Potential Energy Savings from the Increased Application of Heating Controls in Homes

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Report:
Evidence review assessing the potential
energy savings from the increased application
of heating controls in residential properties
across the European Union
May 2014
Prepared for eu.bac

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Contents
1. Executive Summary ....................................................................................................... 2
2. Context........................................................................................................................... 3
3. Methodological Approach............................................................................................... 5
4. Spreadsheet tool............................................................................................................ 7
Calculator overview ........................................................................................................... 7
Global inputs...................................................................................................................... 8
Country data...................................................................................................................... 9
Country calculations..........................................................................................................12
Total outputs.....................................................................................................................14
5. Data collation and evidence review ...............................................................................15
Data and evidence overview.............................................................................................15
EU-wide data sources.......................................................................................................16
Heating system efficiency .................................................................................................18
Control packages and ownership......................................................................................19
Evidence for comfort taking...............................................................................................22
Discount rates...................................................................................................................23
Control Costs....................................................................................................................23
6. Results..........................................................................................................................24
Overview of results ...........................................................................................................24
Baseline and Scenario selection .......................................................................................24
Country and EU-wide results ............................................................................................26
System Balancing and District Heating .............................................................................32
7. Conclusions...................................................................................................................33
8. Recommendations ........................................................................................................34

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1. Executive Summary
This report presents an evidence-based assessment of the potential for energy and carbon
emission savings through the appropriate installation and use of heating controls in all
residential buildings throughout EU member states. This estimation of the potential for
heating controls savings runs from the present day out to the year 2030. The assessment
encompasses potentials at the individual country level as well as a total potential for the
whole EU region.
The project commenced with a review of available data sources and literature. These data
points provided the inputs that underpinned the subsequent analysis and assessment and
the savings. Data coverage, as expected, varied considerably across member states with a
small number of countries having relatively good and comprehensive coverage, but with
many member states having little publically available data on the installed base and/or the
potential within their borders.
The resulting spreadsheet tool developed as the principal output of this project used the
assembled data and evidence to define the housing stock in the EU-27. This included
metrics such as the total floor area, the heating fuel mix, average useful heat demand and
the current penetration of heating controls – covering programmers, thermostats, weather
compensation and thermostatic valve control. These data were combined to form a snapshot
of the present day situation in countries across the European Union.
From the starting point of the base year the tool uses a simple stock model to estimate how
the efficiency of each country’s heating systems will improve as controls are installed. This
allows the calculation of the delivered energy for space heating in each country in future
years, and by comparing a scenario of increased control installations against a baseline of
business as usual (BAU) it is possible to calculate the difference in delivered energy and,
consequently, the CO2 emission and fuel bill savings.
The results show that heating system controls have a significant role to play in reducing
household greenhouse gas emissions and energy bills as well as contributing to European
energy security through reduced household energy demand. Results show that the
enhanced adoption of existing heating control technologies in EU homes lead to peak annual
energy savings of over 50TWh per year, nominal fuel bill savings of around €4.3 billion and
CO2 savings of nearly 12MtCO2 per annum. This annual CO2 emissions saving would be the
equivalent CO2 emissions generated by the space heating of 4.4 million European homes.

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2. Context
Throughout Europe as a whole, buildings consumed 41% of final energy consumption in
20101
. As such, buildings represent the highest final energy end use, followed by transport at
32% and industry at 25%. In the Energy Efficiency Plan 20112
it was acknowledgedthat the
building sector has the highest potential for energy savings, but since 1990 final energy
consumption in buildings generally has risen by 1% each year3
.
A focus onresidential buildingsshows homes to be responsible for approximately 28% of EU
final energy consumption annually, accounting for around two thirds of all building related
consumption. The actual percentage of residential versus non-residential energy use varies
per country, but for the majority of countries residential use represents around 60%, with
some countries split being much higher at 70:30. Breaking this down further into heating
versus power use in the dwelling, within the average European home the proportion of
energy use for the spaceheating accounts for 67% of this consumption4
.
Interestingly, research has found that the highest space heating consumption in dwellings is
not automatically found in countries with the coldest winters. Countries with what are
classified moderate winters, for examples countries such as Ireland and Belgium are found to
have some of the highest energy use values for space heating5
throughout the EU.
Over recent years we have seen a slight decrease in the energy required for space heating.
For example it can be seen that the total consumption for space heating is a mere 3% lower
in 2009 than it was in 1997. However over the last decade a general trend for energy
efficiency in space heating to improve is apparent, and Odyssee data shows a decrease in
energy used (per m2
) of around 1.4% per year since 1997. A proportion of this reduction can
be attributed to the expansion of new build dwellings, which are considered to be around
40% more efficient than existing buildings built before 1990. In the existing stock a
combination of replacement on old inefficient heating appliances, fuel switching and
retrofitting insulation into the existing housing stock are also considered to have played a
part. Changing demographics (leading to lower numbers of occupants in homes) and the
additional demand generated by new build properties have conspired to offset most of the
gains made in household energy efficiency.
1
Up from 37% in 1990, source Eurostat
2
Energy Efficiency Plan 2011, European Commission, COM(2011) 109 final
3
1.5%/year for non-residential and 0.6%/year for residential buildings
4
Energy Efficiency trends in buildings in the EU http://www.odyssee-indicators.org/publications/PDF/Buildings-brochure-
2012.pdf
5
Source: Odyssee data

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An additional significant counteracting effect` that diminishes the gains expected from
general efficiency trends is the impact of ‘comfort taking’ after retrofitting new, more efficient
heating systems and/or insulation measures. Heating behaviours are seen to have a
negative impact on theoretical efficiency gains, with the result that occupants living in well
insulated homes tend to have higher indoor temperatures than poorly insulated homes.
Some of this comfort taking effect can also be linked to the challenge of effectively controlling
heating systems which are fitted with limited controls.
The impact of the comprehensive roll out and adoption of heating control systems throughout
the European Union, in terms of its potential to reduce energy demand for space heating has
not been fully investigated at a country by country level to date, although a recent study in
the UK has demonstrated a technical potential for simple controls on a heating system to
reduce energy use by as much as 40%.
Given the earlier discussion of factors that are counteracting the drive to maximum efficiency
gains, namely larger dwellings, the move to central heating and increased comfort taking; the
installation and effective use of control systems offer an energy efficient solution for all three
of these counteracting forces. A thorough review of the existing evidence into the installed
base, and the potential for increased penetration of sophisticated controls is timely and
necessary if the EU is to have a realistic chance of achieve its 20% reduction in energy use
target by 2020, and the more challenging targets that will need to be set beyond that date.

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3. Methodological Approach
The primary aim of this project was to provide a robust analysis of the potential energy
savings from heating controls based on the currently available evidence base. As part of the
consultation process for the Ecodesign second working plan, industry had put together some
outline calculations on the potential for energy savings from controls. These calculations
were simply derived using percentage savings applied to current energy demand, in effect
assuming that all heating controls across a region are
installed/replaced/upgradedinstantaneously.This simple approach gives a
reasonableinitialestimate but there is a need to develop a more sophisticated calculation
approach where the speed of uptake and installation of such control systems in existing
dwellings can be adjusted and modelled to reflect a more ‘real-life’ scenario. The new
analysis takes into account a more representative, progressive roll out of heating controls
and the impact that these controls will have on energy demand and emissions in the future.
The estimation of potential energy savings from defined uptake rates of four potential
packages of heating system controls was built up from the following four project stages:
 Data gathering
 Evidence analysis
 Spreadsheet tool development
 Testing the tool
The data gathering process adopted three approaches. The first was to explore EU-wide
data resources which focus on energy in general and the residential sector in particular. The
second was to explore in more depth a selection of the largest countries in the EU-27,
consulting the websites of national statistical and energy agencies for information specific to
their country. The final approach was to consult with members of eu.bac to establish whether
they were aware of other data sources (both public and internal) which could be used to
inform the project.Once the data had been collected it was necessary to undertake a process
of analysis and harmonisation. For example where two separate sources for a particular data
point gave different values then one would need to be rejected in preference for the other.
Country level data produced by national energy or statistical bodies was given greater
confidence than EU-wide data produced by third parties and was therefore the primary
source. The data was then converted into useable inputs which could be fed into the
spreadsheet tool. After some experimentation this eventually took the form of a core data
table that captured the main inputs for all countries in a single table.

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The spreadsheet tool was developed in an iterative style with a number of development
phases followed by interactive sessions with eu.bac members. Feedback from these
sessions led to modifications and improvements to the functionality. Throughout this process
it was necessary to make some concessions and trade-offs to avoid the calculation
becoming overly complex or including variables for which good quality data does not yet
exist. Initially the tool was designed for two boiler types and three heating control packages,
but it quickly emerged that this would be insufficient to deal with future advanced heating
controls which are expected to become a bigger feature of the European heating control
market and it was therefore expanded to four heating control packages. Any further
expansionwas considered to add unnecessary complexity given that the poor level of data
coverage means that this additional complexity would be unlikely to improve the results
significantly.
The study focuses on mains gas, LPG and oil heated homes with individual central heating
and one significant simplification which had to be made was the assumption that the mix of
heating fuels would stay broadly consistent over the course of the period under study. In
reality it is expected that in many European countries there will be a substantial amount of
switching from fossil fuelled heating systems to air and ground source heat pump systems. It
is not yet clearly understood what rate of switching will be seen and without this information it
is difficult to incorporate this into the model with confidence in the results it would generate.
The savings have been calculated on a country-by-country basis as well as for the EU as a
whole. This allowsthe savings potential of heating system controls to be communicated to
national governments as well as European government using a consistent calculation
approach. Hence this could help to stimulate a joined up policy approach through
mechanisms such as the EPBD.Furthermore the spreadsheet tool was designed from the
outset to be simple to update should new data become available.
An important input into the tool is the selection of control packages which represent the
current, and likely future, situation in each country. In general this covered control packages
which represent the current mix of controls found in existing installations, those likely to be
installed under current policy approaches, and a reasonable best practice set of controls that
future policy should aspire to drive forward.The final step in the project was to test the tool
and ensure that the outputs it was producing were valid. The tool has gone through a series
of testing phases which ensure that the functionality works correctly. This testing process has
included inspection and verification of the code in the spreadsheet by a project team member
and parallel calculations which corroborate the outputs given by the tool.

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4. Spreadsheet tool
Calculator overview
One of the core elements of this project was the development of a spreadsheet-based
calculation tool which allows the user to estimate the savings which can be achieved by
installing heating controls in the European housing stock. The European housing stock is
expected to evolve significantly over the coming decades in two principal ways.
First there will be substantial numbers of new build dwellings constructed to accommodate
growing populations and the changing demographics of those populations. Secondly, the
energy performance of the existing housing stock will be gradually improved and this will
have an impact on the saving potential of heating system controls. Both of these factors are
addressed by the calculation tool.
The tool allows the user to model the housing stock with two types of boiler (low and high
efficiency) and four control packages (package 1 to 4 with package 1 being the most limited
and package 4 being the most sophisticated).
If desired (or if limited data is available for a particular country is available) it is possible to
model a single boiler type or fewer control packages. This means that it is possible to arrive
at an estimate for the saving using different levels of data availability, although greater
confidence can be attributed to the saving for a country if it has better data availability.
The core of the spreadsheet tool is a simple heating system stock model. This takes a
defined mix of boilers and heating controls in the base year (2010) and a set of assumptions
about future rates of boiler replacement and control upgrades and then calculates what the
mix of boilers and heating controls will be in each year out to 2030.
When combined with the system efficiencies it is possible to derive the average boiler
efficiency of the whole housing stock in each year and, using an estimate of useful heat
demand in each year, the delivered energy can be calculated.
Given that the uptake of heating controls in Europe will take some time it is necessary to
account for other, complementary policies put in place which are expected to reduce heat
demand by improving the energy performance of the residential building stock through, for
example, improved insulation, glazing and draught proofing.
These other efficiency measures will contribute to a reduction in the overall heat demand
which will need to be delivered by the heating system and therefore has a consequential
knock-on effect on the savings from implementing heating control improvements.

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From the outset the tool was developed with a view to making it as easy as possible for
eu.bac to update as new data becomes available. This has been achieved by simplifying the
data input approach.
The tool is also simplified by being agnostic about the typeof boiler or the exact nature of
theheating control package selected as the user specifies the system efficiency of the
combined boiler and heating control package and the penetration of that combination in
2010.
The tool is divided into four main sections:
 Global Inputs – controls variables which are common to all countries and allows the
user to select the countries to be included in the final results
 Country Data – main data input screen for all country-specific data
 Country Calculations – each country has its own calculations tab where the user
can define the scenarios to explore
 Total Outputs – aggregates the results from the individual country tabs to present
total savings
Global inputs
Here the user can set the future heat demand and energy price scenarios and select the
countries to include in the final calculation.
The heat demand scenario defines how quickly improvements to the fabric of buildings
occur. This impacts the trajectory of useful heat demand in dwellings (the amount of heat
required to achieve a comfortable temperature) which has a knock on effect on the savings
which can be achieved by fitting heating controls: if insulation is fitted in a dwelling then the
useful heat demand for that dwelling decreases, the boiler has to deliver less heat to satisfy
that demand and so the impact of the heating control is reduced.
There are four heat demand scenarios which are based on work conducted for the proposed
Energy Efficiency Directivei
and which are defined as follows:
Autonomous scenario:
This scenario considers that technology diffusion is only driven in an autonomous
way. This scenario takes into account the development in terms of demographic
drivers, technology and technology diffusion, renovation and demolition rates, new
builds, etc.

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Low policy intensity scenario (LPI)
This scenario is characterised by low policy intensity, i.e. by considering an additional
technology diffusion of BAT [best available technologies] beyond autonomous
diffusion only driven by increases in market energy prices and comparatively low level
energy efficiency policy measures as in the past in many EU countries.
High policy intensity scenario (HPI)
This scenario describes the additional technology diffusion of best energy saving
technologies (BAT) to the maximum possible, from an economic viewpoint.
Technical scenario:
This scenario considers a full technology diffusion of BAT to the maximum possible.
The maximum, here, corresponds to technical limits.
The Global Inputs screen also allows the user to account for energy price increases by
specifying an annual percentage increase in energy prices.
Finally the user can define which countries should appear in the Total Outputs screen by
adding an ‘X’ beside the country. In order to illustrate the relative size of the countries the %
of total EU-27 dwelling floor area has been given as a guide.
Country data
The Country Data screen contains all of the core data which feeds into the calculations
contained within the individual Country Calculations screens. In order to keep the process of
updating the calculations simple it was decided to collate all of the key data points on one tab
and then have the calculations reference this central repository.
The first (green) table gives the core country data which includes information about the
building stock, heating system efficiency and the mix of heating systems and controls.
Parameter Description
Existing useful heat demand 2010
Delivered heat demand per metre squared for existing properties in 2010
calculated using average system efficiency for existing buildings
Total floor area Total floor area of existing housing stock in 2010
New useful heat demand 2010
Delivered heat demand per metre squared for new build properties in 2010
2010 Households Number of existing households in 2010
Floor Area Per Household (Existing) Average floor area per household of existing dwellings
Floor Area Per Household (New) Average floor area per household of new build dwellings
Demolition Rate Average rate of demolition of existing buildings
% Central Heating (Existing) % of existing homes with individual central heating systems
% Central Heating (New) % of new build homes with individual central heating systems
Central Heating Emission Factor Average emission factor of central heating fuels

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% Condensing Boilers % of existing homes with condensing boilers
Comfort Taking % of savings taken up through increased comfort
Control Knowledge % of savings missed through lack of awareness of control settings
Standard& Control 1 (Efficiency)
Efficiency of standard boiler (often a non-condensing boiler) and four
different control packages in 2010
High Eff & Control 1 (Efficiency)
Efficiency of high efficiency boiler (often a condensing boiler) and four
Standard& Control 1 (Existing)
Penetration of standard boiler and four different control packages in
existing buildings in 2010
High Eff & Control 1 (Existing)
Penetration of high efficiency boiler and four different control packages in
Standard& Control 1 (New)
Penetration of standard boiler and four different control packages in new
buildings in 2010
High Eff & Control 1 (New)
new buildings in 2010
Package 1 to 2 (Controls Only)
Capital cost of upgrading from one package to another when replacing
controls only
Package 1 to 2 (Boiler & Controls)
boiler and controls
below defines the various data items given in this table:
Parameter Description

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Existing useful heat demand 2010
Delivered heat demand per metre squared for existing properties in 2010
Total floor area Total floor area of existing housing stock in 2010
New useful heat demand 2010
Delivered heat demand per metre squared for new build properties in 2010
2010 Households Number of existing households in 2010
Floor Area Per Household (Existing) Average floor area per household of existing dwellings
Floor Area Per Household (New) Average floor area per household of new build dwellings
Demolition Rate Average rate of demolition of existing buildings
% Central Heating (Existing) % of existing homes with individual central heating systems
% Central Heating (New) % of new build homes with individual central heating systems
Central Heating Emission Factor Average emission factor of central heating fuels
% Condensing Boilers % of existing homes with condensing boilers
Comfort Taking % of savings taken up through increased comfort
Control Knowledge % of savings missed through lack of awareness of control settings
Efficiency of standard boiler (often a non-condensing boiler) and four
Efficiency of high efficiency boiler (often a condensing boiler) and four
Penetration of standard boiler and four different control packages in
Penetration of standard boiler and four different control packages in new
buildings in 2010
new buildings in 2010
controls only
boiler and controls
Table 1: Definition of core country data items
The following table (purple) on the calculator covers the rate of new builds in each country
(note that this data currently extrapolates the 2010 new build rate out to 2030).

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The following table (red) gives a metric which is referred to as ‘Control Knowledge’. This
allows the model to apply a reduction factor to the savings to account for consumers’
understanding of how to set their controls. This is a time series variable to allow for improving
understanding of heating controls over time, for example through educational programmes. If
data quantifying the missed saving opportunity in each country can be found then the
impacts of e.g. communications campaigns could be modelled. Currently all values for
control knowledge are set to 100% so no reduction is applied.
The following table (dark blue) has a factor which allows the user to account for the
additional benefit (over and above improving heating system efficiency) of reduced useful
heat demand through zone control. The use of zones reduces useful heat demand by
allowing the householder to better control heat in different areas within the home (for
example maintaining lower temperatures in the bedrooms) and to switch off radiators in
unused rooms. This behaviour reduces the heat delivered by the heating system, reducing
annual demand. As there is little robust data available to define the extent of this saving, this
variable is kept to zero at this time.
The following table (orange) has 2010 heating fuel prices and then uses the energy price
growth variable given in the Global Inputs screen to project energy prices out to 2030.
The final tables (light blue) give the values of the four potential future heat demand reduction
scenarios with the final table giving the values of the specific scenario selected in the Global
Inputs screen.
Country calculations
The individual Country Calculation screens contain the main calculations which produce the
savings estimates. Each country has its own screen where the user can define a baseline
and a scenario to explore.
By selecting the country in the yellow cell at the top left, the relevant country data will be
populated in the green table on the left using data from the data given in the Core Country
Data table. The column of yellow cells headed ‘zoning’ allows the user to indicate which of
the four control packages includes zone control. This forces the model to apply the useful
heat demand reduction factor to this package if applicable.
The pink (Baseline) and blue (Scenario) tables to the right of the country data allow the user
to define the Baseline and Scenario parameters. The Baseline should be populated with
rates of uptake that could be expected assuming that there is no policy intervention other
than what is currently in place, such as building codes. The Scenario should be populated

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with rates of uptake which could be expected with additional policy interventions to drive the
uptake of heating controls.
For simplicity, intervals of five years are used in the Baseline and Scenario inputs table and
the calculation interpolates between these values to calculate savings on an annual basis.
The Baseline and Scenario variables which can be adjusted cover:
 Upgrade Rate Inputs
o The percentage of existing and new build homes which upgrade their boiler
from standard to high efficiency each year
o The percentage of existing and new build homes with standard boilers which
upgrade their heating controls only (i.e. without upgrading their boiler)
o The percentage of existing and new build homes with high efficiency boilers
which upgrade their heating controls only (i.e. without upgrading their boiler)
 Upgrade Market Breakdown Inputs
o The mix of packages which households upgrade to when upgrading their
boilers (which will be influenced by the country’s building codes)
o The mix of packages which households upgrade to when they are upgrading
their heating controls only
The red Housing Stock table below the Scenario table describes the evolution of the housing
stock in this country in terms of the number of homes, floor area and useful heat demand
between 2010 and 2030.
The pink (Baseline) and blue (Scenario) Boiler Stock & Energy tables then describe the
evolution of the mix of boiler and control packages for both existing and new build properties
over time based on the inputs given in the upper pink coloured table. It also gives the stock
average efficiency for existing and new build homes and this is used to calculate the useful
heat demand, delivered energy, CO2 emissions and cost of the energy.
Finally, the green table gives the gross and net savings (net of comfort taking and control
knowledge influences) as well as the marginal capital costs of the controls between the
Baseline and Scenario, while on the left hand side of each Country Calculation there are
graphs which present these results to the user.

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Total outputs
The final screen in the tool, Total Outputs, takes the results of each country’s calculation and
aggregates the costs and savings to give an estimate for all of the countries selected in the
Global Inputs screen. These results are displayed in both tabular and graphical form with the
graphs showing the trend in total annual and cumulative savings over time as well as the
distribution of the savings between the different countries at different points in time.

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5. Data collation and evidence review
Data and evidence overview
The first stage of data collection process was to build a picture of the housing stock and
residential space heating demand in each of the countries being studied. The information
gathered for this part are the data inputs listed below which form the basis of the Country
Data screen within the spreadsheet tool. Data points were needed for each individual EU
country so the search for this data initially focused on sources which listed data on all EU
countries, as opposed to searching country by country.
The second stage of the data collection process was to examine how the different heating
control packages would affect the heat demand. This required a picture to be built of current
heating control ownership in homes with individual central heating and how this might
change over time through boiler replacement and heating controls upgrades and policy
scenarios. From early on it became clear this information wasn’t available on an EU wide
basisso research was undertaken on a country by country basis, reviewingdata from national
statistical and energy agencies, Government funded energy models and research papers or
surveys from specific countries. This task concentrated on the ten highest energy-using EU
countries.
Data was collected primarily through an in-depth desk review of internet based data
sources.Government funded or published sources were the first choice as the data can be
easily verified and is officially sanctioned. This gave us much of the information needed and
also highlighted the gaps in information on heating control penetration. We attempted to fill
the gaps by contacting eu.bac members to identify standard industry assumptions and any
‘grey’ research that could help to build an accurate picture of what controls households have
installed already within their jurisdictions.
A full list of the data sources used in this project can be found in Annex A at the end of this
report but the main sources for the data found in the Country Data tab are as follows:

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Parameter Source
Existing useful heat demand 2010 Energy Efficiency Directive supporting data
Total floor area BPIE
New useful heat demand 2010 Energy Efficiency Directive supporting data
2010 Households ODYSSEE
Floor Area Per Household Derived from total floor area and 2010 household numbers
% Central Heating Entranzeenerdata.eu
Heating fuel mix Entranzeenerdata.eu
Emission Factors Standard emission factors used in IPCC reporting
% Condensing Boilers Entranze.enerdata.eu
Energy Prices Eurostat
Table 2: Source of core country data items
EU-wide data sources
The main data collection method implemented was to use data on all EU 27 which originated
from the same reference source. Not only was this the most time and cost effective method,
it also ensured that data inputs were consistent across all countries. By searching for data
on a country by country basis the original collection methods could vary substantially and
therefore the data for one country may not be comparable with another country.
The tool uses a baseline year of 2010, and then makes assumptions about the rate of
upgrade of the heating control stock in future years. Therefore, the initial data inputs within
the tool must be for the same year, i.e. 2010. This was difficult to achieve if one was to look
at individual country’s data sources. Often the national statistics sites only show the latest
data sets collected, or, in other cases, had not collected data for some inputs which resulted
in gaps. The EU wide datasets did, however, show data for the same year for each country
hence these data sources, and base year, 2010 were used.
There was only a limited amount of time allocated to this section of the project and after an
initial investigation searching for individual data points on a country by country basis, this
approach was ruled out as a way to complete the inputs, as the time needed to search for
each piece of information would be outside the project scope. The initial search
concentrated on the ten countries with the highest energy demand, as this could have the
greatest impact on the overall results. This became time consuming due to the constraint
that the data is not collected and/or not displayed consistently across different countries’
national statistics or government sites.
For some of the target countrieswe were able to locate specific housing stock analysis, or
housing energy reports. These reportswere typically funded and commissioned by the
national Government. In England for example there are two reports of this kind: the English
Housing Survey (EHS) which provides a wealth of data and insight into the state of the
housing stock, and the Domestic Energy Fact File (DEFF). The EHS report is published

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annually and concentrates on: housing stock, dwelling condition, energy performance and
improvement potential. The DEFF aims to draw together important data about energy use in
homes.The results are based on an assessment of a random sample of houses or
households, depending on the questions being answered. These surveys tended to contain
more in-depth information on the heating system and often touch on the subject of heating
controls. Other examples from member states have also been observed:
Polandhasdeveloped a report of a similar nature which contained information on the
presence of heating controls in its housing stock.6
The publication contains detailed
information on domestic energy consumption by the purpose of use, the ownership of energy
using devices and housing characteristics which influence energy consumption.
The Spanish InstitutoNacional de Estadistica has a comprehensive dataset entitled Survey
on Households and the Environment 20087
. It surveys habits, consumption trends and
attitudes of households towards the environment, including water and waste, as well as
surveying characteristics relating to energy usage (insulation, heating/cooling equipment and
low energy lighting). It has a lot of detail on how the heating system is used including the
percentage of dwellings with thermostat heating or cooling, as well as asking the average
room temperature programmed through the day, number of rooms with heating and months
when heating is used, and details on heating used over night and when away from home. It
also has the percentage of dwellings that have changed or decided to change heating
system in the next 12 months, showing reasons for their choice.
A Belgium study published in 2011 entitled Energy Consumption Survey for Belgium
Households8
, in association with Eurostat, holds some limited information on heating
systems and behaviour of householders. The aim of this survey was to give better insights
into household energy consumption and dwelling characteristics in Belgium. This study
provided figures on the percentage of homes with a high efficiency/condensing boiler by
heating fuel type, whether the temperature can be controlled within the dwelling and what
temperature are living areas heated to. Results are presented by region and, where
possible, in comparison to the 2001 census.
A common data set across countries was a country wide census. These surveys included
some information on the quality of the housing stock, mainly with regards to the percentage
of homes with central heating, for example.
6
http://www.stat.gov.pl/cps/rde/xbcr/gus/EE_energy_consumption_in_households_2009.pdf
7
http://www.ine.es/jaxi/menu.do?type=pcaxis&path=/t25/p500&file=inebase&L=1
8
http://www.buildup.eu/publications/39444

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With these issues in mind we investigated EU funded projects and report in this area with the
assumption that they would have the same level of data qualityas the national Government
data sets. The EU funding of these projects also meant that they covered all or most of the
EU-27 countries being investigated in this study, which in most cases meant a consistent
data collection method for each country and covering the same baseline year. Key EU-wide
data sources included European policy directive supporting research, such as Ecoboiler and
the Energy Efficiency Directive supporting material, or EU-funded data research and
collection programmes such as Entranze (an Intelligent Energy Europe programme).
For some data inputs, a generic ‘best guess’ was made for all EU countries. For example the
demolition rate is assumed to be the same across all countries covered.
It is important to note that if individual country data on this topic becomes available it is
possible to input this into the model at a later date to enhance the accuracy of the model at
the individual country level.
Heating system efficiency
The impact of heating system controls on household energy demand is thought to act in two
main ways. Firstly heating controls improve heating system efficiency meaning that less fuel
is required to achieve a given useful heat demand in a home. Secondly heating controls
reduce the useful heat demand of a home, for example by shutting off heating in unused (or
less used) areas of the home and restricting the time of operation of the heating system.
The effect of heating controls on system efficiency is relatively well researched and
understood, however the effect of heating controls on useful heat demand is less well
understood. As a result this project is focussed on changes in heating system efficiency with
changes to useful heat demand not being fully explored (although some functionality has
been introduced into the tool in order to allow for this in the future if data becomes available).
The source for the effects of controls on heating system efficiency was modelling work
undertaken by VHK for the Eco-design of Boilers and Combi Boilers project.9
The control
functionality developed in thatmodel was based on the European standards for heating
control design and is considered the leading source for this type of data, having been used in
the development of European legislation.
9
http://www.ecoboiler.org

19
The Ecoboiler model v5b10
was used to derive heating system efficiencies for the different
combinations of boiler type, timer control, temperature control and valve control found in the
different countries. This was supplemented with more recent evidence concerning the impact
of TRVs on heating system efficiency drawn from the Salford tests conducted in the UK by
BEAMA and research undertaken in Dresden in Germany comparing the savings of replacing
old TRVs with new TRVs.
This led to the following system efficiencies for the different combinations of the heating
control packages with the standard and high efficiency boiler:
Control Package Standard High Efficiency
No controls 46% 53%
Timer only 51% 58%
Timer + TRVs (>15 yrs old) 55% 62%
Timer + Room Stat 55% 65%
Timer + Room Stat / OTC 57% 67%
Timer + room stat + TRVs > 15 years old 59% 69%
Timer + Modulating Stat 60% 70%
Timer + OTC + TRVs > 15 yrs old 63% 72%
Timer + room thermostat + TRVs 64% 74%
Timer + OTC + TRVs (Modern) 68% 77%
Timer + Modulating Stat + TRVs 69% 79%
Control packages and ownership
One of the main inputs into the tool is an estimateof what types of heating controls are
already present in homes across Europe. A lengthy search of published research, reports
and surveys uncovered limited data on this topic.The UK Government’s energy department,
DECC, have recently published an assessment of how heating controls affect domestic
energy demand11
and their conclusions concur with ours that very little research has been
undertaken in this area to date and the small range of studies that do exist are very small
scale and therefore cannotrepresent a national picture. Information on heating fuel, and
boiler type (condensing / non-condensing) is often readily available, but further information
on heating controls is not present.
We were not aware of a central data source which contains this information and so this was
an important feature of the literature review. Speaking to industry who were able to signpost
10
http://www.ecoboiler.org/public/ECOBOILER_Model_v_5b.xls
11
https://www.gov.uk/government/publications/how-heating-controls-affect-domestic-energy-demand-
a-rapid-evidence-assessment

20
recognised sources of information produced some useful data but did not fill all of the gaps
for many of the countries. In some case we built on the limited data that is availableto
construct control package definitions using estimates based on industry knowledge and
experience of the development of the heating control market. Once the control packages and
their penetration in the housing stock were agreed it was possible to produce usable outputs
from the tool.We expect that in the coming years further heating controls penetration data will
emerge and will allow the savings estimates to be refined further. The packages chosen for
each of the countries analysed are given below. Due to data constraints some Member
States have beengrouped together:
France
Package Control combinations
1 Timer + TRVs (>15 yrs old)
2 Timer + Room Thermostat
3 Timer + Room Thermostat + TRVs
4 Timer + Outside Temperature Control + TRVs
Germany
1 Timer + Room Thermostat + Manual Radiator Valves
2 Timer + Room Thermostat + TRVs > 15 yrs old
3 Timer + Outside Temperature Control + TRVs > 15 yrs old
4 Timer + Outside Temperature Control + TRVs (Modern)
Italy
1 No controls
2 Timer + Room Thermostat / Outside Temperature Control
4 Timer + Outside Temperature Control + TRVs
Netherlands
2 Timer + Modulating Thermostat
4 Timer + Modulating Thermostat + TRVs

21
Spain
1 No controls
2 Timer + Room Thermostat / OTC
4 Timer + OTC + TRVs
United Kingdom
1 Timer only
2 Timer + Room Thermostat or TRVs
4 Timer + Time Proportional Room Thermostat + TRVs
Eastern Europe (Bulgaria, Romania, Slovakia, Czech Rep., Poland, Austria, Slovenia,
Lithuania, Latvia, Hungary)
1 Timer only
3 Timer + Room Thermostat + TRVs > 15 years old
The penetration of the above heating control packages used in the spreadsheet tool aregiven
in the tables below.
Existing dwellings
Each country’s housing stock is broken down into the two boiler types, low and high
efficiency, and then each boiler type is further split into one of the four control packages
described above (therefore each row sums to 100%).

22
In some cases data exists which describes the mix of heating controls in homes with high
efficiency boilers separately to the mix of heating controls in homes with standard boilers.
This was the ideal set of data for this project.
In other cases the data simply describes the mix of heating controls in all homes without the
distinction between boiler types. In these cases it was necessary to estimate how the
distribution of a heating control package might fall between homes with a high efficiency
boiler and homes with a standard boiler. Given that heating controls are currently most
commonly installed at the same time as boilers are replaced, the simpler packages were
allocated in greater proportion to the standard boilers and the more complex packages were
allocated in greater proportion to the high efficiency boilers.
Country
Standard Boiler + Control
Package as below
High Efficiency boiler + Control
Package as below
1 2 3 4 1 2 3 4
Austria 24% 27% 15% 27% 0% 2% 1% 4%
Bulgaria 24% 11% 23% 42% 0% 0% 0% 0%
Czech Republic 24% 6% 13% 27% 0% 5% 10% 15%
France 16% 13% 44% 15% 0% 0% 9% 3%
Germany 5% 6% 27% 37% 0% 0% 0% 25%
Hungary 24% 29% 16% 31% 0% 0% 0% 0%
Italy 10% 70% 5% 0% 0% 0% 5% 10%
Latvia 24% 29% 16% 31% 0% 0% 0% 0%
Lithuania 24% 29% 16% 31% 0% 0% 0% 0%
Netherlands 30% 0% 0% 0% 30% 10% 20% 10%
Poland 24% 11% 23% 42% 0% 0% 0% 0%
Romania 24% 11% 23% 42% 0% 0% 0% 0%
Slovakia 24% 7% 20% 28% 0% 4% 7% 10%
Slovenia 24% 29% 16% 31% 0% 0% 0% 0%
Spain 15% 55% 15% 0% 0% 0% 10% 5%
United Kingdom 10% 30% 17% 0% 3% 16% 23% 0%
Evidence for comfort taking
A major research exercise that investigated the evidence for the ‘Rebound Effect’, in all its
manifestations, was carried out on behalf of DG Env in 2010/1112
. The report entitled
‘Addressing the Rebound Effect’ looked at rebound in its various forms, namely direct,
indirect and economy wide. The type of Rebound Effect of interest to this project is the
potential for direct rebound effects, otherwise known as ‘comfort taking’, as a result of a
decrease in the unit cost of heating one’s home as a result of improvements in the heating
system and/or extra insulation measures being implemented in the dwelling.
12
http://ec.europa.eu/environment/eussd/pdf/rebound_effect_report.pdf

23
Occupants who previously found it difficult and/or costly to heat their home to an adequate
temperature, post the intervention, found it cheaper and easier to heat to an adequate
degree hence they make the decision to heat to a higher temperature, and/or keep their
heating on for longer than previously, hence reducing the maximum theoretical savings that
are calculated as a direct result of the intervention. The study found that typically, the
reduction from the maximum theoretical savings for such interventions to be in the region of
10-30% depending on the study and countries involved.
It is worth noting that the installation of heating controls has the potential to mitigate comfort
taking connected to other measures such as installing new boilers, fitting insulation or
draught proofing the home. If these measures are installed in homes with inadequate heating
controls then the likelihood of comfort taking is increased as the heating system is less
controllable.
For example recent research for the Department of Energy and Climate Change in the UK
showed that householders having their walls insulated reported that the house then got too
warm and they had to open windows. This would increase their energy use significantly
above what would be expected yet would be avoided with individual room controls.
Discount rates
In order to calculate the net present value of the marginal capital costs and energy cost
savings associated with the increase uptake of heating controls it was necessary to choose
appropriate discount rates. We have adopted the European Union-recommended13
real
social discount rates of 5.5% for countries eligible for the Cohesion Fund14
and 3.5% for the
remaining countries.
Control Costs
Installation costs were based on a response to the UK Government call for evidence on the
Green Deal policy in 2011 by the UK controls industry association, TACMA, derived from
information sourced from installers and published prices. The labour cost element was
corrected for individual Member States using labour cost data from Eurostat. It should be
noted that the cost per measure is based on a three bedroom semi-detached house, which is
the most common house type in the UK rather than an average size, and as a result
estimates of installed costs used in this study are likely to be higher than the actual costs
consumers would pay.
13
http://ec.europa.eu/regional_policy/sources/docgener/guides/cost/guide2008_en.pdf
14
http://ec.europa.eu/regional_policy/thefunds/cohesion/index_en.cfm

24
6. Results
Overview of results
Although the calculator tool has been set up to be adaptable, we have drawn up a scenario
based on the best data currently available to determine the potential savings from heating
system controls in the EU. Both annual and cumulative results are given for energy
consumption, CO2 emissions and energy bill savings. The figures given in this section are
net of comfort taking (assumed to be 5%).
Sufficient data was available to generate results for 16 EU-27 countries (Austria, Bulgaria,
Czech Republic, France, Germany, Hungary, Italy, Latvia, Lithuania, Netherlands, Poland,
Romania, Slovakia, Slovenia, Spain and the United Kingdom). It was not possible at this time
to generate sufficiently robust results for the remaining 11 EU-27 countries (Belgium, Cyprus,
Denmark, Estonia, Finland, Greece, Ireland, Luxembourg, Malta, Portugal and Sweden).
The 16 countries for which we do have data comprise around 89% of the total residential
floor area of the EU-27 so the aggregated saving can be expected to be close to the value
which would be generated if we had obtained data for all EU countries. Additional savings
are therefore to be expected if data for all EU27 were included.
Baseline and Scenario selection
The baseline chosen for this analysis attempts to describe the current situation of boiler
upgrades and heating control replacement and extrapolate that situation out to 2030,
essentially assuming that there is no meaningful change to the rate of installation of heating
controls. Most control installation activity is therefore seen at the point of boiler replacement
with only modest numbers of installations being seen at other times. The mix of controls
installed at upgrade continues in a similar vein to that seen at present, with the installation of
full sets of heating controls (or advanced heating controls where relevant) remaining
comparatively rare.
The scenario which has been explored in this case assumed the same rate of boiler
replacement as the baseline (as this is driven more by boiler failure rather than householder
behaviour) but considered an accelerated increase in the penetration of heating controls over
the course of this decade leading to a replacement rate of around 20% per annum in the
2020s. Along with this increase in the rate of installation, the scenario also models the impact
of a move to the most sophisticated heating control package (Package 4) happening across
the housing stock with all households that upgrade do so by installing Package 4.

25
It should be noted that the exact mechanism which would drive this has not been considered
as the aim of this project was to explore the impact such a mechanism would have.
The effect on the boiler stock for a country (in this case Italy) can be seen in the two figures
below which show the boiler stock at five year intervals in the Baseline case and Scenario
case:
Figure 1: Change in boiler stock in Italy (Baseline)
Figure 2: Change in boiler stock in Italy (Scenario)
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
2010 2015 2020 2025 2030
%ofBoilerStock
Standard Package 1
Standard Package 2
Standard Package 3
Standard Package 4
High Efficiency Package 1
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
2010 2015 2020 2025 2030
%ofBoilerStock
Standard Package 1
Standard Package 2
Standard Package 3
Standard Package 4

26
Country and EU-wide results
Country 2010 2015 2020 2025 2030
Austria 0 679 1,542 1,877 1,722
Bulgaria 0 81 176 208 175
Czech Republic 0 238 551 721 661
France 0 3,891 8,996 11,024 10,609
Germany 0 857 1,607 1,743 1,372
Hungary 0 267 605 751 705
Italy 0 3,257 7,669 9,613 9,130
Latvia 0 22 46 55 50
Lithuania 0 18 43 53 50
Netherlands 0 872 2,333 3,245 3,424
Poland 0 288 628 754 629
Romania 0 110 237 280 239
Slovakia 0 54 126 160 147
Slovenia 0 157 356 425 393
Spain 0 1,289 2,462 2,693 2,358
United Kingdom 0 4,704 13,524 20,183 21,076
Total 0 16,784 40,901 53,785 52,740
Table 3 - Annual energy savings (GWh)
Figure 3 - Annual energy savings (GWh)
0
10,000
20,000
30,000
40,000
50,000
60,000
2010 2015 2020 2025 2030
AnnualEnergySaving(GWh)
United Kingdom
Spain
Slovenia
Slovakia
Romania
Poland
Netherlands
Lithuania
Latvia
Italy
Hungary
Germany
France
Czech Republic
Bulgaria
Austria

27
Country 2010 2015 2020 2025 2030
Austria 0 1,623 7,833 16,767 25,802
Bulgaria 0 203 920 1,923 2,877
Czech Republic 0 582 2,778 6,127 9,608
France 0 9,219 45,278 97,657 152,227
Germany 0 2,264 9,078 17,795 25,502
Hungary 0 637 3,076 6,622 10,283
Italy 0 7,180 37,958 83,200 130,380
Latvia 0 54 246 511 774
Lithuania 0 44 215 466 727
Netherlands 0 1,979 10,930 25,609 42,605
Poland 0 718 3,274 6,893 10,335
Romania 0 275 1,244 2,597 3,891
Slovakia 0 132 632 1,385 2,159
Slovenia 0 378 1,813 3,852 5,907
Spain 0 3,236 13,751 27,084 39,641
United Kingdom 0 11,015 60,926 150,611 255,879
Total 0 39,539 199,951 449,099 718,599
Table 4 - Cumulative energy savings (GWh)
Figure 4 - Cumulative energy savings (GWh)
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
800,000
2010 2015 2020 2025 2030
CumulativeEnergySaving(GWh)
United Kingdom
Spain
Slovenia
Slovakia
Romania
Poland
Netherlands
Lithuania
Latvia
Italy
Hungary
Germany
France
Czech Republic
Bulgaria
Austria

28
Country 2010 2015 2020 2025 2030
Austria € 0 € 45 € 113 € 152 € 154
Bulgaria € 0 € 4 € 9 € 12 € 11
Czech Republic € 0 € 14 € 35 € 50 € 51
France € 0 € 247 € 631 € 853 € 906
Germany € 0 € 54 € 112 € 134 € 116
Hungary € 0 € 16 € 41 € 56 € 58
Italy € 0 € 283 € 736 € 1,018 € 1,068
Latvia € 0 € 1 € 2 € 3 € 3
Lithuania € 0 € 1 € 2 € 3 € 3
Netherlands € 0 € 69 € 203 € 312 € 363
Poland € 0 € 16 € 39 € 51 € 47
Romania € 0 € 3 € 8 € 10 € 10
Slovakia € 0 € 3 € 7 € 10 € 10
Slovenia € 0 € 12 € 29 € 39 € 39
Spain € 0 € 77 € 162 € 196 € 189
United Kingdom € 0 € 219 € 696 € 1,146 € 1,322
Total € 0 € 1,063 € 2,824 € 4,046 € 4,351
Table 5 - Annual financial savings (€m nominal)
Figure 5 - Annual financial savings (€m nominal)
€ 0
€ 500
€ 1,000
€ 1,500
€ 2,000
€ 2,500
€ 3,000
€ 3,500
€ 4,000
€ 4,500
€ 5,000
2010 2015 2020 2025 2030
AnnualFinancialSaving(€m)
United Kingdom
Spain
Slovenia
Slovakia
Romania
Poland
Netherlands
Lithuania
Latvia
Italy
Hungary
Germany
France
Czech Republic
Bulgaria
Austria

29
Country 2010 2015 2020 2025 2030
Austria 0 106 546 1,243 2,020
Bulgaria 0 9 46 102 161
Czech Republic 0 33 166 391 648
France 0 574 3,017 6,920 11,401
Germany 0 140 597 1,242 1,869
Hungary 0 38 197 452 742
Italy 0 613 3,467 8,082 13,384
Latvia 0 2 12 25 41
Lithuania 0 2 11 26 43
Netherlands 0 153 907 2,266 4,001
Poland 0 39 191 428 676
Romania 0 8 40 89 140
Slovakia 0 6 33 76 125
Slovenia 0 28 141 319 517
Spain 0 189 857 1,789 2,757
United Kingdom 0 503 2,990 7,901 14,249
Total 0.0 2,444 13,219 31,351 52,772
Table 6 - Cumulative financial savings (€m nominal)
Figure 6 – Cumulative financial savings (€m nominal)
0
10,000
20,000
30,000
40,000
50,000
60,000
2010 2015 2020 2025 2030
CumulativelFinancialSaving(€m)
United Kingdom
Spain
Slovenia
Slovakia
Romania
Poland
Netherlands
Lithuania
Latvia
Italy
Hungary
Germany
France
Czech Republic
Bulgaria
Austria

30
Country 2010 2015 2020 2025 2030
Austria 0.0 0.2 0.4 0.4 0.4
Bulgaria 0.0 0.0 0.0 0.0 0.0
Czech Republic 0.0 0.0 0.1 0.1 0.1
France 0.0 0.9 2.0 2.4 2.4
Germany 0.0 0.2 0.4 0.4 0.3
Hungary 0.0 0.1 0.1 0.2 0.1
Italy 0.0 0.7 1.7 2.2 2.1
Latvia 0.0 0.0 0.0 0.0 0.0
Lithuania 0.0 0.0 0.0 0.0 0.0
Netherlands 0.0 0.2 0.5 0.7 0.7
Poland 0.0 0.1 0.2 0.2 0.2
Romania 0.0 0.0 0.0 0.1 0.0
Slovakia 0.0 0.0 0.0 0.0 0.0
Slovenia 0.0 0.0 0.1 0.1 0.1
Spain 0.0 0.3 0.5 0.6 0.5
United Kingdom 0.0 1.0 2.8 4.2 4.4
Total 0.0 3.7 8.9 11.7 11.4
Table 7 - Annual CO2 savings (MtCO2)
Figure 7 – Annual CO2 savings (MtCO2)
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
2010 2015 2020 2025 2030
AnnualCO2Saving(MtCO2)
United Kingdom
Spain
Slovenia
Slovakia
Romania
Poland
Netherlands
Lithuania
Latvia
Italy
Hungary
Germany
France
Czech Republic
Bulgaria
Austria

31
Country 2010 2015 2020 2025 2030
Austria 0.0 0.4 1.8 3.9 5.9
Bulgaria 0.0 0.0 0.2 0.4 0.6
Czech Republic 0.0 0.1 0.6 1.3 2.0
France 0.0 2.0 10.0 21.6 33.7
Germany 0.0 0.5 2.0 4.0 5.7
Hungary 0.0 0.1 0.6 1.4 2.1
Italy 0.0 1.6 8.5 18.7 29.4
Latvia 0.0 0.0 0.1 0.1 0.2
Lithuania 0.0 0.0 0.0 0.1 0.1
Netherlands 0.0 0.4 2.2 5.3 8.7
Poland 0.0 0.2 0.9 1.8 2.7
Romania 0.0 0.1 0.3 0.5 0.8
Slovakia 0.0 0.0 0.1 0.3 0.4
Slovenia 0.0 0.1 0.4 0.8 1.3
Spain 0.0 0.7 3.0 5.9 8.7
United Kingdom 0.0 2.3 12.8 31.6 53.7
Total 0.0 8.6 43.6 97.7 156.1
Table 8 - Cumulative CO2savings (MtCO2)
Figure 3 - Cumulative CO2 savings (MtCO2)
0.0
20.0
40.0
60.0
80.0
100.0
120.0
140.0
160.0
180.0
2010 2015 2020 2025 2030
CumulativeCO2Saving(MtCO2)
United Kingdom
Spain
Slovenia
Slovakia
Romania
Poland
Netherlands
Lithuania
Latvia
Italy
Hungary
Germany
France
Czech Republic
Bulgaria
Austria

32
System Balancing and District Heating
There may also be additional benefits to be found in the proper balancing of heating systems
fitted with thermostatic radiator valves.
An incorrectly balanced system will result in excess heat being delivered to the first radiators
in the heating circuit, with the last radiators receiving insufficient heat. A correctly balanced
heating system ensures that there is an even distribution of heat between radiators as hot
water flows around the heating circuit.
A study in 200415
analysed the change in energy demand of 4,000 homes connected to
district heating systems in Slovakia which were fitted with TRVs and balancing valves where
previously they were fitted only with manual radiator valves,. This resulted in an energy
saving of around 27%, which is understood to be a result of correctly balanced systems
allowing the TRVs to operate effectively across the whole of their setting range.
These results have not been included in this study, which focuses on residential properties
with individual central heating systems. However the results are nevertheless interesting and
suggest that the potential savings from heating controls in residential properties are likely to
be higher than detailed here, particularly in countries with a high proportion of homes
connected to district heating. This therefore warrants further study.
15
Studie über die Sanierung von Heizungsanlagen (Study on the renovation of heating systems)

33
7. Conclusions
It is clear from the results produced by this project that heating system controls have a
significant role to play in reducing household greenhouse gas emissions and energy bills as
well as contributing to European energy security through reduced household energy
demand.
Around three quarters of the calculated savings come from the three largest countries (the
United Kingdom, France and Italy) and preliminary results covering 16 EU countries lead to
peak annual energy savings of over 50TWh per year, nominal fuel bill savings of around €4.3
billion and CO2 savings of nearly 12MtCO2 per annum. This annual CO2 emissions saving
would be the equivalent CO2 emissions generated by the space heating of 4.4 million
European homes. Cumulative energy savings of over 700TWh are expected by 2030, with
cumulative nominal energy bill savings reaching €50 billion and cumulative CO2 savings of
over 150MtCO2.
In order to establish the cost-effectiveness of the enhanced retrofit scenario described in this
report over the business as usual case, the marginal costs and benefits accrued between
2013 and 2030 were discounted to arrive at the net present value.The total discounted
marginal capital costs amount to around €13 billion with energy bill savings of around €38
billion giving a net benefit of around €23 billion over the period. This implies that the benefits
exceed the costs by around 2.7 to 1 and the cost-effectiveness of upgrading European
heating controls between now and 2030 is an overall financial saving of €150 per tonne of
CO2.
This project shows that there is a clear case for increasing the rate of installation of heating
controls in the European housing stock thanks to their effect on heating system efficiency
alone, but the evidence for savings from heating controls points to substantial
benefitsextending beyond increasing heating system efficiency.

34
8. Recommendations
The findings in this report should serve as a wake up call to policy makers. It is clear from the
results that there is a significant potential for energy savings through a greater application of
heating system controls. It is equally clear from the data on the existing penetration of
controls in European homes that too little is being done and the business as usual approach
will not deliver the full potential for greenhouse gas savings expected in the domestic sector.
Allied to a growing recognition that combinations of measures are required to deliver energy
savings in practice, it is clear that heating controls must be a key element of any packages of
measures developed in a strategy for energy efficient renovation.
As heating controls form part of a system, the European Commission needs to be clear how
they are accommodated within the existing policy framework, which currently focuses on
products and buildings. The Commission should work with industry to identify ways to ensure
that a greater uptake of heating controls can be driven through the existing framework.
Controls offer additional benefits to those quantified in this report in the form of reducing
useful heat demand but to date only relatively limited laboratory and field trials with small
sample sizes have been conducted so the extent of these benefits is currently uncertain.
Laboratory and field trials are costly but if these additional benefits were better understood,
the case for installing heating controls would be furtherstrengthened.
Data required to fill the gaps on current heating control ownership, and to help with targeting,
should already exist as Energy Performance Certificates, required under the Energy
Performance in Buildings Directive and whichhave now been running for many years. This
data is held at a national level, but it seems that no analysis synthesising the data on heating
control ownership held in national EPC databases has yet been carried out.Alternatively this
information could be gathered through the extension of existing household energy surveys or
through market research undertaken by eu.bac members in collaboration with national
energy agencies in European member countries.

35
Annex A – References and useful links
Broin, E.O. (2007) Energy Demands of European Buildings: A Mapping of Available Data,
Indicators and Models.
Buildings Performance Institute Europe (BPIE) (2011) Europe’s buildings under the
microscope: A country-by-country review of the energy performance of buildings.
Cambridge Architectural Research Limited (2011) A Guide to The Cambridge Housing
Model.
Cayla, J-M. From practices to behaviors: Estimating the impact of household behaviour on
space heating energy consumption.
CECODHAS Housing Europe’s Observatory (2011) Housing Europe Review 2012: The nuts
and bolts of European social housing systems.
Central Statistics Office [Poland] (2009) Energy Consumption in Households in 2009.
Department of Energy & Climate Change. UK Housing Energy Fact File 2012
https://www.gov.uk/government/collections/domestic-energy-fact-file-and-housing-surveys
eu.bac .Proposed Control Packages – Eastern European Nations. Presentation slides
obtained from eu.bac member.
European Commission (2009) ECO design directive LOT 1 Test with the ECOboiler model
(version June 2009).
European Commission DG for Energy and Transport (2002) Labelling and other measures
for heating systems in dwellings.
Fraunhofer ISI (2009) Study on the Energy Savings Potentials in EU Member States,
Candidate Countries and EEA Countries Final Report.For the European Commission.
Fuhrmann, K.-D.HerzGruppe (2011) CEE regional data hot water heating. Presentation
slides obtained from eu.bac member.
InstitutoNacional de Estadistica [Spain] (2013) Population and Housing Censuses 2011.
Inteligent Energy Europe (2006) Ecoheatcool work package 1: The European Heat Market
Final Report.
ODYSSEE-MURE (2012) Energy Efficiency Trends in Buildings in the EU.

36
Shipworth et al. (2010) Central heating thermostat settings and timing: building
demographics.
Sommerville, M. (2007) Space Heating Energy Efficiency Program Evaluation Report.
The Hague: Ministry of the Interior and Kingdom Relations (2010) Housing Statistics in the
European Union 2010.
Van Holsteijn en Kemna (VHK) (2007) Eco-design of CH Boilers: Task 1-7 Report (final).
Van Holsteijn en Kemna (VHK) (2007) Ecodesign of EuP: Lots 1&2: CH-boilers and water
heaters. Presentation slides obtained from eu.bac member.
VITO, ICEDD, FPS Economy (2011) Energy Consumption Survey for Belgian households.
Links to useful websites:
BPIE (Buildings Performance Institute Europe) - http://www.buildingsdata.eu
ECEE -
http://www.eceee.org/ecodesign/products/Lot22_23_kitchen/2013.06.04_Ecodesign_Regulat
ion.pdf
Energy Consumption Survey for Belgian Households -
http://www2.vlaanderen.be/economie/energiesparen/doc/Eurostatenquete_onderzoeksrappo
rt.docz
Energy Demands of European Buildings. A mapping of available data, indicators and
models - http://publications.lib.chalmers.se/records/fulltext/136409.pdf
ENTRANZE - http://www.entranze.eu
German National Statistics site - https://www.destatis.de including Statistics Yearbook 2012
http://www.eepotential.eu
Hungarian Central Statistics Office - http://www.ksh.hu/?lang=en
Italian National Statistics site - http://dati.istat.it/?lang=en
Netherlands National Statistics site - http://statline.cbs.nl
ODYSSEE - http://www.indicators.odyssee-mure.eu/online-indicators.html

37
Poland National Statistics site - http://www.stat.gov.pl including Energy Consumption in
households 2009 report
http://www.stat.gov.pl/cps/rde/xbcr/gus/EE_energy_consumption_in_households_2009.pdf
Romanian National Institute of Statistics - https://statistici.insse.ro/shop/?lang=en
Spanish National Statistics institute - http://www.ine.es
UK National Statistics – www.gov.uk

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