating usage in kWh/(m2_{,year). It wasn’t possible in this work to get data of the size of each building, which} means that it’s not possible to compare the result from this study with other studies. However, assuming that the building area have been the same and that no extensions of the buildings have been done during the period, the trend in changed heat use should be the same, unless the result is presented in kWh/m2_{, year and kWh/building, year.}

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Abbreviations

Abbreviation Description

DH District heating

DHW Domestic hot water

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Nomenclature

Symbol Description Unit

D Date

Dend End date for the period in question Dstart Start date for the period in question

DD Degree day °C/day

Haverage Average heating MWh

Hcorrecteed Temperature corrected heating usage MWh HDHW,average The average heating for DHW MWh

HDHW, day Heat for DHW and day MWh

HDHW Heat for DHW in the period MWh

Hend End of the heating trend MWh

HStart Start of the heating trend MWh

k Constant for DHW

n Numbers of days in the month

x year

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1 Introduction

In this thesis work together with Borlänge Energi (The local energy company) and Sweco the task is to analyse the trends among the district heating (DH) customers need of heating. Borlänge Energi has 6 534 DH connections including 5 695 single-family households and 784 connections for companies where multi-family houses includes [1]. This thesis work will focus on single and multi-family houses. While Borlänge Energi has data back until 1998 saved, they haven’t analysed how the energy need among their customers has change over time. Since the data from each customer needs to be processed before it can be used, 345 DH connections has been chosen which will represent different areas. In their data it’s possible to know if it’s a private or company customer where multi-family households used to be owned by companies.

In Table 1 the heat sources for the DH network in Borlänge under 2017 is listed [2]. As can be seen household waste, biomass and waste heat from the industri accounts for a large part of the total heat delivered to the customer. The emissions from DH is specified to 68 CO2 -eq/kWh for combustion and 4 CO2-eq/kWh for transport and production of the fuels. A majority of this emissions is coming from burning the waste.

Table 1, fuel for DH in Borlänge 2017

Fuel Share

Waste heat from the industry 20.4 %

Smoke condensation 1.5 %

Net heat from heatpumps 0.5 %

Houshold w aste 39.3 %

Landfill and sewage gas 0.6 %

Bio fuel 34.7 %

Electricity to heatpumps, electricity boiler etc 1.8 %

Fossil fuel 0.5 %

Other 0.7 %

While there are some old buildings being replaced by new ones most of the buildings that we will use in the coming decades are already built.

In 2018 there were 2 081 112 single-family houses in Sweden which is an increase of 63 048 single-family houses over the last three years [3]. During the same period there were 146 618 newly houses built which means that 83570 or 4.1 % of the houses was replaced by a new house [4]. With this pace all single-family houses would be replaced every 75 year.

The conclusion of this means that althrough it’s good that new buildings comply with the new stricter rules regarding energy use, the existing buildings also need to be improved if we want to make a greater impact on the total usage of heating in buildings.

The rules for energy efficiency in new buildings get stricter over time, but as a majority, of the buildings that we will use and live in the upcoming decades, are already built. Even thought it’s very interesting to know if any energy efficiency actions are done in existing buildings.

Aims and Objectives

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2 Literature Review

Energy authority’s report from 2018

The energy authority in Sweden realeses a report every year that includes energy usage in several various sectors as, transport, heating, industry etc and compare it with older data. According to that report the delivered energy that is used for heating building and DHW per square meter in Sweden have dropped in detached houses, multi-family houses and facilities between 1995 and 2016 [5].

After temperature correction the energy delivered for heating need to multi-family houses have dropped from around 185 kWh/m2_{to 145 kWh/m}2_{, from 170 kWh/m}2_{to 140} kWh/m2_{in facilities and 165 kWh/m}2_{to 110 kWh/m}2_{in single-family houses. This} corresponds to a decrease of 1.34 % per year in multi-family houses, 1.07 % per year in facilities and 2.23 % per year in single-family houses.

The main reason for this is the installation of heat pumps, energy efficiency, going from oil to electricity or DH and new buildings [5]. The presented numbers are counted delivered energy so it doesn’t consider losses within the DH system or the energy that the heat pump takes up either from the air or the ground. For all buildings together the total delivered energy for heating has gone down from 170 kWh/m2_{to 125 kWh/m}2_{but with their} assumption of heating pumps the used energy have only dropped to around 150 kWh/m2_. This means that the average delivered energy for heating have dropped around 2 kWh/(m2_{,year). The actual used energy have dropped about 1.3 kWh/(m}2_,year).

If heat pumps aren’t considered the decrease of energy usage for heating in single-family households is 0.9 % and in multi-family households 1.2 % per year under the period 1998-2016 [6].

Heating and cooling energy trends and drivers in Europe

In the article data from several countries in Europe, that has gathered data over a period of twenty years to see trends in energy usage for heating and cooling [7]. The authors have looked at energy usage, residents per household, floor area and gross domestic product to see how trends in several different perspectives. The result show that the energy usage has dropped in both western, eastern and northern Europe when looking at kWh/m2 _{and the} whole europe overall. The need of energy for heating and cooling dropped 1 % per year counted per square meter. If only energy for heating and cooling is considered then the total energy usage increased in the whole Europe between 1990 and 2000, while between 2000 and 2010 it decreased in eastern, northern and western Europe.

In Sweden the energy usage dropped from 229.1 kWh/(m2_{,year) in 1990 to 132.3} kWh/(m2_{,year) in 2010. This corresponds to a 2.71 % decrease per year which is more then} the numbers from the Energy authoruity’s report. It doesn’t mention if it’s delivered energy or used energy.

Statistic energy ratings for Germany: Heat energy consumption trends of the

dwelling stock since 2004

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Simple question, complex answer

This study have looked into how Sweden would be able to lower the heating demand in the building sector by 50 % until 2050, compared to 1995 which was a national goal set in 2005 [9]. The goal that was set didn’t had a clear explanation of what it was calculated on, and it never did, before the goal was removed in 2012, but most engineers in the building industry believed it was kWh/(m2_{*year). A part of this will be achieved by new building being more} efficient but as many buildings are used for a very long time, many of the buildings that exist today will still be used by 2050.

The study was focused on Dalarna county where it was discovered that, by the time the study was done around 2012, nearly 80 % of the buildings were built before 1981. Under the period of 1994 and 2008 around 0.2 % of the multi-family and single houses was renewed per year. For one or two family houses it was 0.2 % in the beginning of the period for later reach 0.4 %. The building type that was most renewed was leisure houses that begin around 0.4 % and in the end of the period was around 0.8 %. These small changes show the importance of carrying throught improvements also to the existing houses, if a bigger impact needs to be achieved for the whole of Sweden. According to the study, if only new built and renovated buildings would be improved it would need a 90 % reduction of energy usage for heating to reach the overall goal of 50 % reduction.

Summary

The different reports points in the same direction, that at least here in Sweden and north of Europe we see that the buildings get more and more energy efficient. Part of this is because of the reduction of average in new buildings but also that there is an improvement among the existing buildings. The pace that old buildings are replaced with new buildings in Sweden are just around 2 %, it’s necessary that there is a big improvement among existing buildings too. According to the report about energy in Europe the square meter per residents have increased from 1990 to 2010 by 5.6 % and statics from Sweden statics show that under the period 2012-2018 the average square meter per resident have gone down slightly from 42 to 41 square meter [10]. It’s right now a too short time period to drag any conclusion and it should be taken into consideration that the Swedish population had it highest increase in population both in actual numbers as in percentage in 2016 in over 200 years [11]. As the population growth have started to decrease now it will be interesting to see if the average floor area per square meter will start to grow again or continue down.

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3 Theory and data anlysis

Data from Borlänge Energi customer is extracted for each indivual where most of the customers have data back until 1998. Under the period 1998 to 2013 the heat usage were read annually but the period between two readings can vary between four and 24 month and are different between varius buildings. This means that it’s hard to calculate and average out usage for a specific year, over several buildings. As an example; when 31 buildings at Spanngatan was summarised the average period between two reading of the delivered heat varied between 341 and 384 days. As all readings where done between December to March this has a great impact of the numbers of degree days and therefore the heating usage.

Availed data

The data available in Borlänge Energis existing system is data of heat delivered to each customer form 1998 until 2018. It contains data from the period from approx 1994-1997, but this is in a format that can’t be available without extensive work (and costs), so this isn’t used in this study. Between 1998 to approx 2013, the energy meters was recorded manually, with dates varying from year to year. After changing meters in 2013, the data of the heat use is reported automatically and summarised for calender year.

For the period 1998-2013 when the meters was read manually, there is also estimated yearly consumption. The difference between this and after last reading and in some cases the numbers of days since last reading. The meter count the amount of kWh of heat which is for both DHW and heating of the building. The readings are done at the heat exchanger which in most cases is for that building only but in areas with multi-family houses one heat exchanger can be for several buildings. In the data it’s only possible to see if it’s a private customer or a company where the multi-family houses use to be owned by a company.

Personal integrity

The information gathered from Borlänge Energi included need of heating per household, which can be seen as personal information. To make sure that this wasn’t spread into this work it was taken away and instead of giving need of heating per building, an average over several buildings have been taken into this calculation. Sometimes the average in an area have incresed or decreased when a bigger or smaller building have been added to the average, but this have been chosen not to be mentioned in this work due to the personal information. The personal information was only available while working in Borlänge Energis system which was only possible to reach while being at their office. The work with the data was done on a computer that was borrowed from Borlänge Energi.

Balance temperature

Even if the residents want to have 20 degrees Celsius in their house or apartment it’s in most cases not needed to start the heater if the ambient temperature is 19 degrees Celsius. The reason for this is that the electronics and the residents generates out some heat which holds the indoor temperature a bit above the outside temperature. For this reason SMHI is calculated the degree days with 17 degrees celcius as the baseline.

Temperature correction

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is counted. Each of these days are then multiplied by the number of degrees that the average temperature is below the balance temperature.

The Swedish meteorological and hydrological institute (SMHI) has also taken forward data for another method called energy index. This is used in a similar way as degree days, but it also takes into consideration the wind, sunshine and how this affect the heating need for the buildings [13]. By taking into considuration the way that a building are used, the thermal specification for building and it’s position, it can give a better temperature correction from year to year. Energy index is often calculated for a specific building but can also be used for an area. In SMHI data they have calculated with a balance temperature of 17 degrees celcius when they count the number of degree days. For Borlänge SMHI are using 4312 degree days as reference year which is the average for the period 1981-2010. For energy index they are using 5065 instead.

Before 2014 each building have different dates for reading the meter in different year and the dates for reading can differ between the buildings as well. It was therefore necessary to calculate the number of degree days individually for each building and year. The method for doing this was by having the number of degree days for each month provided by SMHI and having the dates of each reading. The data from SMHI was however with a monthly resolution which means that for the heating usage before 2014 the number of degree days won’t compensate directly to the heating usage. All the degree days per month is set to the first of each month which means that if the reading was done the second of January one year to the first of January the year after, one month of degree days won’t be counted into the calculation. Without a daily resolution of the degreed days a more exact calculation is hard to get. As this work however looks at the overall trend and the year before will have too many degree days the overall trend won’t be affected.

Hot water

Borlänge Energi delivers heat to their customers and only know how much heat that has been delivered to each building. How big part of which goes to DHW isn’t known. As the heat used for DHW isn’t dependent of the outdoor temperature from year to year, this part shouldn’t be temperature corrected. A standard value that often is used is that 30 % of the total heating need over a year go to DHW [14]. The use of DHW is however not constant over the year, it varies a lot. In Table 2 it’s possible to see how the amount of DWH varies over the year [15]. As the cold-water temperature can change quite a bit over the year, the amount of energy needed to heat up the DWH does however change even more which aren’t considered in this study.

Table 2, variation of DHW usage in relation

Month Relative value

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Since the DHW share of total DH does change over the year it complicate the calculations, when the measured data varies from four to sixteen months. This will have effect of how big part of total heating that are DHW. When the period varies between different buildings it means that every building and year should get an individual value for DHW share to get a more accurate result.

Energy signature

From 2014 most buildings have the readings of heating usage the first day every month which means that it’s much easier to take an average mesure over several buildings and compare the data with weather data. One way to take advantage of this is to calculate the energy signature for buildings [14]. This is to see how a building heating need varies with outdoor temperature and should make it possible to see where the balance temperature for the building or buildings are. As the heating delivered to the building also is for domestic hot water (DHW) there will be a baseload also under hot months. The heating for the building should be linear with the outdoor temperature. To make sure the buildings´ time constant doesn’t affect the results an average of temperature of a month is recommended to be used for this calculation. Here the monthly heating usage will be divided by the number of days in the month and 24 hours to get the power instead of heating per month.

Heating reader

Borlänge Energi use ultrasound meter in most cases and they are exchanged in all buildings every ten years and both before and after the meter is tested. Before they are put into use, they go through three test’s where the flow is full, medium and low. The meter is only allowed to have a two percentages of error on full and medium flows and five percentage of margin of error at low flow. When they are taken out after ten years, they are put through the same three test’s again where they are allowed to have the same margin of error [16]. This is to ensure that the customer shouldn’t be charged too much (or too less) for the heat delivered. In this study, it’s assumed that all meters show the actual value. Since the result shows for a clusters of 20-50 buildings some errors due to too low values for individual buildings is assumed to be compensated for larger values for other buildings.

Energy cost

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Figure 1, how the prices among DH network in Sweden is divided

Disposable income

In Table 3 the disposable income in Borlänge per household is compared to the average in Sweden for everyone above 18 years old. Not only did Borlänge have lower average disposable income in 2011 but the increse is also slower in Borläge compared to Sweden in average. It should however be taken into considuration that there is a difference between disposable income and consumption space after living cost.

Table 3, disposible average annual income per household in Borlänge compare to Sweden in SEK.

Borlänge Sweden 2011 344 400 398 100 2012 355 500 407 300 2013 366 600 418 100 2014 378 700 438 400 2015 389 800 461 700 2016 395 400 470 100 2017 398 400 474 300

Single family house prices

The average price per square meter for single-family houses in Borlänge have under the period May 2018 to April 2019 been 19 647 SEK/m2 _{and in Sweden it’s 24 889 SEK/m}2 [22][23]. While the prices in Sweden on average have gone up 1.1 % under the last 12 months the price have decreased in Borlänge with 0.5%.

Sample size

To make sure that a single or a few customer with different trend when the majority have to high impact on the total results it’s importatant not to take too few samples. A common practise is to use at least 30 samples in a study to neglect those impacts and still be able to see their overall results or trends [24]. For this reason at least 30 customer have been taken out in most inspected areas. For multi-family connections the sample was smaller as there in most cases are a lot fewer connections then 30 in a area.

Investigater areas

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been different in several areas where the houses is of different types and from various time periods. The areas have been taken out to both get that each area have similar building types and are built in about the same time. The different areas is then taken out to represent different decades of building to see if there is any differences between older and newer buildings.

Hytting - single-family houses

In Hytting the most buildings are one-floor houses while there exist a few one and a half floor houses. These buildings are mostly built in the late 1960th_{and here there are 56 DH} customers [25]. The first DH customers connected 2001 with 29 customers that where connected to the DH network. In 2002 the number was 34 and it steadily increases to 56 in 2013. In this area all customer along the roads Debattvägen, Ordsvägen and Prosavägen was used.

Lergatan - terrace houses

On Lergatan there is a few newer terraced houses from 2007, that where picked out which leads to a short period of data. In 2007 there was five DH customers, but it increased to nine in 2010, where it has been the same until 2018. The household that was used was between 19A and 23C. Here new houses was picked out to see if there was any differences between newer and older buildings. Because of this the sample was a lot smaller then the other cases.

Mjälga East - single-family houses

Here it’s mostly one and a half and two floor houses from the 1940th_{[25]. In Mjälga east} there is in 1998 only nineteen DH customers, which increase to 50 in 2010 and thereafter is steady until 2018. Here data from customer along the roads Grönstigen, Tundalsgatan and Tvärgatan was used.

Mjälga west – single-family houses

In the area Mjälga west there where is 1998 a total of 24 DH customers and that grow steadily up to 53 in 2011, where it’s been stable until 2018. There are one and one and a half floor houses from the 1970s [25]. The roads that was picked out here was Gårdavägen, Hornsgatan, Lingatan, Sockenvägen and Åkergatan.

Mjälga west - multi-family houses

There are also some multi-family households that will be investigated separately in Mjälga west and there where seven buildings in 1998 connected to DH but this later increased to nine in 2002 and later to ten in 2005.

Skräddarbacken – single-family houses

The area Skräddarbacken is located in the west of Borlänge and it was built in the 1990th [25]. Here 56 DH customers have been taken into account which all have data under the period 1998 and 2018. There is a mix of one and one and a half floor houses in the area with some two-floor houses. In this area 56 housholds along Lapp Anders väg was used for data analysis.

Spanngatan – single-family houses

East of Borlänge city centre Spanngatan have 31 one and a half floor houses from the 1970th [25]. Of these, there is data for 30 DH customers since 1998 and then one more was added 2000.

Stora Tuna – single-family house

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connected by 2003 is considered. For this reason only 73 customer is used in the staticts. The road that was picked out here for use was Drabantgatan, Knecktgatan, Marchegatan, Rotegatan and Väpnargatan.

Tjärna Ängar – multi-family houses

Here there are 8 connections where every connection is heating several buildings, which was built in the 1970th_{[26]. The buildings that are connected to the DH are all along the roads} Klöverstigen and Kornstigen.

Table 4, investigetad areas

Area Type of households Number of DH customer investigated

Building year

Hytting Single-family houses 32 1960th

Lergatan Terraced houses 9 (5 from start) 2000th

Mjälga east Single-family houses 50 (19 from start) 1940th Mjälga west Single-family houses 53 (24 from start) 1970th

Mjälga west Multi-family houses 7 1970th

Skräddarbacken Single-family houses 56 1990th

Spanngatan Single-family houses 30 1970th

Stora Tuna Single-family houses 73 (37 from start) 1950th

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4 Method

Need of heating per building

As it wasn’t possible to get out the number of square meter per building in this work the need of heating will be calculated per connection. As the overall trend was what was supposed to be investigated in this thesis work and not todays efficiency the most interesting value are the percentage of improvements. For this it doesn’t matter if the need of heating is per building or per square meter as long as the buildings have the same size. For this reason it has been choosen not to calculated the overall changes in all investigated buildings but instead the area with similar houses seperate.

Calculation

To calculate the heating usage for an set of houses the method used in this work is made in several steps. In Figure 2 there is a flowchart over the steps.

Step 1: As the buildings have different dates for readings the heating usage is taking out for each building every year. Here it’s the value standing on the meter that is used after subtracting the last used value. With large variations of time between the readings, the readings with closese to one year between is taken out. If the meter has been changed during this time, this is calculated separately. The dates which is used is noted. This is done on all buildings in an area.

Step 2: The second step is to take the average heating usage as Haverage for a building or area for the years where the exact values from first of January to last of December, in most case 2014-2018. This is later multiplied with 0.3 to get the amount of heat used for DHW per year as HDHW,Average. The calculated value for the heat used is then divided with 12 and n which is the amount of days in the specific month. This is then multiplied with the factor found in table 1 as k to get an estimated value of the use of DHW each day. The equation 4.1 and 4.2 show the calcualtions.

𝐻𝐷𝐻𝑊,𝑎𝑣𝑒𝑟𝑎𝑔𝑒 = 𝐻𝑎𝑣𝑒𝑟𝑎𝑔𝑒 ∗ 0.3 𝑒𝑞𝑢𝑎𝑡𝑖𝑜𝑛 4.1

𝐻_{𝐷𝐻𝑊,𝑑𝑎𝑦} = 𝑘 ∗ 𝐻𝐷𝐻𝑊,𝑎𝑣𝑒𝑟𝑎𝑔𝑒

12 ∗ 𝑛 𝑒𝑞𝑢𝑎𝑡𝑖𝑜𝑛 4.2 Among the Mjälga multi-family houses the DHW is calculated for each house separately. The reason for this is that the total heating need for each building varies a lot which leads to negative values if 30 % of the average for the whole area is used.

Step 3: With the dates from the first step and the daily DHW usage calculated in the second step, the total DHW for the whole period is calculated.

𝐻𝐷𝐻𝑊 = ∑ 𝐻𝐷𝐻𝑊,𝑑𝑎𝑦 <𝐷𝐸𝑛𝑑

𝐷𝑠𝑡𝑎𝑟𝑡

𝑒𝑞𝑢𝑎𝑡𝑖𝑜𝑛 4.3

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𝐷𝐷_{𝑝𝑒𝑟𝑖𝑜𝑑}= ∑ 𝐷𝐷

<𝐷𝑒𝑛𝑑

𝐷𝑠𝑡𝑎𝑟𝑡

Step 5: In this step the amount of heat used for DHW in step 3 is first subtracted from the measured values to get the amount of energy used for heating the building. The difference here is then divided by the number of degree days that is calculated in step 4 before multiplied with 4312, which is the number of degree days in the reference year. After that the amount of heat calculated for DHW in step 2 is added.

𝐻𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑒𝑑 =

(𝐻_{𝑚𝑒𝑠𝑢𝑟𝑒𝑑}− 𝐻_𝐷𝐻𝑊)

𝐷𝐷𝑝𝑒𝑟𝑖𝑜𝑑

∗ 4312 + 𝐸𝐷𝐻𝑊,𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑒𝑞𝑢𝑎𝑡𝑖𝑜𝑛 4.5

Step 6: The calculations in step 1 to 6 is done on all buildings for every year. By then taking an average over all buildings for each year the results will show how the trend for the area has been over the whole period of time.

To get the heating trends corrected after energy index instead the same method is used with the two changes. In step 4 the formula summerizing energy index for each period instead of degree days. In step five the value is multiplied with 5065 as the reference year for energy index instead of 4312 for degree days.

As there can be small variation from year to year it’s the overall trend that will be taken out in this thesis. Excel can make a linear regression of the made diagram and then the equation of this is used. The term for the first year is given as parameter Hstart in the equation while the parameter A, gives the linere changes per year as in equation 4.6.

𝑦 = 𝐴𝑥 + 𝐻𝑠𝑡𝑎𝑟𝑡 𝑒𝑞𝑢𝑎𝑡𝑖𝑜𝑚 4.6

The second step is to calculate need of heating in the 2018 according to the linear trend. To get this equation 4.7 is used where the trend is multiplied with the number of years.

𝐻_𝑒𝑛𝑑 = 𝐻_{𝑠𝑡𝑎𝑟𝑡}+ 𝐴 ∗ 𝑥 𝑒𝑞𝑢𝑎𝑡𝑖𝑜𝑛 4.7

To get the average decrease of energy per year equation 4.8 is used to get the decrease in percentage.

𝑥_{𝑐ℎ𝑎𝑛𝑔𝑒} = 1 − √𝐻𝑒𝑛𝑑

𝐻_{𝑠𝑡𝑎𝑟𝑡}

𝑥

Margin of error in the calculations

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that building for 2002 will be counted in the average of 2003 for the other buildings. It will also look like that building was connected one year later then the other buildings.

If the time period is two years then the input could be split in two values and a date could be added in the middle to get a rough calculation per year. It does however become a problem to do this if it’s around 18 months as it would mean that the measurement could be over two summers or two winters. This means that just setting a middle date and split the energy usage in two can give completely different results after temperature corrections. For this reason those won’t be split as there are to high uncertainties in splitting up the heating usage.

Equation 4.5 Dates Calculate average heating

usage for last few years With complete data

(2014-2018)

DHW monthly variation, table

2 kWh used and dates

for measurement period

Calculate amount of DHW for the measurement period, 4.3 Calculate degree days

for measured period, equ 4.4

Calculate daily mean value for heating of

DHW, equ 4.1, 4.2

Subtract DHWfrom total heat used during the measurement period

Calcualte kWh used for heating per degree

day

Multiply with number of degree days for reference year

Add DHW for base year

Flow chart for temperature correction of annual heat use in

builings

Corrected use of heating per year.

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5 Results

Need of heating

Single-family houses

Hytting

Among the 32 customers that are connected from 2001 the average heating usage is going down until 2008 before it starts to rise until it reaches the highest average heating usage in 2010 before it then starts going down again 2013. After that it’s stable between 2014 and 2018. The overall trend is that the need of heating increse by 0.1 % per year. See Figure 3.

Figure 3, average need of heating for Hytting

If all the 56 customers are considered the overall trend is that the need of heating among the buildings goes down 0.2 % per year and building. This can be that either the new houses connected over time is smaller or just more energy efficient. See Figure 4.

Figure 4, average need of heating when new customer is considered

y = 0,034x + 20,554 R² = 0,0416 0 5 10 15 20 25 MW h/ bui ldi ng

Energy index Degree days Linear (Degree days)

y = -0,0378x + 21,001 R² = 0,0487 0 10 20 30 40 50 60 0 5 10 15 20 25 B ui ldi ng s MW h/ bui ldi ng

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Lergatan

The need for heating decreese and increase between 2007 and 2011. It reaches the lowest value in 2013 before it increases nearly every year into end of 2018 with an average need of heating in 2018 close to the top in 2008. See Figure 5.

Figure 5, average need of heating on Lergatan

Mjälga east

The average heating usage is at it’s highest in 1998 but it drops a lot to 1999 when several new customers are added. Over the period 1999-2010 the several new customers are added nearly every year while the period starts with a small increase of heating usage per customer before it goes down some. From 2010 and forward the number of costumers is stable and the need of heating have only small variations. See Figure 6

Figure 6, need of heating among customer in Mjälga East

Mjälga west

As the number of customers increases steadily especially between 1998 and 2006 while the average heating usage is quite stable it’s hard to draw any conclusion from the development during this period. In the period after 2006 the average need of heating is stable with very little variation. See Figure 7

y = -0,0313x + 7,5558 R² = 0,0529 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Bui ldi ngs MW h/ bui ldi ng

Number of buildings Energy index Degree days Linear (Degree days)

y = -0,0578x + 20,848 R² = 0,2517 0 10 20 30 40 50 60 0 5 10 15 20 25 B ui ldi ng s MW h/ bu ildi ng

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Figure 7, need of heating in Mjälga west

Skräddarbacken

With small variations the overall trend in Skräddarbacken has been that the average need of heating in the buildings are going down slowly. See Figure 8.

Figure 8, average need of heating in Skräddarbacken

Spanngatan

As some customers have a very high consumption registered in 1998 which can be due to several different reasons the diagram is from 1999. On average the heating usage has gone down but there is a peak in 2002, which is a single user that had really high consumption that year which manages to draw up the average. There is also a small peak in 2012 before it goes down again in 2013. See Figure 9.

y = -0,1303x + 27,649 R² = 0,2991 0 10 20 30 40 50 60 0 5 10 15 20 25 30 35 B ui ldi ng s M W h/bui ldi ng

Number of buildings Energy index Degreee days Linear (Degreee days)

y = -0,0666x + 18,517 R² = 0,4469 0 2 4 6 8 10 12 14 16 18 20 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 MW h/ bui ldi ng

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Figure 9, average need for heating on Spanngatan

Stora Tuna

The average need for heating in the households went down a little in the period 2002 until 2007 before it shifts to increase until 2010. After that it decreased again until 2013 before increase a little bit the coming years until 2018. The decrease here is 0.1 % per year among all connections but if only the 73 connections that have data from 2003 are considered and the energy decrease is calculated from 2003 then the trend is flat. See Figure 10.

Figure 10, average need for heating in Stora Tuna Multi-family houses

Mjälga west

Here there where several buildings added over time but if only the connections that are from 1998 or before the heating demand chifts from year to year. The overall trend is that the need of heating goes down by 0.4% per year. See Figure 11.

y = -0,0663x + 21,72 R² = 0,1851 0 5 10 15 20 25 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 MW h/ bui ldi ng

Energy index Degree days Linear (Degree days)

y = 0,0067x + 20,031 R² = 0,0036 0 10 20 30 40 50 60 70 80 0 5 10 15 20 25 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 B ui ldi ng s Mwh/ bui ldi ng Year

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Figure 11, need of heating for multi-familyhouses in Mjälga west

Tjärna Ängar

After decreasing and increasing back an forth in the years 1998-2002 it decreases during the period 2002 to 2008. After that the overall trend was flat with some small increase and decreases. See Figure 12.

Figure 12, average need for heating in Tjärna Ängar

Summary

The overall trend for most areas is that the heating demand is going down during the 20-year period. As in Table 5 the decrease is largest among the single-family houses in Mjälga west but here new buildings have been added over time. Among the areas with constant number of connections the area with highest decrease is Tjärna Ängar with an average decrease of 0.4 %. y = -0,4349x + 102,66 R² = 0,2139 0 20 40 60 80 100 120 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 MW H /c o nnec ti o n

Energy Index Degree days Linear (Degree days) Linear (Degree days)

y = -6,9038x + 1673,9 R² = 0,1786 0 500 1000 1500 2000 2500 MW h/ co nnec ti o n

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Table 5, changes among the areas with constant number of connedtions(SF=single-family houses, MF=multi-family houses, TH=Thearace houses)

Area Building year Start year Connections Change per year

Determination

Hytting SF 1960th ₂₀₀₁ ₃₂ _{+0.2 %} _0.0416

Lergatan TH 2000th ₂₀₀₇ _{9 (5 from start)} _{-0.4 %} _0.0529

Mjälga east SF 1940th ₁₉₉₈ _{50 (19 from start) -0.3 %} _0.2517 Mjälga west SF 1970th ₁₉₉₈ _{53 (24 from start) -0.5 %} _0.2991

Skräddarbacken SF 1990th ₁₉₉₈ ₅₆ _{-0.4 %} _0.4469

Spanngatan SF 1970th ₁₉₉₉ ₃₀ _{-0.3 %} _0.1851

Stora Tuna SF 1950th ₂₀₀₂ _{73 (37 from start) 0.0 %} _0.0036

Mjälga MF 1970th ₁₉₉₈ ₇ _{-0.4 %} _0.2139

Tjärna Ängar MF 1970th ₁₉₉₈ ₈ _{-0.4 %} _0.1786

Energy signature

Then looking at the energy signature it’s only possible to look at the period from 2014 to 2018 as that’s the only period where it exists monthly values. The problem however is that under this period there is only two months where the average temperature is over 16.3°C. This means that it’s possible to see the need for heating is flattening out to what should be only hot water but with only two months it’s too little to get a really good idea. It’s however possible to see differences between various residential building areas of how much the energy increases with lower temperatures.

Table 6, describe how the heating needs change with the temperature

Area Building year Heating increase per degree Celsius [W]

Hytting 1960th _-192 Lergatan 2000th _-65 Mjälga east 1940th _-188 Mjälga west 1970th _-237 Skräddarbacken 1990th _-143 Spanngatan 1970th _-173 Stora Tuna 1950th _-189 Tjärna Ängar 1970th _-8760

In Table 6 it’s clearly Lergatan that stands out with a low heating increase when the temperatures drop. Among the other Skräddarbacken also has low increse of heating need with lower temperature, while there are small differences between the other areas except Mjälga West. Mjälga West is the area where the heating need increases the most, when the temperature decreas in the winter. As Tjärna Ängar is several mulitfamily houses per connection it’s natural that it has a lot higher increase when the temperature decreases. See more in Appendix 1, Energy signature.

For the balance temperature it’s a bit hard to get an exact value as there is only two months with an average temperature above 16.3°C but most areas seems to have their balance temperature around 16-17°C. But also here, Lergatan stands out as it looks like it’s balance temperature is just over 15°C. This can be seen as all months with a temperature above 15 degrees have their heating usage above the trend line in opposite to the other cases.

Sensitivity analysis

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over the year is assumed. To see if this assumption can have an impact on the overall results they are both tested on two different areas.

5.3.1. Change of DHW share

In Figure 13 the amount of heating that goes to DHW has been changed to see how it affects the result after temperature correction on Spanngatan. In this case degree days are used for temperature correction.

Figure 13, need of heating after temperature correction with different share assumed for DHW

5.3.2. Change to constant DHW

In section 3.5 data have been taken for how the amount of DHW change over the year in a household. This means that if the time period between the readings for a building are between June to August or December to February different amount of energy should be subtracted before temperature correction is done.

In Figure 14 however it’s possible to see that if the DHW is changed to be split equally over the year it has very little effect on the end result and the overall trend. Note that the y-axel doesn’t start at zero.

Figure 14, difference between constant DHW and varide according to table 1

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5.3.3. Conclusion

Both the share of heating for DHW and how it was divided over the year, were areas with big uncertainties and still is. As it’s assumed that the DHW is the same over all years even if the total energy usage change. It does however seams like these two areas have very little impact on the end results.

5.3.4. Change balance temperature

On Lergatan it was possible to see that the energy signature curve broke at a lower temperature then 17 degree which SMHI have set as balance temperature in their calculations. To see if there was any differences if the balance temperature was changed the number of degree days was recalculated. As SMHI also provided the average temperature for each month and for the reference year it was possible to recalculate the amount of degree days for each month and the reference year for the whole period. This leads to the result seen in Figure 15 where the calculations have been done with a balance temperature of 17 degrees and 15 degrees Celcius.

Figure 15, change of balance temperature on Lergatan

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6 Discussion

As the times of readings for used of heating is different between each building and year at the same time as the data for degree days and energy index only have monthly values, it gives some margin of error in the calculations. Another margin of error is that a good method for taking out DWH from the total heating wasn’t found when the period between the readings varied between 4 to 16 months. As shown in Figure 13 and Figure 14 however it’s not so big difference in the results depending on which share that are selected on Spanngatan. One clear sign of the error in this calculation can be seen in the diagram for Mjälga multi-family households. One of the connections have a lot higher consumption then the others which most likely is because it’s for several buildings. This building however has the readings for 2007 the 4th_{of January and the next one the 1}st_{of January. This means that the degree} days for January 2007 comes on the wrong year and draws down the consumption after temperature correction in 2006 and pushes up the consumption in 2007. If it was split up in several connections the average would show smaller changes as the extra energy would be divided between several buildings, but it would still be a lot of energy that was calculated on wrong amount of degree days. As I however look for the trends over time this shouldn’t have much affect on the end results.

According to the Energy authority report single-family households lower the delivered energy for heating on average of 2.6 % per year from 1998 to 2016 and the same number for multi-family householf is 1.7 % [5]. If however the total used energy is counted, the decrease per square meter is 0.9 % per year for single-family households and 1.2 % among multi-family households [6]. If this is compared with the improvements among Borlänge Energis DH customer in Table 5 it’s still better than any of the areas.

The Energy authorities report are looking at the amount of energy delivered to the buildings for heating usage [5]. This leads to there can be some differences between the real heating need of a building and amount of energy delivered to the building, especially if the heating system is for burning fuel as this won’t give a 100% efficency.

According to the Energy authority report [5] some of the main reasons in Sweden the delivered energy per square meter has gone down over the latest years is heat pumps, new houses, going from oil to other source like DH and energy efficiency improvements. With the data that was accessible it’s not possible to know if someone has had oil before and if so, how much energy they used. In this work the need of heating per household is counted so if new building is added it can lower the average need of heating but if it’s larger than the average building it won’t change so much or may even increase the average.

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One reason for the small changes that has happened among the DH customer can be the low energy price that the district heating customer have. The variable cost for the customer is 0.695 sek/kWh in 2019 including energy taxes for DH and around 1.23 sek/kWh for electricity. This means that it’s quite a bit cheaper than someone having electricity for heating [17]. This also means that any investment that is purely for energy efficiency will have a longer payback period compare to someone that have electricity. A heatpump are often bought since it gives around 3 times the heat compare to the electrcity it uses, which can give good savings if electricity are used for heating. But if you have DH in Borlänge it can be a long payback period as the electricity cost so much more. Even compared with other DH network in Sweden the cost for the end customer is very low looking at the variable cost per kWh.

The fact that the average household in Borlänge have lower disposable income then the average of Sweden, together with low cost of DH could be a reason for that less is done to lower the need of heating. Most renovation for lower the heating demand can have a large investment cost and with low heating cost the return of investment period will be longer. Low heating demand can in normal cases be good when the house will be sold, but if the buyer have knowledge regarding the low heating cost it may not matter so much and the investment cost won’t be paid back.

As people with a lower income use to live in older houses they are also more likely to live in less insulated houses. In Sweden there have been a larger decrease of energy usage in households with low income than households with high income [27]. This should mean that the households in Borlänge should have decrease where energy usage is more then on average in Sweden. Here the low energy prices do however most likely have an affect that as mention before low income households is most likely less interested to do investment with long payback period.

In this work there where considered, that at least 30 connections should be taken from each area, but in most cases a lot more was taken to get better understanding over a bigger area. The problem was that in most areas new customers was added over time even if it was understood that this could have an effect on the results. This could be seen in the end when new customer was removed and only customer with data from the beginning was considered. It would have been better that already from the start only take customer that where connected from the beginning in the areas, even if that would mean that each area would have to be larger or more areas would be taken.

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7 Conclusions

According to the Energy authority’s report multi-family houses had seen a drop of 1.2 % per year during the time period 1998 to 2016 which includes new buildings. In Tjärna Ängar the drop during the time period of 1998 to 2018 was 0.4 % per year. For single-family household the Energy authority’s report shows a reduction of heating usage by 0.9 % per year while in Skräddarbacken and Spanngatan the reduction is 0.4 % and 0.3 %.

In Sweden in general there have been a larger energy improvments in households with lower income compare to households with high income. As Borlänge households have lower income then the average they should therefor have larger then average improvments. But Borlänge Energi does however have one of the lowest DH cost in Sweden which means that investments for energy efficiency will have a longer payback period then on average of Sweden. As a household with low income are less likely to make a investment with a long payback period this could very well be a reason for that very little have happened in Borlänge when it comes to energy efficiency.

Limitations and/or Applicability of the Study

As the times for reading the heating usage varies normally between 8 to 14 months and the resolution for the degree days and energy index from SMHI is per month it means that there is some approximation when the heating usage is temperature corrected. There is also no split between what is used for heating and what is used for DHW which leads to that this has to be approximated. The DHW is counted in the total results but as this shouldn’t be temperature corrected it needs to be separated temperately for the corrections.

A better method of calculate the amount of energy going to DHW would most likely have some impact of the end results as different amount of energy would be temperature corrected. The information regarding DHW variations over the year was in amount of hot water, the amount of energy that needed to heat up the water is however different. This isn’t taken into consideration in this work.

Recommendations for Future Work

To get a better understanding of how different residential areas are compared to each other it would be neccessary to have the heated area of each building. After looking at the energy signature for different areas it seems likely that the new residential areas are more energy efficient than the old ones, but to be sure, heated area would be needed for each building. By having heated area, it would also be possible to make a comparison to other studies and see how the results stands against Sweden in general.

Acknowledgment

I would especially want to thank my supervisor Mats Rönnelid for leading me through this work. Particularly in the beginning of the work he was a great support to get me going and later we have had many interesting discussions.

It was Julia Kosulko from Sweco that came up with the idea of this thesis work and she has later been an additional support through out the thesis work.

The work wouldn’t have been possible without the collaboration from Borlänge Energi where I was able to get my hands on all the data from their DH customer. Within Borlänge Energi I have also got support from several people including Mattias Råbacka, Jakob Vidarsson and Mattias Wettergren.

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Master Level Thesis