Content uploaded by Jean-Nicolas Louis
Author content
All content in this area was uploaded by Jean-Nicolas Louis on May 28, 2014
Content may be subject to copyright.
Smart Houses for Energy Efficiency and Carbon Dioxide Emission Reduction
Jean-Nicolas Louis, Antonio Caló, Eva Pongrácz
Thule Institute, NorTech Oulu
University of Oulu
Oulu, Finland
e-mails: jean-nicolas.louis@oulu.fi, antonio.calo@oulu.fi, eva.pongracz@oulu.fi
Abstract—Understanding the energy consumption of
households is a cornerstone for improving residential energy
efficiency. In addition, the CO
2
emission profile of energy
consumption must be fully understood, to achieve the
decarbonisation of energy sector in Europe. Smart houses
incorporated into smart grids allow the survey and control of
household energy consumption. In this article, the electricity
consumption and its related CO
2
emissions are studied for a
typical Finnish household. A model detached house is used to
simulate the effect of home automation, designed to optimize
energy usage, on the CO
2
emissions of this household. Hourly
electricity production data are used with an hourly electricity
consumption profile generated using fuzzy logic. CO
2
emissions
were obtained from the monthly and weekly electricity
generated data. The CO
2
emissions related to the use of electric
appliances represent around 543 kg
CO2
/y per dwelling when
considering the electricity generated only, and 335 kg
CO2
/y
when balancing the emissions with the exported and imported
electricity. Home automation reduced the CO
2
emissions by 13 %.
Part of emission reduction was achieved through peak shifting,
by moving energy consumption load from daytime to night
time. This paper aims at highlighting the role of home
automation in reducing CO
2
emissions of the residential sector
in the context of smart grid development.
Keywords-CO
2
emissions; Home Automation; Load shifting;
modelling;
I. INTRODUCTION
In December 2013, the European Commission has set
clear goals in its Energy Roadmap 2050
(COM(2011)885/2), to achieve a decarbonised society.
Decarbonisation in this context means reducing greenhouse
gas emissions to 80 % - 95 % below 1990 levels by 2050.
This will provide considerable challenges for electricity
production, consumption and management. Smart grids
represent one tool for achieving this target. Smart grids aim
at increasing the energy efficiency of the network, peak load
shaving, load shifting, and reduction of energy
consumption. Smart buildings are expected to be an integral
part of smart grids, with smart meters as the gateway
allowing the entrance of smartness into the building. Smart
meters receive and send information to and from the
building for use such as in Home Area Networks, and Grid
handling.
The massive deployment of smart meters under way in
Europe facilitates digital measurements, and will allow a
consequent access to energy consumption data to energy
companies and authorities. European Union (EU) Member
States have the obligation of implementing smart meters
covering 80 % of consumers by 2020 at the latest [1]. In
contrast to the European Energy Efficiency Directive
(2012/27/EU) [1], the Finnish Electricity Market law
588/2013 and its application Act 2009/66 on the electricity
supply in the survey and measurement sets the deadline of
2014 [2]. Legal obligations to increase energy efficiency
also provide a motivation to the deployment of renewable
energy sources (RES) as a vector for energy production,
both electrical and heating, in a large scale as well as in the
buildings. Home energy management can have a significant
role in contributing to energy efficiency and cutting or
shifting peak load. This can be achieved through an active
collaboration of energy consuming systems and the
information network on a local level [3]. Putting together
the different factors, smart grids, smart building, RES-based
heat and electricity and energy efficiency, involve the
development of a smart energy network (SEN), capable of
managing the energy system through constant monitoring.
The impact of energy efficiency on emissions from the
residential sector ha
s been a subject of much research [4]. It
has been shown that electric load shifting from the
residential sector may reduce air pollution in urban areas
[5]. To this effect, developing mathematical tools that are
able to anticipate and cut emissions through the deployment
of smart systems and home automation is of major
importance.
This article aims at exploring the significance of home
automation and its impact on the carbon emissions of
dwelling, and the possible ways home automation can
contribute to decarbonisation. In the first section of the
paper, a description of the CO
2
emissions from the
production and the use of electricity in Finland will be
presented. The second section presents the methodology
used for translating hourly carbon emissions to single
households will be described. The third section shows and
details the results from the simulations carried out on two
chosen type of dwelling which will be described and
analysed.
II. R
ELATED RESEARCHES
Researches on smart houses and their development have
been going on for quite some times. Smart homes can be
broadly seen as a building monitored and controlled for
multiple purposes [6]. The energy management feature
taken in charge by the smart home is one aspect that has
44Copyright (c) IARIA, 2014. ISBN: 978-1-61208-332-2
ENERGY 2014 : The Fourth International Conference on Smart Grids, Green Communications and IT Energy-aware Technologies
been developed. Algorithms for generating electricity
consumption load profile have been developed on an hourly
and half-hourly basis [7], but also with a finer grid on a
minute-basis [8]. These algorithms can be further used to
emphasise the potential of energy smart houses and their
roles in improving the energy efficiency, reducing the
energy consumption, and reducing the carbon emissions
from the energy used. More elaborated algorithms have
been developed where the integration of each appliance
within the dwelling have been modelled [9], [10]. Finally,
the management of appliances within the dwelling may as
well be implemented in simulation for optimizing their
usage and enhancing demand-side management [11], [12].
Previous studies have attempted to measure the impact of
energy efficiency measures on the CO
2
emissions from the
residential sector [4]. Detailed algorithms for evaluating the
CO
2
emissions from appliances usage have been proposed
[13]. One of the main drawbacks of the previous methods is
that the carbon emissions are based on a fixed coefficient,
thus limiting the understanding of the CO
2
emissions
mechanism. A more dynamic model has been elaborated for
estimating the CO
2
emissions and their impact on demand
response [14]. Although the last research has based its
dynamism on real dataset of energy production on an hourly
basis for various countries, the CO
2
emissions related to the
production of electricity is based on the IEA annual report
on CO
2
emissions [15]. Consequently, studies on segmented
electricity production, related CO
2
emissions, and the
impact of home automation on the emissions are lacking.
III. E
LECTRICITY CONSUMPTION AND CARBON EMISSIONS
IN
FINLAND
The role of the residential sector in reducing carbon
emissions is paramount in the development of the future
smart grid [16]. In terms of CO
2
emission reduction in the
residential sector, the largest effort should be made in
retrofitting buildings. The average renewal time of the
residential sector is estimated to be around 70 years [4]. The
influence of technology on CO
2
emissions needs to be
highlighted. Consequently, technology upgrading can
greatly influence the total CO
2
emissions of the residential
sector. In Finland, lighting consumes over 30 % of the total
electricity used in households for appliances [17]. The
upgrade of lighting technology is one way for impacting
energy consumption [9], but also for reducing carbon
emissions [18]. Furthermore, home energy management
systems will continue to play a role for increasing energy
efficiency, reducing energy consumption [19] and allow
load shifting.
Electricity generation and consumption is being
constantly surveyed, recorded and reported by the Official
Statistics of Finland. In 2012, household appliances
consumed 8 072 GWh of electricity [17]. At the same time,
2 579 781 households were registered in Finland [20],
resulting in an average consumption of 3 129 kWh/y.dw
-1
.
There can be considerable deviation from this average value,
if the households is in an apartment building or a detached
house [21]. Furthermore, the total electricity production in
Finland was around 67.7 TWh in 2012, while the total
consumption of electricity was around 82.9 TWh, and a total
of 8.4 MtCO
2
were emitted. Therefore, it can be estimated
that the share of electricity using devices in the total CO
2
emissions from electricity production and use are 1001
tCO
2
/GWh
pro
or 817 tCO
2
/GWh
cons
.
IV. M
ETHODOLOGY
A. Electricity consumption profile
The house electricity demand profile is drawn on an
hourly basis using different components for evaluating the
electricity consumption from appliances, without primary
and secondary electric heating systems. Two dwellings will
be studied: one having home automation and one without
home automation and the difference in their CO
2
balance
will be evaluated.
The modelled house contains twenty-one appliances, all
of them labelled A or B [9]. The house being in Finland, one
of the appliances is an electric sauna stove. The sauna stove
used for the modelled house was set to be 6 kW. The overall
electricity consumption of appliances in this modelled house
is 4 501 kWh/y, which correlates with the findings of the
European ODYSSEE MURE project and that of the
Sähkötohtori Analysis
[21]. The measured data were
obtained from detached houses in Oulu, Finland, which were
equipped with a 10 kW sauna stove.
B. Hourly electricity generation and emissions
Data acquisition consisted of analysing the electricity
generation of all power plants in Finland and the categories
of power plants on an hourly basis. Secondly, the carbon
dioxide emissions associated with the aforementioned
categories were calculated on an hourly basis. Monthly CO
2
emissions are available from July 2007 to October 2013
[22]. It is then possible to evaluate the CO
2
emissions on an
hourly basis by associating both elements, the primary
energy source for electricity generation and the associated
monthly CO
2
emissions.
The electricity generated in Finland on an hourly basis is
reported by the Finnish Transmission Service Operator –
Fingrid since 2004 [23]. The data is split into two groups: the
electricity generated from the power plants and the electrical
load on the network taking into consideration the import and
export of electricity. Moreover, the Finnish Industry
Association (Energiateollisuus) recorded the weekly
electricity generated from 1990 [24], which has been broken
down between the energy technology used for producing the
electricity: wind, hydropower, nuclear, CHP Industry, CHP
district heating, conventional and gas turbine power plant.
Finally, Fingrid informs in real-time the state of the network
using the same categories as mentioned above. Thus, for
building up the hourly electricity generation by categories for
the year 2012, the weekly average electricity production by
category is used in parallel with the hourly electricity
generated countrywide. The exported electricity is
45Copyright (c) IARIA, 2014. ISBN: 978-1-61208-332-2
ENERGY 2014 : The Fourth International Conference on Smart Grids, Green Communications and IT Energy-aware Technologies
considered in the electricity generated and in the
corresponding CO
2
emissions. The imported electricity is
considered as a share of the CO
2
emissions from electricity
consumption in Finland. In order to include the imported
electricity into the overall emissions from electricity
consumption in Finland, it is necessary to know the energy
mix for producing the electricity of the country from which
Finland is importing. The hourly electricity generated from a
particular energy source in the primary country is evaluated
using (1). The notation h, w, and m designate the hourly,
weekly, and monthly time step respectively, i is the energy
technology used for producing the electricity, and tot stands
for the total amount of a unit countrywide.
(1)
Where P
h,i
is the electric energy generated by a given
technology per hour [MWh/h], P
w,i
is the electric energy
generated on a weekly basis by a given technology
[MWh/w], P
w,tot
is the total amount of electricity produced
in Finland per week [MWh/w], and P
h,tot
is the total amount
of electricity produced per hour [MWh/h].
Once the hourly electricity generated by technology has
been defined, it is possible to evaluate the hourly emissions
from the power plant park.
C. The CO
2
emissions from power plants
As part of the its legal obligation, Finland is publishing
the CO
2
emissions from power plants, and every energy
intensive industry reports its expected and measured CO
2
emissions for each site [25]. The Finnish Industry
Association estimates monthly specific emissions related to
electricity production, based on the type of fuel used by the
energy industry [24]. By knowing the hourly electricity
production from each sector, it results in estimating the CO
2
emissions for each hour countrywide using (2) to (5).
(2)
Where, a is evaluated using (3) if the full week is within
the same month n, or (4) if the full week is between two
months, n and n+1.
(3)
(4)
Thus, the hourly emission is given by,
(5)
Where E
h,i-gen
is the total emissions from the electricity
generated hourly [ktCO
2
/h], and E
w,tot
is the total weekly
emissions for all power plants [ktCO
2
/m],
δ
w
is the day
number within a week where Monday is 1 and Sunday is 7,
δ
m
is the number of days in the studied month, E
m,n
is the
monthly CO
2
emissions for the month n. Fig. 1 (a) illustrates
the energy generated and its corresponding CO
2
emissions
on an hourly basis for the year 2012 in Finland. It can be
noticed that, although there is a strong correlation of CO
2
emissions to electricity generation, emissions may decrease
even though the energy generation increases, due to the fact
the energy mix is changing Fig. 1 (b).
The emissions due to the electricity imported are further
implemented to the primary emissions from the electricity
generated within the country. The CO
2
emissions from the
electricity generated dedicated to the export is further
subtracted from the hourly emissions E
h,c1
. In order to
account the total CO
2
emissions from the electricity load in
the country, the emissions from each country with which
Finland is trading electricity are evaluated, meaning
Norway, Sweden, Russia and Estonia. As the hourly energy
mix is not known for each country, a general coefficient of
CO
2
emissions has been considered for each country named
P
h,i
=
P
w,i
P
w,tot
! P
h,tot
E
w,i
= a !
P
w,i
P
m,i
!
!
m
7
!
"
#
$
%
&
!
a =
7E
m,n
!
m
!!
a =
!
w
!
E
m,n
!
m
!
"
#
$
%
&
+
E
m,n+1
!
m
! 7 "
!
w
( )
!
"
#
$
%
&
E
h,i!gen
=
P
h,i
P
w,i
" E
w,tot
(a) (b)
Figure 1. Hourly electricity generation, net import and their related CO
2
emissions in (a). 2012, and (b). from 4.02-02.03.2012.
46Copyright (c) IARIA, 2014. ISBN: 978-1-61208-332-2
ENERGY 2014 : The Fourth International Conference on Smart Grids, Green Communications and IT Energy-aware Technologies
previously, respectively 30, 17, 384 and 1 014 kgCO
2
/MWh
pro
[15].
The share of CO
2
emissions coming from each trading
country is evaluated using (6).
(6)
Where, E
h,cn
is the hourly emissions for each participating
country to the electricity trade [kgCO
2
/h], P
h,load
is the
hourly electric load on the Finnish network [MWh/h], P
h,net-cn
is the net balance of electricity traded between Finland and
the country n [MWh/h] in case of export or the difference
between the electricity generated and the electricity
exported in the case of Finland, and E
cn
is the coefficient of
CO
2
emissions for the corresponding country
[kgCO
2
/MWh]. In case P
h,net-cn
is negative, the coefficient of
CO
2
emissions is equal to E
h,i-gen
as the emissions from the
Finnish production is exported as well, otherwise, E
cn
takes
the value defined by the IEA.
Finally the hourly emissions E
h
are determined as the
sum of the hourly emissions for each participating country
to the electricity trade E
h,cn
as shown in (7).
(7)
The emission data in Fig. 1 are then translated to a single
household where the hourly electricity consumption profile
has been previously generated using (8).
(8)
Where E
h,house
is the hourly emissions from the household
[kgCO
2
/h], and P
j,house
is the total hourly electricity consumed
by the household excluding the electric heating [kWh/h].
Two cases are differentiated: CO
2
levels towards the
production of electricity within the primary country, the net
CO
2
emissions level considering the import and export as
presented in this section. In the first case, P takes the value
of the total electricity produced in the primary country P
h,tot
.
In the second case, P takes the value of the total load on the
electric grid of the primary country P
h,load
.
The results give an estimate of CO
2
emissions related to
the electricity consumption in a private household on an
hourly basis. This model is then applied to the previously
modelled dwelling in order to estimate the daily CO
2
emissions from an average Finnish dwelling.
V. R
ESULTS AND DISCUSSION
The model showed that the CO
2
emissions are highly
dependent on electric consumption levels. Depending on the
energy mix for electricity production at a given time, CO
2
emission levels may be lower at peak hours and thus not
proportional to consumption levels. Two models have been
developed. In the first case, the CO
2
emissions from the
dwelling are accounted relatively to the electricity
production only. In the second case, the CO
2
emissions from
the dwelling are balanced with the electricity exported and
imported. Fig. 3 represents the energy consumption for the
two modelled dwellings with home automation (Fig. 3 (a)),
and without home automation (Fig. 3 (b)). The electricity
consumption shown was extracted for a randomly selected
week in May 2012, starting on Monday, the 23
rd
of May.
A. Case 1: Emissions related to the electricity production
These dwellings are similar in their characteristics e.g.
number and types of appliances, number of inhabitants,
dwellings dimensions, or users’ habits. The CO
2
emission
levels vary from 0.06 to 0.20 kgCO
2
.kWh
-1
. The levels
depend on the energy mix of Finland’s electricity generation.
Consequently, the emissions from the dwelling, on an hourly
basis, peak at 1.93 kgCO
2
.h
-1
for the dwelling without home
automation and 1.81 kgCO
2
.h
-1
for the dwelling with home
automation. In the first peak emission case, the related
energy demand was 10.03 kWh.h
-1
and, in the second peak
emission case, 9.42 kWh.h
-1
. The maximum electricity
consumption in the first case is 12.33 kWh.h
-1
, and 10.16
kWh.h
-1
. The emission peaks are somewhat related to the
level of electricity consumption but also to the energy mix
for electricity generation at the same time. The use of home
automation may reduce the instantaneous peak of CO
2
emissions. The daily electricity profile of the dwellings and
CO
2
balance between the two dwellings are represented in
Fig. 3 (c).
The difference in the profile of the two modelled
dwellings result in a 592 kWh.y
-1
reduction of total
electricity consumption. In terms of CO
2
emissions, the
dwelling that is not equipped with a home automation emits
543 kgCO
2
.y
-1
, while the house with home automation emit
473 kgCO
2
.y
-1
. The amount of CO
2
saved represents 12.78 %
of original emissions.
The home automation shifted some of the electricity
consumption from the evening peak to the night. It resulted
in a decrease of the CO
2
emissions in the evening down to 37 %
from the original level, and an increase of 51 % of CO
2
emissions overnight (Fig 3. (d)). Considering, however, that
the emissions overnight are about 0.1 kgCO
2
.h
-1
on average,
E
h, c
n
=
P
h,net !c
n
P
h
,
load
" E
c
n
n= 2
n
#
E
h
= E
h, c
n
n
=
1
n
!
E
h,house
=
P
j,house
P
h,tot
! E
h
!10
3
j
"
Figure 2. Total and average daily profile of the carbon dioxide
emission in 2012, Finland
47Copyright (c) IARIA, 2014. ISBN: 978-1-61208-332-2
ENERGY 2014 : The Fourth International Conference on Smart Grids, Green Communications and IT Energy-aware Technologies
this can be regarded as relatively small cumulative amount.
The emissions increased overnight by 3 to 5 kgCO
2
, and
reduced by 17 kgCO
2
on average over the whole year during
the evening.
While the home automation was not optimised for
reducing CO
2
emissions but for cutting the building peak
load consumption, it resulted in the decrease of CO
2
emissions that relates to the electricity consumption.
Notwithstanding, it is to be seen that the emissions related to
electricity generation countrywide vary throughout the day.
Fig. 2 represents the summed CO
2
emissions per hour on the
left axis and the hourly average profile of CO
2
emissions on
the right axis for the year 2012 from the electricity produced
in Finland.
The CO
2
emissions during the peak hours are 0.95
ktCO
2
.h
-1
on average, and correspond to a total of 346 kt
CO2
between 6 and 7 pm. The lowest point on the daily plot of
CO
2
emissions occurs around 2 and 3 am, with an average
emission of 0.8 ktCO
2
.h
-1
and a corresponding emission for
this particular hour throughout the year of 294 ktCO
2
.
B. Case 2: Emissions related to the net load
The CO
2
emissions in this 2
nd
case were found much
lower than in the first case. Firstly, the total CO
2
emissions
factor E
h,i-gen
has slightly decreased. This can be interpreted
as an improvement in the global CO
2
emissions from the
electric load at the country level. This is explained by the
fact that Finland is mostly importing its electricity from
Sweden and in second place from Russia. Norway and
Estonia represents a small share of the electricity trade. On
the one hand, Sweden has an average emission factor
around 7 times smaller than the one of Finland, and on the
other hand, Finland is exporting electricity with a high
emission factor. Also, as the emissions from the Finnish
electricity has been calculated for every hour, it introduce
some peaks of CO
2
emissions while the electricity from the
surrounding countries are applied a constant factor, thus
bring a bias result. Nevertheless, the exchange of electricity
is beneficial for Finland in terms of CO
2
emissions. The
same dwellings that the one mentioned in case 1 were found
to have 335 kgCO
2
.y
-1
in case the home automation was not
simulated, and 293 kgCO
2
.y
-1
when home automation was
running. For both dwellings, the difference between case 1
and 2 is around 38 %. This shows that the CO
2
emissions
can be interpreted very differently depending on whether the
Figure 3. Electricity consumption with its related CO
2
emissions for (a) a dwelling with automation and (b) a dwelling without automation,
(c) Daily electricity consumption profiles and (d) its related CO
2
emissions balance between 2 dwellings.
Mon
Tue
Wed
Thu
Fri
Sat
Sun
Mon
23/05/2012 24/05/2012 25/05/2012 26/05/2012 27/05/2012 28/05/2012 29/05/2012 30/05/2012
0
1
2
3
4
5
6
7
8
Date
Electricity Consumption [kWh/h]
Time [day]
Household_Sauna_No_Automation
0.0
0.5
1.0
1.5
2.0
Related CO 2 Emissions
Balanced with Export - Without Automation
CO2 Emissions [kgCO2]
Mon
Tue
Wed
Thu
Fri
Sat
Sun
Mon
23/05/2012 24/05/2012 25/05/2012 26/05/2012 27/05/2012 28/05/2012 29/05/2012 30/05/2012
0
1
2
3
4
5
6
7
8
Date
Electricity Consumption [kWh/h]
Time [day]
Houseold_Sauna_Automation
0.0
0.5
1.0
1.5
2.0
Related CO 2 Emissions
Balanced with Export - With Automation
CO2 Emissions [kgCO2]
(a)
(b)
(c)
(d)
48Copyright (c) IARIA, 2014. ISBN: 978-1-61208-332-2
ENERGY 2014 : The Fourth International Conference on Smart Grids, Green Communications and IT Energy-aware Technologies
produced electricity, dedicated to the export, is subtracted
from the overall electricity consumption of the country or if
it should be included into the total CO
2
emissions.
Similarly, the peak of CO
2
emissions due to the
electricity consumption in the dwellings are reduced
compared to the Case 1 by 24 %. In the Case 2, the peak of
CO
2
emissions for the dwelling without home automation
reach 1.46 kgCO
2
/h, and 1.37 in case the home automation
is running in the dwelling.
At the system level, the total and average hourly CO
2
emissions have decreased as well. In case the exported and
imported electricity are accounted in the total emissions, the
low peak occurs between 4-5 am with an average emissions
of 0.64 ktCO
2
and the high peak period occurs between 10-
11 am with an average emissions of 0.75 ktCO
2
and
cumulates 275 ktCO
2
for the same hour.
Regarding the shift of CO
2
emissions due to the home
automation device and the feedback strategies used for
informing the private consumers, it has decreased by 6
kgCO
2
in the evening and has risen by 2.7 kgCO
2
in the
night time. The quantities of CO
2
shifted, presented in Fig. 3
(d), are different from Case 1 to Case 2 as the CO
2
emission
profile for both cases are different, as shown in Fig. 2.
C. Discussion
Both cases showed that load shifting can contribute to
12.7 % decrease in CO
2
emissions. However, there is a
difference depending if the balance of import and export is
considered.
As well, consumer awareness and their willingness to
comply is also a factor in the potential for reducing CO
2
emissions. Table 1 summarises the results from the CO
2
emissions and the electricity consumption from both
dwellings.
It is necessary to point out the importance of methods
evaluating emissions on the results. It is paramount that the
countries involved use the same methodology for their CO
2
evaluation. In this study, Finland is mostly importing
electricity from Sweden and Russia and exporting to
Norway and Estonia. For Sweden, it results in importing
“polluted” electricity and exporting cleaner electricity to
Finland. Consequently, for Finland, the shifting of CO
2
emissions is greater when relating the emissions to the gross
electricity production. Multi-objective algorithms will need
to be developed for optimising electricity consumption
and/or CO
2
emissions. In addition, an added level of
complexity is if export/import net emissions are considered
or not.
VI. C
ONCLUSIONS
The article detailed the CO
2
emissions of electricity
generation in Finland. Firstly, monthly and weekly data of
electricity generation were used to calculate corresponding
CO
2
emissions into hourly data. This was used to evaluate
the CO
2
emission profile of households. The model was
based on hourly electricity load profiles previously built.
Secondly, the CO
2
emissions associated with imported
and exported electricity generation were accounted as well.
Both cases show the same peak distribution in their daily
profile. Notwithstanding, emissions will depend on the fuel
used at a particular hour. Therefore, the relationship
between electricity production, import and export is not
straightforward. The cumulated carbon emission overnight
from the electricity produced in Finland stands at around
290 ktCO
2
.h
-1
, while the peak reaches 345 ktCO
2
.h
-1
.
Considering the import and export of electricity, and their
related CO
2
emissions, the peak dropped to 230 ktCO
2
.h
-1
overnight, and the high peak at 275 ktCO
2
.h
-1
, respectively.
Although the home automation has not been optimised
for reducing the CO
2
emissions from the modelled
household, the CO
2
emissions from the electricity
consumption are somehow proportional to electricity
consumption levels. The study showed that home
automation might reduce the carbon emission by 12.7 %
while influencing the private consumers’ everyday routine.
The CO
2
emissions have been reduced most substantially
during the evening peak, by 18 kg
CO2
/h.y
-1
in the first case
and by 6 kg
CO2
/h.y
-1
in the second case, while the emissions
at night time have increased from 3 to 5 kg
CO2
/h.y
-1
on
average. Although the CO
2
emissions related to electricity
consumption from appliances are strongly correlated, the
energy mix for producing this electricity needs to be
considered and thus optimised for reducing the carbon
footprint of households.
Consequently, smart buildings within a smart grid may
not only participate to load shifting, increase energy
efficiency or decrease in electricity consumption, but can
also contribute significantly to the reduction of CO
2
emissions. It will, in turn, impact the total CO
2
emissions of
the country and will assist in achieving the decarbonisation
goal of the EU.
This limitation of this research is that there was no
information available on the variation of the energy mix
from the exporting countries, therefore, the import
TABLE I. CO
2
EMISSIONS SUMMARY FOR THE TWO STUDIED CASES
CO
2
emissions relative to
Electricity
produced
Net electricity
consumed
Unit
Min. E
h,i-gen
0.06
0.04
kgCO
2
/kWh
Max. E
h,i-gen
0.20
0.19
Max E
h,house
SA
1.81
1.37
kgCO
2
/h
Max E
h,house
SNA 1.93 1.46
Max P
j,house
SA
12.33
12.33
kWh/h
Max P
j,house
SNA
10.16
10.16
Total E
h,house
SA
543
335
kgCO
2
/a
Total E
h,house
SNA
473
293
Max Average E
h,i-gen
0.95
0.75
ktCO
2
Min Average E
h,i-gen
0.8
0.64
Max Sum E
h,i-gen
346 275
Min Sum E
h,i-gen
294
234
(b)
49Copyright (c) IARIA, 2014. ISBN: 978-1-61208-332-2
ENERGY 2014 : The Fourth International Conference on Smart Grids, Green Communications and IT Energy-aware Technologies
electricity had to be considered with a yearly constant CO
2
emission factor. Secondly, in the case of Finland, a more
detailed estimation would require knowing the energy mix
hour-by-hour, rather than estimating it from the monthly
average.
Further research will investigate the impact of private
consumers in correlation with home automation for reducing
the CO
2
emissions of households. In addition, a full
assessment considering district-heating systems shall be
done, in order to achieve full integration of smart buildings
in a smart energy network. Finally, the multi-objective
algorithms will have to be further developed.
A
CKNOWLEDGEMENT
The authors thank the Thule Institute Doctoral Programme
for financing this research.
R
EFERENCES
[1] Publications Office, Directive 2012/27/EU of the European
Parliament and of the Council of 25 October 2012 on energy
efficiency, amending Directives 2009/125/EC and
2010/30/EU and repealing Directives 2004/8/EC and
2006/32/EC Text with EEA relevance. 2012, pp. 1–56.
[2] TEM, Valtioneuvoston asetus sähköntoimitusten
selvityksestä ja mittauksesta. 2013, pp. 1–6.
[3] D. Long Ha, S. Ploix, M. Jacomino, and M. Hoang Le,
“Home energy management problem: towards an optimal
and robust solution,” in Energy Management, no. 5, F. Macia
Perez, Ed. InTech, 2010, pp. 77–106.
[4] H. Hens, G. Verbeeck, and B. Verdonck, “Impact of energy
efficiency measures on the CO
2
emissions in the residential
sector, a large scale analysis,” Energy & Buildings, vol. 33,
Jan. 2001, pp. 275–281.
[5] N. Gilbraith and S. E. Powers, “Residential demand response
reduces air pollutant emissions on peak electricity demand
days in New York City,” Energy Policy, vol. 59, no. C, Aug.
2013, pp. 459–469.
[6] L. C. De Silva, C. Morikawa, and I. M. Petra, “State of the
art of smart homes,” Engineering Applications of Artificial
Intelligence, vol. 25, no. 7, Oct. 2012, pp. 1313–1321.
[7] M. Stokes, “Removing barriers to embedded generation: a
fine-grained load model to support low voltage network
performance analysis,” Institute of Energy and Sustainable
Development de Montfort University, Leicester, 2005.
[8] A. Grandjean, J. Adnot, and G. Binet, “A review and an
analysis of the residential electric load curve models,”
Renewable and Sustainable Energy Reviews, vol. 16, no. 9,
Dec. 2012, pp. 6539–6565.
[9] J.-N. Louis, A. Caló, K. Leiviskä, and E. Pongrácz, “Home
Automation for a sustainable living – Modelling a detached
house in Northern Finland,” Proceedings of the 7th
International Conference on Energy Efficiency in Domestic
Appliances and Lighting EEDAL’13, Sep. 2013, pp. 1–11.
[10] N. Arghira, L. Hawarah, S. Ploix, and M. Jacomino,
“Prediction of appliances energy use in smart homes,”
Energy, vol. 48, no. 1, Dec. 2012, pp. 128–134.
[11] R. Missaoui, H. Joumaa, S. Ploix, and S. Bacha, “Managing
Energy Smart Homes according to Energy Prices: Analysis
of a Building Energy Management System,” Energy &
Buildings, vol. 71, Mar. 2014, pp. 155–167.
[12] P. Chavali, P. Yang, and A. Nehorai, “A Distributed
Algorithm of Appliance Scheduling for Home Energy
Management System,” IEEE Trans. Smart Grid, vol. 5, no. 1,
Jan. 2014, pp. 282–290.
[13] R. Huang, M. Itou, T. Tamura, and J. Ma, “Agents based
Approach for Smart Eco-home Environments,” presented at
the The 2010 International Joint Conference on Neural
Networks (IJCNN), 2010, pp. 1–8.
[14] P. Stoll, N. Brandt, and L. Nordström, “Including dynamic
CO
2
intensity with demand response,” Energy Policy, vol.
65, no. C, Feb. 2014, pp. 490–500.
[15] IEA, International Energy Agency, “CO
2
Emissions From
Fuel Combustion Highlights 2013,” Oct. 2013, pp. 1–158.
[16] S. J. Darby, J. Stromback, and M. Wilks, “Potential carbon
impacts of smart grid development in six European
countries,” Energy Efficiency, vol. 6, no. 4, May 2013, pp.
725–739.
[17] Official Statistics Finland, Ed., Energy Consumption in
households by energy source in 2012, 2012. [Online].
Available: http://www.stat.fi/til/asen/2012/asen_2012_2013-
11-13_tau_002_en.html. [Accessed: 05-Dec-2013].
[18] E. Tetri, A. Sarvaranta, and S. Syri, “Potential of new
lighting technologies in reducing household lighting energy
use and CO
2
emissions in Finland,” Energy Efficiency, Dec.
2013, pp. 1–12.
[19] M. G. Ippolito, E. R. Sanseverino, and G. Zizzo, “Impact of
building automation control systems and technical building
management systems on the energy performance class of
residential buildings: An Italian case study,” Energy &
Buildings, vol. 69, Feb. 2014, pp. 33–40.
[20] Official Statistics Finland, Ed., Household-Dwelling units by
number of persons 1960 - 2012. [Online]. Available:
http://www.stat.fi/til/asas/2012/asas_2012_2013-05-
22_tau_001_en.html. [Accessed: 05-Dec-2013].
[21] V. Rouhiainen, “Decomposing Electricity Use of Finnish
Households to Appliance Categories,” presented at the
Energy efficiency in domestic appliances and lighting,
Proceedings of the 5th international conference EEDAL '09,
Berlin, 2010, vol. 2, pp. 438–455.
[22]
Finnish Industry Association, “Power generation in Finland -
fuels and CO
2-
emissions,” Energiateollisuus, Helsinki, Nov.
2013.
[23] Fingrid Oyj, Ed., Load and generation. [Online]. Available:
http://www.fingrid.fi/en/electricity-market/load-and-
generation/Pages/default.aspx. [Accessed: 04-Dec-2013].
[24] Finnish Industry Association, “Electricity net production,
imports and exports, in Finland,” no. 258. Energiateollisuus,
Helsinki, 28-Nov-2013.
[25] Energiamarkkinavirasto, “Emission Allowance Balance.”
EMV, Helsinki, 28-Nov-2013.
50Copyright (c) IARIA, 2014. ISBN: 978-1-61208-332-2
ENERGY 2014 : The Fourth International Conference on Smart Grids, Green Communications and IT Energy-aware Technologies