The impact of building automation control systems as retrofitting measures on the energy efficiency of a typical Norwegian single-family house

Abstract

In this study, the energy savings and cost-effectiveness of building automation measures in a retrofitting context are evaluated for a Norwegian single-family house. In addition, two methods for calculating savings from implementing building automation control systems (BACS) are compared: energy performance simulation and the BACS factor method proposed in EN 15232. Four retrofitting packages and four automation levels are combined to create 16 model versions. This study shows that BACS can increase energy savings and improve indoor comfort when implemented as a retrofitting measure. However, the relative impact on energy savings of building automation decreases when the delivered energy is lower. In addition, the impact of building automation on the energy savings is low compared to the effect of retrofitting the building envelope. When only BACS are implemented, savings up to 21% can be achieved, but when an integrated solution is implemented savings up to 60% can be achieved. Finally, some directions for future work are suggested.

Full text

IOP Conference Series: Earth and Environmental Science
PAPER • OPEN ACCESS
The impact of building automation control systems as retrofitting
measures on the energy efficiency of a typical Norwegian single-family
house
To cite this article: L C Felius et al 2020 IOP Conf. Ser.: Earth Environ. Sci. 410 012054
View the article online for updates and enhancements.
This content was downloaded from IP address 194.110.89.206 on 25/01/2020 at 01:30

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution
of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
Published under licence by IOP Publishing Ltd
SBE19 Thessaloniki
IOP Conf. Series: Earth and Environmental Science 410 (2020) 012054
IOP Publishing
doi:10.1088/1755-1315/410/1/012054
1
The impact of building automation control systems
as retrofitting measures on the energy efficiency of a
typical Norwegian single-family house
L C Felius1, M Hamdy1, B D Hrynyszyn1and F Dessen2
1Department of Civil and Environmental Engineering, Faculty of Engineering, NTNU
Norwegian university of Science and Technology, Trondheim, Norway.
2Department of Engineering Cybernetics, Faculty of Information Technology and Electrical
Engineering, NTNU Norwegian university of Science and Technology, Trondheim, Norway.
E-mail: laurina.felius@ntnu.no, bozena.d.hrynyszyn@ntnu.no
Abstract. In this study, the energy savings and cost-effectiveness of building automation
measures in a retrofitting context are evaluated for a Norwegian single-family house. In addition,
two methods for calculating savings from implementing building automation control systems
(BACS) are compared: energy performance simulation and the BACS factor method proposed
in EN 15232. Four retrofitting packages and four automation levels are combined to create 16
model versions. This study shows that BACS can increase energy savings and improve indoor
comfort when implemented as a retrofitting measure. However, the relative impact on energy
savings of building automation decreases when the delivered energy is lower. In addition, the
impact of building automation on the energy savings is low compared to the effect of retrofitting
the building envelope. When only BACS are implemented, savings up to 21% can be achieved,
but when an integrated solution is implemented savings up to 60% can be achieved. Finally,
some directions for future work are suggested.
1. Introduction
Buildings consume a large share of the total energy consumption in Europe, mostly due to
an inefficient existing building stock [1]. As residential buildings represent around 75% of the
existing buildings, it can be concluded that retrofitting offers a significant energy saving potential
[2]. The building stock situation in Norway is similar, though it is characterized by its reliance on
electricity as the main energy source in residential buildings [3, 4]. A way to improve the energy
efficiency of existing buildings, though not often done, is by implementing building automation
control systems (BACS). BACS can reduce the operational energy use while maintaining a
comfortable indoor climate as highlighted in previous work [5], but system settings can have a
significant effect on the achieved energy savings [6]. The system can also reduce peak loads and
overall energy costs. This is increasingly important in Norway, as a new grid rent tariff will be
introduced by the end of 2020 [7]. If the typical consumption pattern is not changed, it will
result in higher energy costs for the consumer [8, 9].
Standard EN 15232 [10] focuses on building automation control (BAC) and technical building
management (TBM) functions that can improve the energy performance of a building. The BAC
functions are divided into heating, cooling, ventilation, hot water, lighting and blind control; the

SBE19 Thessaloniki
IOP Conf. Series: Earth and Environmental Science 410 (2020) 012054
IOP Publishing
doi:10.1088/1755-1315/410/1/012054
2
TBM functions focus on data monitoring and diagnosis. Four efficiency levels are introduced in
the standard based on the implemented functions (see table 1) [10]. These labels are not related
to energy performance labels [11], though both are defined by delivered energy. To quickly
estimate the effect of BAC and TBM functions on the energy performance of a building, the
BACS factor method can be used as presented in EN 15232. Efficiency factors for different
building categories are divided into thermal and electrical energy. Thermal energy includes
energy used for heating, cooling and domestic hot water. Electrical energy includes auxiliary
energy and energy used for lighting. The energy use of appliances is not taken into account.
By multiplying the efficiency factors with its associated delivered energy, the expected energy
savings are estimated. The efficiency factors assume that the current standard of BACS is level
C. However, for this case study it was assumed that there is no automation present (level D).
The overall efficiency factors for housing are listed in table 1.
Table 1. BACS levels with corresponding thermal and electrical efficiency factors to estimate
the energy savings from implementing BACS and TBM functions [10]
Thermal Elecrical
Level D: no automation 1.10 1.08
Level C: standard BAC for new buildings 1.00 1.00
Level B: advanced BAC with some TBM functions 0.88 0.93
Level A: high-performance BAC and TBM functions 0.81 0.92
Several studies discussed the expected outcome from upgrading BACS in residential buildings
and concluded that significant energy savings can be achieved [12, 13, 14, 15]. However, the
number of studies investigating the effect of BACS in a residential context is limited and does
not focus on cold climates. Therefore, the aim of this study is to evaluate the potential of BACS
as an energy saving measure for a typical Norwegian house. Previous work highlighted that
the effect on the total energy consumption is low compared to savings that can be achieved
by upgrading the building envelope [5, 16]. Therefore, four BACS packages are assessed in
combination with four retrofitting packages, resulting in 16 models. The packages are based
on standards and are not optimized for this building. In the BACS packages, not all functions
listed in EN 15232 are included. The combinations of measures are evaluated in terms of energy
savings, indoor comfort and cost-effectiveness. In addition, the soundness of the BACS factor
method is evaluated by comparing estimated energy savings calculated with this method to the
simulation results.
2. Case study model
The case study (see figure 1) is a typical Norwegian single-family house as described by Thyholt
et al. [17]. The external walls and roof are a timber-frame construction and the walls of the
lower floor are constructed with LECA blocks. The ground floor is a concrete slab on grade with
no to little insulation. This housing type is located throughout Norway, but for this study the
climate of Trondheim, Værnes was used. The house was built according to the building code of
1969. However, higher U-values are used in the simulation model to take into account that the
insulation may have worsened over the years due to deterioration. An overview of the electrical
loads and internal gains in the house is given in table 2.

SBE19 Thessaloniki
IOP Conf. Series: Earth and Environmental Science 410 (2020) 012054
IOP Publishing
doi:10.1088/1755-1315/410/1/012054
3
Figure 1. Layout and appearance of a typical single-family house, mostly built in the 70s and
early 80s, with an area of ca. 170 m2. The east half of the lower floor is partly submerged.
Table 2. Electrical loads and internal gains in the case study. The lighting load in the
retrofitting packages, after upgrading to LED, is given in brackets.
Equipment Room Area [m2] Lighting [Watt] Heating [watt]
Kitchen Freezer, fridge, oven, microwave,
kettle, small cooking, dishwasher
8.5 2 x 46 (10) 1000
Living room Television, TV receiver, HiFi, clock,
personal computer
LR 1 35.0 3 x 46 (10) 3800
LR 2 21.0 3 x 46 (10) 2000
LR 3 26.6 3 x 46 (10) 1500
Bedroom
Clock, cordless
phone
BE 1&2 11.2 46 (10) 1000
BE 3&4 9.1 46 (10) 1000
Bathroom BA 1 3.5 46 (10) 500
BA 2 1.9 30 (6) 250
WC 1.5 30 (6) 250
Laundry Iron, vacuum cleaner, washing
machine, dryer
6.4 46 (10) 500
Hall
H 1 5.3 2 x 30 (6) 600
H 2 3.6 30 (6) 250
H 3 2.3 30 (6) 250
2.1. Simulation model
The case study building was modeled in IDA-ICE [18]. The aim of this study required a
model where all rooms were modeled as individual zones to study the effects of BACS (i.e.
individual room control and setpoints). The simulation model was validated by comparing the
model with standardized input values and occupancy behaviour [19] to reference values [20, 21].
Occupancy behaviour and distribution of internal gains were adjusted to fit a more realistic
scenario. Occupancy schedules were derived from Nord et al. [22], the schedules for lighting and
equipment were taken from open-source models by Richardson et al. [23, 24] and the schedule
for DHW was taken from Ahmed et al. [25]. These models were adapted to the case study and
location.
The effect of four building automation levels was evaluated for four retrofitting packages,

SBE19 Thessaloniki
IOP Conf. Series: Earth and Environmental Science 410 (2020) 012054
IOP Publishing
doi:10.1088/1755-1315/410/1/012054
4
resulting in 16 model versions. The retrofitting packages are based on standards and are not
optimized for the building type. The first package (R0) is the building in its current state,
without any renovation. Standard values for old buildings as presented in NS 3031 [19] are
used. In package R1 (minimum retrofitting) only the windows are improved. As a result, it
is expected that the airtightness improves and that thermal bridges around the windows are
reduced. To estimate the improvement of airtightness, the method of Ridley et al. [26] was
used. In package R2 (moderate retrofitting) the house is upgraded to TEK 17, the current
minimum energy requirements in Norway [27]. Package R3 (major retrofitting) is based on the
building envelope criteria from the Norwegian passive house standard [28]. The improvements
to the energy performance characteristics are listed in table 3. In packages R1-3 it is necessary
to upgrade the ventilation system to ensure proper air quality. Additionally, all lights were
replaced by LED lights in R1-3.
Table 3. Energy performance characteristics in the four renovation packages.
R0 - no
renovation
[19]
R1 -
minimum
R2 [27] -
moderate
R3 - major
[28]
U-value basement wall [W/m2K] 1.0 1.0 0.18 0.10
U-value basement floor [W/m2K] 0.5 0.5 0.10 0.08
U-value timber frame wall [W/m2K] 0.6 0.6 0.18 0.10
U-value roof (loft insulation) [W/m2K] 0.6 0.6 0.13 0.08
U-value windows [W/m2K] 2.8 1.2 0.8 0.8
Air leakage at 50 Pa [h-1] 6.0 1.4 0.6 0.6
Norm. thermal bridge value [W/m2K] 0.07 0.07 0.05 0.03
SFP [kW/(m3/s)] 2.0 1.5 1.5 1.5
Heat recovery [%] 0 80 80 80
Ventilation system Mech.
exhaust
Balanced Balanced Balanced
The heating system consists of electric radiators and was sized according to the heating
demand of the building. The heating capacity includes the additional reheating capacity needed
to increase room temperatures from night to day setpoint within one hour, as required in some
of the BACS scenarios. Though this house typically has a fireplace, it is assumed that it is not
used. There are five occupants that use the whole house apart from the storage rooms (i.e. no
heaters and no internal gains). Internal and external blinds and window opening control are
added as fixed occupant behaviour to avoid overheating and to provide fresh air. Windows are
opened when the operative temperature exceeds a setpoint given by the running mean outdoor
temperature or if the CO2levels are higher than 1000 ppm. The blinds go down when the
indoor temperature exceeds 23.5 ◦C. These control strategies are only applied when at least one
occupant is at home.
2.2. Building Automation Control
Automation levels D, C, B and A were implemented in R0-3 based on their description in EN
15232 [10]. Only HVAC systems and equipment that were already present in level D were
automated. An overview of the input parameters is given in table 4. As the standard describes
the type of control but does not give specific input parameters, several assumptions were made.
•The heating temperature setpoints in level D are reference values [19]. The setpoints in

SBE19 Thessaloniki
IOP Conf. Series: Earth and Environmental Science 410 (2020) 012054
IOP Publishing
doi:10.1088/1755-1315/410/1/012054
5
level C, B and A are based on survey data, taken from [29].
•Ventilation air flow rates are based on minimum requirements from TEK17 [27].
•The supply air temperature setpoint, when variable, is increasing with the outdoor
temperature as it is mainly used to provide fresh air [30, 31].
•The day/night schedule is based on NS 3031 [19].
•Automation of the domestic hot water system and blinds were outside the scope.
•There is no active cooling system and therefore automation of cooling was irrelevant.
Table 4. Settings for the BACS and TBM functions for levels A, B, C and D.
Heating D [◦C] C [◦C] B [◦C] A [◦C]
control day / night occ. / not occ.
Living 22.0 21.5 21.5 / 19.0 21.5 / 19.0
Bedroom 22.0 19.0 19.0 / 19.0 19.0 / 19.0
Bathroom 22.0 23.0 23.0 / 19.0 23.0 / 19.0
Supply air
temperature
D [◦C] C and B [◦C] A [◦C]
All rooms 18.0 16.0-21.0, depending on Tout 16.0-21.0, depending
on Tin,op
Air flow rate D [L/s m2] C and B [L/s m2] A [L/s m2]
day / night occ. / not occ.
Living (+) CAV, 0.3310.33 / 0.19 0.33 / 0.19
Bedroom (+) CAV, 0.8010.19 / 0.80 0.80 / 0.19
Kitchen (-) CAV, 1.2013.50 / 1.20 3.50 / 1.20
Laundry (-) CAV, 1.60 3.20 / 1.60 3.20 / 1.60
Bathroom (-) CAV, 4.40 8.70 / 4.40 8.70 / 4.40
Toilet (-) CAV, 6.30 6.30 / 6.30 6.30 / 6.30
Lighting
control
D and C B A
All rooms Manual on/off Manual on/off with
day/night schedule
and daylight control
Automatic presence
detection with
daylight control
1In the baseline scenario (R0,D) the supply air is provided by natural ventilation.
3. Energy performance
The delivered energy (see equation 1) and indoor climate (i.e. temperature and CO2levels)
were assessed to evaluate the energy performance of the 16 model versions. There was no energy
production on site and all energy was delivered by electricity (η= 1).
Delivered energy = XEdemand,energy source
ηenergy source
−Eproduced,on site (1)
The results from the energy performance simulations are shown in Figures 2 and 3. Figure 2
shows the effect of BACS integrated with building envelope retrofitting on the energy savings.
The lines represent different retrofitting packages and the symbols represent the BACS levels
from D (left) to A (right). The graph shows the savings from the integrated solutions compared
to the retrofitting scenario without BACS. Up to 21% energy savings were achieved only
by implementing BACS (see figure 2). The slopes of the trendlines indicate that the effect
of automation measures relatively decreases when the delivered energy before implementing

SBE19 Thessaloniki
IOP Conf. Series: Earth and Environmental Science 410 (2020) 012054
IOP Publishing
doi:10.1088/1755-1315/410/1/012054
6
automation is lower (i.e. level D in packages R1-3). This is consistent with results of other
studies [6, 13]. The figure indicates that a similar annual energy consumption can be achieved
in R0,A as in R1,D and similar in R2,A as in R3,D.
Figure 3 compares the simulation results with the estimated savings from the BACS factor
method and shows the relation between them. Integrating BACS with envelope retrofitting can
save up to 60% energy compared to the existing situation. The graph indicates that for this
type of housing in the studied climate, the BACS factor method underestimated the energy
saving potential of BACS (i.e. the achieved savings are higher than estimated). The savings for
detached housing in Trondheim can be estimated by applying a correction factor of: 1.9613 +
1.0698 * BACS factor estimation. This is the correction factor for the overall savings, though
it would be more precise to find the correction factors for thermal and electrical energy.
Figure 2. The effect [%] of upgrading
building automation measures on the deliv-
ered energy in terms of energy savings for
different levels of renovation.
Figure 3. Correlation between the
simulation results and estimated energy
savings according to the BACS factor
method.
3.1. Indoor comfort
Many parameters impact indoor comfort, but in this study only the thermal comfort based
on overheating hours and indoor air quality in terms of CO2levels were evaluated. The
thermal comfort analysis based on EN 15251 [32] showed that the number of unacceptable
hours decreased in R0 and R1 when automation is upgraded, but increased when the house is
retrofitted to a higher level (R2 and R3) (see figure 4). For all cases (R0-3) the unacceptable
hours were mostly due to overheating. As there was no active cooling system installed, it
indicates the assumed window opening behaviour no longer provided sufficient free cooling after
retrofitting. In addition, the supply air temperature can be high during the summer (equals
outdoor temperature). In the automation packages C-A there were less hours in the best category
(category I) due to changed setpoints (i.e. night setback). However, this setpoint is accepted in
Norwegian households and therefore the indoor temperature should be assessed with adaptive
comfort criteria instead. More detailed analysis showed that the indoor temperature increased
after retrofitting and became more stable. CO2levels improved significantly after renovation,
and the best results were achieved with automation level A, because the ventilation is based on
demand.

SBE19 Thessaloniki
IOP Conf. Series: Earth and Environmental Science 410 (2020) 012054
IOP Publishing
doi:10.1088/1755-1315/410/1/012054
7
Figure 4. Total occupied hours [%] per comfort criteria as given in EN 15251 [32].
4. Costs and payback period analysis
Discounted payback period (DPP) as given in equation 2 was used to preliminary assess the
profitability of the model versions. This method gives the number of years to break even by
comparing the yearly energy savings with the initial investment, taking into account the time
value of money.
Discounted payback period = ln[(1 −dI C
ES ∗e∗r)−1]
ln[1 + r](2)
where dIC is the difference in investment cost between the model version and the original in
NOK, ES is the energy savings in kWh per year, e is the energy price in NOK/kWh and r is the
real interest rate, as calculated in [33]. The interest rate was assumed to be 3.0% because of the
low nominal interest on housing loans, the inflation rate was set to 0.03% and the energy price
for electricity including taxes and grid rent was 1.23 NOK/kWh with a yearly price escalation
assumed at 3.15% [34]. The investment costs including removal of old elements were taken from
Norsk Prisbok [35] and were divided into retrofitting and automation packages (see table 5).
Maintenance, replacement and recycling costs were not taken into account.
Table 5. Investment costs in NOK for the retrofitting and BACS packages.
dIC dIC Included in BACS
R0 - Level D 34 387 CAV system
R1 174 401 Level C 76 126 VAV system, weather sensor, general home automation and
thermostat controllers
R2 736 891 Level B 134 967 As R1 + daylight sensors and room control units
R3 893 266 Level A 174 735 As R2 + occupancy sensors and temperature sensors
Figure 5 shows the discounted payback period in years for a fixed energy price and with an
energy price escalation. As mentioned earlier, the energy savings achieved in R0,A were similar
to R1,D and those achieved in R2,A were similar to R3,D. However, the payback period for a
higher level of retrofitting was longer. This means that if the focus is only on saving energy,

SBE19 Thessaloniki
IOP Conf. Series: Earth and Environmental Science 410 (2020) 012054
IOP Publishing
doi:10.1088/1755-1315/410/1/012054
8
it is more profitable combine a lower level of renovation with the highest level of automation
(see figure 6). Though more energy savings are achieved when upgrading to a higher level of
building automation, the payback period does not significantly shorten. This is due to higher
investment cost for achieving a higher level of automation.
Figure 5. Discounted Payback Period (DPP) for the
model versions, both with and without energy price
escalation.
Figure 6. Correlation between
the total energy consumption and
discounted payback period.
5. Limitations and future work
Not all BACS functions were taken into account because the focus was on those functions that
have the most significant impact on energy savings. In addition, this study did not focus on
optimizing the setpoints for the different levels of automation. As choosing the setpoint can
significantly influence the energy consumption, it may be that higher savings can be achieved
when setpoints are optimized. Retrofitting packages R1-3 were not optimized for this building
type, and it was not considered how they are implemented. Therefore, they may not be realistic
options. In addition, the study focused on electricity as a heating source, but these houses
commonly have a fireplace and in retrofitting packages it is common to install an air source heat
pump. Future work should focus on optimizing both retrofitting and BACS packages for this
building type and should include several heating sources. Only two parameters were chosen to
evaluate the indoor climate. Other parameters, such as number of undercooling and overheating
hours and humidity levels, should be evaluated as well. The cost of replacement, maintenance
and recycling were outside the scope. Therefore, the actual payback period will be longer than
presented. When these costs are taken into account, it is more precise to perform a life cycle
cost analysis instead. Future work should also focus on different grid rent tariffs to calculate the
energy costs instead of a fixed energy price. Though the results look promising, they can only
be concluded for a single-family house in the climate of Trondheim. Results may be different for
other housing typologies and for other micro-climates in Norway, which should be investigated
more.
6. Conclusion
The effect of building automation in combination with retrofitting packages was evaluated for a
Norwegian detached house. The study showed that implementing building automation control
systems can result in energy savings up to 21% compared to no automation, regardless the
renovation level. However, these savings are rather low compared to those that can be achieved
by retrofitting the building envelope. By integrating BACS and building envelope renovation,
energy savings up to 60% can be achieved. The results indicated that achieved energy savings

SBE19 Thessaloniki
IOP Conf. Series: Earth and Environmental Science 410 (2020) 012054
IOP Publishing
doi:10.1088/1755-1315/410/1/012054
9
from retrofitting to a lower level and upgrading BACS are similar to when the building is
retrofitted to a higher level without improving BACS. The former option had a shorter payback
period, though more studies and optimization of the packages are needed assess the full effect
of automation on costs, energy and indoor climate.
Acknowledgments
The authors would like to acknowledge the Norwegian University of Science and Technology,
NTNU, for all financial support via the strategic research program ENERSENSE.
References
[1] European Commission Energy performance of buildings accessed: 2019-03-31 URL https://bit.ly/2UncsxY
[2] Economidou M, Atanasiu B, Despret C, Maio J, Nolte I and Rapf O 2011 Buildings Performance Institute
Europe (BPIE) 35–36
[3] Sartori I, Wachenfeldt B J and Hestnes A G 2009 Energy Policy 37 1614–1627
[4] Sandberg N H, Bergsdal H and Brattebø H 2011 Building Research & Information 39 1–15
[5] Felius L C, Dessen F and Hrynyszyn B D Expected 2019 Accepted for publication in Energy Efficiency
[6] Ippolito M, Zizzo G, Piccolo A and Siano P 2014 2014 Int. Symposium on Power Electronics, Electrical
Drives, Automation and Motion (IEEE) pp 1272–1277
[7] Hansen H, Jonassen T, Lϕchen K and Mook V 2017 NVE Hϕringsdokument nr 5–2017
[8] Schønfeldt Karlsen S, Backe S and Hamdy M Expected 2019
[9] Schønfeldt Karlsen S, Hamdy M and Attia S Expected 2019 Submitted to Energy and Buildings
[10] European Committee for Standardization 2017 En 15232-1 - energy performance of buildings
[11] European Committee for Standardization 2017 En iso 52003-1 2017 - energy performance of buildings -
indicators, requirements, ratings and certificates
[12] Ippolito M, Sanseverino E R and Zizzo G 2014 Energy and Buildings 69 33–40
[13] Sanseverino E R, Zizzo G and La Cascia D 2013 2013 Int. Conf. on Clean Electrical Power (ICCEP) (IEEE)
pp 591–595
[14] Vallati A, Grignaffini S, Romagna M and Mauri L 2016 2016 IEEE 16th Int. Conf. on Environment and
Electrical Engineering (EEEIC) (IEEE) pp 1–5
[15] L´opez-Gonz´alez L M, L´opez-Ochoa L M, Las-Heras-Casas J and Garc´ıa-Lozano C 2016 Applied Energy 178
308–322
[16] Hrynyszyn B D and Felius L C 2018 Cold Climate HVAC Conference (Springer) pp 183–193
[17] Thyholt M, Pettersen T D, Haavik T and Wachenfeldt B J 2009 Energy analysis of the norwegian dwelling
stock
[18] EQUA Solutions AB Ida indoor climate and energy (version 4.8) accessed: 2019-03-28 URL https://www.
equa.se/en/ida-ice
[19] Norge S 2016 Ns 3031 - energy performance of buildings: Calculation of energy needs and energy supply
[20] Bøeng A C 2005
[21] Hagen H 1990 Byggforsk 552.103 Oppvarming av boliger. Energiforbruk og kostnader. (SINTEF Byggforsk)
[22] Nord N, Tereshchenko T, Qvistgaard L H and Tryggestad I S 2018 Energy and Buildings 159 75–88
[23] Richardson I, Thomson M, Infield D and Delahunty A 2009 Energy and Buildings 41 781–89
[24] Richardson I, Thomson M, Infield D and Clifford C 2010 Energy and Buildings 42 1878–87
[25] Ahmed K, Pylsy P and Kurnitski J 2016 Solar Energy 137 516–30
[26] Ridley I, Fox J, Oreszczyn T and Hong S H 2003 Int. J. of Ventilation 1209–18
[27] Direktoratet for Byggkvalitet 2017 Byggteknisk forskrift (tek 17)
[28] Norge S 2013 Ns 3700 - criteria for passive houses and low energy buildings: Residential buildings
[29] Halvorsen B and Dalen H M 2012 Ta hjemmetempen (Statistics Norway)
[30] Deme K D, Belafi Z, Gelesz A and Reith A 2015 Proc. of Int. IBPSA Building Simulation Conf., Hyderabad,
India
[31] Hamdy M, Hasan A and Siren K 2011 Energy and Buildings 43 2055–2067
[32] European Committee for Standardization 2007 En 15251 - indoor environmental input parameters for design
and assessment of energy performance of buildings-addressing indoor air quality, thermal environment,
lighting and acoustics
[33] Hamdy M and Mauro G 2017 Energies 10 1016
[34] Statistics Norway Electricity prices accessed: 2019-03-26 URL https://bit.ly/2U7h9Nl
[35] Jensen Ø N and Rud´en O 2018 Norsk Prisbok 2018 (Norconsult Informasjonssystemer AS and Bygganalyse
AS)