1. Technical guides for owner/manager of an air conditioning
system: volume 7
A benchmarking guide adapted to air
conditioning based on electricity bills
1

2. Team
France (Project coordinator)
Armines - Mines de Paris
Austria
Slovenia
Austrian Energy Agency
University of Ljubljana
Belgium UK
Université de Liège Association of Building
Engineers
Italy BRE
Politecnico di Torino (Building Research
Establishment Ltd)
Portugal
University of Porto Welsh School of
Architecture
Eurovent-Certification
Authors of this volume
Jérôme ADNOT (Armines, France)
Daniela BORY (Armines, France)
Maxime DUPONT (Armines, France)
Roger HITCHIN (BRE, UK)
The sole responsibility for the content of this publication lies with the authors. It does not
represent the opinion of the European Communities. The European Commission is not
responsible for any use that may be made of the information contained therein.
2

3. Objectives of the guide
The objective of this guide is mainly to help building owners to detect changes in energy
consumption of their air conditioning plant as a first step in identifying and correcting faults. Even
in the absence of obvious faults , building owners should monitor energy consumption and be
alert for early signs of problems. This will enable them to react promptly through behavioral
changes, operational changes (system adjustments), investment in component replacement or
in extreme cases with a complete retrofit. When changes have been introduced, monitoring
allows the effectiveness and actual payback times to be assessed for future use.
In order to help the managers an action plan has been defined: a complete scheme of the plan is
reported in the next page and it is detailed all along the document.
Moreover, some example of ratios of energy consumption are presented in the annex as
indicative values that should be considered for their order of magnitude more than the value
itself.
3

4. ACTION PLAN
PRELIMINARY ACTIONS
Determine the electricity uses included in the bill in general
Collect electricity bills (prefer monthly to annual basis to get more accuracy)
Determine parameters having effect on electricity consumption in general
1st Part: Basic benchmarking of 2nd Part: Benchmark typical energy 3rd Part: climate based energy 4th Part: multi-parameter energy
energy consumption ratios signature signature
Collect activity indicators that might Choose main activity indicators that Determine the best time period Choose main activity and climate
justify variations in electricity justify variations in electricity (monthly/weekly for DD, daily for average indicators that justify variations in
consumption consumption temperature) electricity consumption
Note any changes other that climate Calculate electricity consumption on the Determine the best temperature reference Determine the best time period (monthly,
(process, adjustments) from one time time period (the same as the one used in (usually 18°C in winter, from 14°C to 24°C weekly, daily)
period to another statistics) in summer)
Measure/get every chosen indicators for
Collect electricity bills (prefer monthly For each activity indicator and the same Measure/get average daily temperature or each time period
to annual basis to get more accuracy) time period, divide energy consumption calculate/get degree-days for each time
by the indicator period Measure/get from bills the electricity
Draw the diagram: time period on the consumption of each time period
X-axis and kWh/time period on the Y- Compare each ratio from one time period Separate cooling degree-days from If enough dots are available, establish
axis to another heating degree-days the linear regression (E=Σai.Pi + b)
between electricity consumption E and
Try to explain variations in Try to explain possible variations Measure /get from the bills electricity every parameters Pi
consumption thanks to previous (climate, adjustments etc…) consumption on each time period
changes or activity indicators Try to find the reasons why a new dot is
Compare each ratio to statistics Draw the diagram for each time period: outside the scatter – If differences are
If variations are unexplainable, call a average temperature or DD on the X-axis too important, too frequent and
professional (energy supplier, ESCO Try to explain possible differences (size, and kWh on the Y-axis unexplainable, call a professional
etc.) for an energy audit climate, adjustments etc…) As soon as a season/year is complete, (energy supplier, ESCO etc.) for an
determine the (linear) regression between energy audit
Try to continue that follow-up of If variations/differences are consumption and climate parameters
energy consumption even if no unexplainable, call a professional Calculate energy ratios by extracting
problem is detected (energy supplier, ESCO etc.) for an Try to find the reasons why a new dot is multiplier coefficients ai
energy audit outside the scatter – If differences are too
important, too frequent and Compare to statistics – Try to explain
Try to continue that benchmarking of unexplainable, call a professional (energy possible differences (size, activity,
energy ratios even if no problem is supplier, ESCO etc.) for an energy audit adjustments etc…)
detected Calculate climate independent energy
ratios (slope of the regression curve) in Try to continue the energy signature
kWh/m2.DD even if no problem is detected
Compare to statistics – Try to explain
possible differences (size, activity,
adjustments etc…) 4

5. 1. Pre-requisite
Energy uses having effect on the electricity bill of a building
Any building owner must be aware that, for more accuracy, direct measurements on individual
items of equipment (chiller for example) or energy use (lighting for example) - which may need to
be organized by himself – are preferable overall utility energy bills. The latter combine energy
consumption by numerous end-uses and may relate to time periods that are not ideal for
monitoring purposes.
The total electricity bill mainly includes the consumption of lighting, ventilating and small power
appliances (kitchen or office equipments, HI-FI etc…) and sometimes process loads such as air
compressors, pumps, fans etc…Air conditioning, is included because non-electric cooling is rare.
Building heating and domestic water heating can also be provided by the Joule effect (direct
electric) or with a heat pump. However these two activities often use fossil fuels and are then
accounted in a separate bill.
Interpretation of electricity bills requires the listing of each type of electrical appliances whose
consumption is included in the energy bill. When possible, try to evaluate the electrical power
they absorb in operation by looking at their electrical plate or documentation (see Table 1).
Appliances type Number of Electric unit Operation hours Energy (kWh)
equipments power
Computers 40 300 4380 13140
Printers type A 10 400 500 2000
(20 stand by) 1000 200
Bulbs type X 50 60 5000 3000
Bulbs type Y 10 100 5000 5000
Neon type Z 70 40 1540 616
Electric heaters 10 1200 2500 30000
Etc. …. … … …
Total energy (kWh) xxx
Table 1 An example of listing the electric appliances and their nominal power.
It is important also to estimate the number of hours they operate during the period covered by
the meter readings.. Absorbed power and time of operation can be used to estimate the energy
consumption. The total amount of energy calculated should be compared to the actual
measured consumption in order to verify the consistency of the list and its exhaustiveness.
Keep in mind that the analysis of air conditioning performances using global energy bills will be
accurate only if the share of air conditioning energy consumption in the bill is significant. If
energy consumption for cooling is submerged by that from other uses, accurate estimation will
be impossible without sub-metering.
Parameters having effects on air conditioning energy consumption
Several parameters have effects on air conditioning energy consumption and more generally on
all thermal energy consumption. It is possible to distinguish four types:
5

6. - Building parameters are intrinsic to the construction of the building. The building shell
thermal characteristics, the glazing surface, heated/cooled areas and their location are
part of them. They were fixed by the first owner of the building and are difficult for
following building owners to change because of the costs of retrofits and relatively long
payback times.
- Policy parameters depend only on the current building owner’s decisions and his will to
save energy. Equipment choices and investments, operational parameters such as
temperature and humidity set points (if building centralized), the time of operation or
maintenance and follow-up policies are among them. The sensitivity of energy
consumption to these parameters is really important.
- Behavioral parameters depend on the occupants’ choices. Operational parameters such
as temperature and humidity set points (if room localized) or the natural uneconomic
behavior of occupants are part of them. Their influence on energy consumption can be
large although the duration of the “good practice” behavior can be short.
- Activity parameters depend mostly on the use of each space . These are largely
determined by the business needs of the organization. They have an important influence
on energy consumption but a building owner or energy manager cannot usually change
them.
Interpretation requires the identification of the most important parameters affecting each type of
energy consumption. Special attention must be paid to causes that might be improved by the
building owner.
These are then the preliminary actions to develop in order to begin the analysis of the energy
consumption of the building and are necessary to all the processes developed in the following
paragraphs.
PRELIMINARY ACTIONS
Determine the electricity uses included in the bill in general
Collect electricity bills (prefer monthly to annual basis to get more accuracy)
Determine parameters having effect on electricity consumption in general
Figure 1: Preliminary part of ACTION PLAN
6

7. 2. Basic benchmarking of energy consumption
With such numerous parameters, it is clear that the energy consumption of buildings can differ
strongly. The first part of this guide is dedicated to the simple follow-up of energy consumption of
one building from one time period to another. Most of owners only focus on energy bills at the
building level looking almost only at the amount of money they pay, but several factors should be
considered when comparing bills such as energy prices (that vary in general form one year to
another), climate effects, activity and changes in the building structure, operation etc. Thinking
about energy uses can bring however a lot of additional information.
After listing energy uses and influent parameters, it is important for a building owner to
determine the optimal measurement frequency (less than an hourly, hourly, daily, weekly,
monthly, yearly etc…). The objective is to maximize the information available while minimizing
the quantity of measurements and associated costs. Again, when certain parameters (climate for
example) vary a lot, it is relevant to reduce the time period to get more accuracy. Prefer
therefore monthly billing to yearly billing for thermal uses. If it is not possible, try to make your
own measurement using additional metrology (general meter, sub-meter, portable metrology).
As soon as consumption of at least two time periods for the same building are available, you can
begin the comparison. The objective of such a follow-up is not only to be aware of variations in
energy consumption but also to look at parameters responsible of such variations. Logically if
thermal uses (pure cooling system, joule effect heating or reversible heat-pump) are not
negligible in the bill in comparison to process uses, electricity consumption must be maximal in
summer (July or August) and in winter (December, January or February). Therefore, detect
possible problems on energy consumption magnitudes and variations with time by trying to
explain the bill through the main influent parameters you found previously. Once the responsible
parameters are underlined, think to possible actions that would allow optimizing energy
consumption1 or call a professional to assist you and perform a detailed energy audit.
Observe for example the variations of annual electricity consumption in some banking agencies
(Figure 2). Electricity uses (office equipments, cash dispensers, lighting, HVAC) are known and
unlikely to change unless in terms of quantity especially for cash dispensers. Opening hours are
constant from one year to another in a bank agency so that electrical appliances operate for the
same duration each year. On these examples, the area of the bank agencies and the staff are
unchanged from one year to another. The only parameters that can explain such variations
could be: activity variations of the agency (customers?), process changes (quantity, type?),
operational changes (temperature set-points, operating hours?) or climate variations (winter,
summer or both?). In that case, the agency manager is finally the best person able to determine
the origins of these variations, to know if they are normal or not and to decide to carry out
actions.
1
You can consult for a full list of Energy conservation opportunities the ECOs list on the AuditAC web site
and the AuditAC technical guide 5 - ECOs for AC auditors.
7

8. Figure 2: Examples of variations in annual electricity consumption in some banking agencies.
Then observe for example the variations of monthly gas and electricity consumption in a service
building in Portugal. Of course the use of gas is related only with the winter season and because
the building is air conditioned all along the summer season, monthly consumptions are related to
the weather.
Figure 3: Examples of variations in annual gas and electricity consumption in a library in
Portugal2.
2
AuditAC Case studies brochure: Case studies: Portugal, n°3.
8

9. 1st Part: Basic benchmarking of energy consumption
Collect activity indicators that might justify variations in electricity consumption
Note any changes other that climate (process, adjustments) from one time period to another
Collect electricity bills (prefer monthly to annual basis to get more accuracy)
Draw the diagram: time period on the X-axis and kWh/time period on the Y-axis
Try to explain variations in consumption thanks to previous changes or activity indicators
If variations are unexplainable, call a professional (energy supplier, ESCO etc.) for an energy audit
Try to continue that follow-up of energy consumption even if no problem is detected
Figure 4: First part of ACTION PLAN
3. Benchmark typical energy ratios
The lack of reference often leads us to think that everything is OK. The objectives of the second
part of that guide are to allow building owners to compare their energy consumption. Although it
is difficult, the idea of benchmarking is then to try to build “universal” indicators that can be
compared in the same activity sector whatever the building. To reach that independency from
the activity level, the best way is to calculate energy ratios (kWh/activity parameter/time period)
by dividing energy consumption of a system (building, energy use, equipment) during a certain
time period by one or more activity indicators.
The first task is to choose the best time period on which you will calculate energy ratios. Unless
you want to regularly follow-up your energy consumption because your activity parameters vary
strongly and frequently, it is useless to consider short time periods such as a week or a month.
Indeed, smaller is the reference more the climate dependence is relevant, so to choose monthly
reference allows to mitigate punctual peak or minimum of temperatures. Prefer typical time
periods such as a year or a season also in order to distinguish between heating and cooling
consumptions.
There exist an obvious relationship between the number of energy uses, their size and power
and the area of the building so that the most common indicator is the cooled area (in square-
meters). It can be used in every air-conditioned spaces especially buildings without specific
processes (office or residential buildings) where no other indicators are present.
Another obvious relationship exists between the area of the building and its occupancy
especially for constant occupancy buildings (no or limited numbers of external visitors) so that
the use of that indicator should then be equivalent to the use of square-meters. In practice, it is
not as clear (Figure 5) and the distinction between the two indicators allows sometimes to
maximize the information.
9

10. 30
25
Permanent occupancy
20
15
10
5
Permanent occupancy = 0,0211 x Square-meters + 2,1101
2
R = 0,7038
0
0 100 200 300 400 500 600 700 800 900 1000 1100 1200
2
Bank agency area (m )
Figure 5: relationship between area and permanent occupancy in the banking sector.
Everybody knows that the more the activity level of a building, the higher its energy
consumption. Thermal uses are not an exception and energy consumption is correlated to
occupancy. For example, heat and humidity productions and inlets due to temporary occupancy
have to be fought against by air conditioning systems. As it is often difficult to count visitors of a
building during a time period, it is better use other indicators that are necessarily such as:
occupied beds in a hospitals, occupied bedrooms in hotels, students in schools, meals in
restaurants, tickets sold in museums or concert-halls etc… In case of variable occupancy, these
indicators should be used in addition to kWh/m2 ratios.
Keep in mind that benchmarking only provides valid results (Figure 6) for the same building on
several time periods or at least for the same category of buildings. Two buildings from the same
activity sector may have totally different energy ratios because of their different sizes. Indeed,
most processes, their intrinsic efficiencies and their control systems are often better in large-
scale buildings. Moreover, these ratios do not take into account the climate influence (outdoor
temperature, sun, wind etc…) on energy consumption so that comparisons of buildings in too
different locations may be analyzed carefully.
600 50000
550
45000
500 2003 2004 2005 2003 2004 2005
Energy ratio (kWh/person.yr)
Energy ratio (kWh/m2.year)
40000
450
35000
400
350 30000
300 25000
250
20000
200
15000
150
100 10000
50 5000
0
0
0 100 200 300 400 500 600 700 800 900 1000 1100 1200
0 5 10 15 20 25 30
Bank angency area (m2) Agency staff
Figure 6: the kWh/m².year ratio decreases with the surface (left) and the staff (right) in the banking
sector.
10

11. If your ratios (compare several ones) are lower than other building ratios or statistics, your
building seems energy-efficient and you have probably no action to carry out or investment to
make. Continue however to regularly follow-up your indicators to anticipate problems. In the
other case, you should probably contact a professional to consider some ways of improvements.
2nd Part: Benchmark typical energy ratios
Choose main activity indicators that justify variations in electricity consumption
Calculate electricity consumption on the time period (the same as the one used in statistics)
For each activity indicator and the same time period, divide energy consumption by the
indicator
Compare each ratio from one time period to another
Try to explain possible variations (climate, adjustments etc…)
Compare each ratio to statistics
Try to explain possible differences (size, climate, adjustments etc…)
If variations/differences are unexplainable, call a professional (energy supplier, ESCO etc.)
for an energy audit
Try to continue that benchmarking of energy ratios even if no problem is detected
Figure 7 Second part of ACTION PLAN for benchmarking of energy ratios.
The EPlabel3 project has developed a simple software that records details of a building, its key
descriptors and its energy use and produces a summary of the measured annual energy
consumption and calculates Energy Performance indicators in units of total weighted energy per
m² of internal area. The software main idea is to label the building energy performance as
demanded in the European Energy Performance Building Directive, but it can be used to define
reference ratios (as built or operational rating) and visually describe the energy content of the
building.
4. Establish the climate based energy “signature” of the building
Previous energy ratios are climate dependent so that only statistical ranges are published.
Several climatic parameters (sun, wind, outdoor temperature, outdoor humidity) play an
important role in energy consumption of thermal uses. The objective of that paragraph is then to
determine the sensitivity of energy consumption to climate parameters. It is focused especially
on the outdoor temperature because that parameter is easier to feel and measure for a building
owner.
3
EPLabel: A programme to deliver energy certificates based on measured energy consumption for display in Public
buildings across Europe within a harmonizing framework. http://www.eplabel.org/
11

12. The particular interest of such a method is to distinguish between climate dependent energy
consumption (variable part) and them that are not (constant part). The building “signature” is
then a curve that describes in a unique way the behavior if the building as a function of a climatic
parameter. However, find a correlation between energy consumption and outdoor temperature
necessarily requires a representative dot scattering, in other words much more dots. Therefore,
monthly energy bills or weekly measurements should be preferred to annual energy bills. There
are three possibilities concerning the temperature: either measure it, or get free daily-averaged
measurements (can be found on the internet4) or pay for local hourly measurements (to national
weather institute).
Two climate indicators can be used in the energy signature. The first one is directly the averaged
outdoor temperature of the time period. As a consequence, the correlation function is growing for
cooling in summer and decreasing for heating in winter. The second indicator is degree-days
that can be calculated for heating (HDD) in winter and cooling (CDD) in summer. HDD
(respectively CDD) is the sum on the chosen time period (higher than a day) of the product of
“the number of days during the time period for which the outdoor temperature is lower
(respectively higher) than a reference value” by “the difference between the daily averaged
outdoor temperature and that reference value”. HDD (respectively CDD) represent how cold
(respectively hot) is a winter (respectively summer) time period.
The main difficulty of using degree-days is to choose the good reference temperature. In theory,
that value is the averaged temperature ensured only by the building shell without any additional
heating (or cooling) system. Logically, the balance temperature in winter is different from the one
in summer. For heating, the reference temperature is usually taken equal to 18°C in Europe. As
heat gains can contribute to about 3°C in buildings and due to the averaged decrease of building
shell thermal conductivity, the reference temperature for heating tends to rise. For cooling, the
same problem exist so that in large office buildings, air conditioning system may start for an
outdoor temperature of 14°C whereas in residential buildings, accounting less heat gains, the
reference temperature may reach 24°C. Observe that not knowing the "proper" base
temperature is, in practice, rarely a problem for detecting changes in consumption: energy
consumption correlates well with "wrong" degree-days - since degree-day values to different
base temperatures are strongly correlated with each other.
Once you got enough dots for your energy signature, you are able to detect rapidly drifts in
energy consumption due to some defaults compared to the past normal operation. It is thus
better to apply this method as soon as the start-up of a new thermal process in order to have
reference (without default) dots. The Figure 8 sums-up some of the typical defaults that can be
detected from an energy signature analysis.
4 For example, a French website centralizes several climate data in a lot of locations in the country
ahttp://www.meteociel.fr/climatologie/climato.php
12

13. kWh/time unit
kWh/time unit
Reference Tightness
CDD/time unit CDD/time unit
Problems on Default on
kWh/time unit
kWh/time unit
controls or the supply
thermal probes reduction
system
CDD/time unit CDD/time unit
Effects of Oversizing or
kWh/time unit
kWh/time unit
Sun
other overcooling
climate
Wind
CDD/time unit CDD/time unit
Figure 8: Few problems that can be detected by the climate-based energy signature of a building.
That method works almost perfectly for heating and is often used in operation and maintenance
contracts with guarantee of results. However, it is not as accurate for cooling because of the
higher number and power of electrical auxiliaries, the stronger influence of humidity and solar
radiation on air conditioning and the non-uniformity of temperature and humidity set points in
buildings due to a lack of regulation.
Once the correlation between energy consumption and degree-days or average temperature is
established, climate independent energy ratios are directly available calculating the slope of the
curve. It is then possible to compare your building ratios with those from any other building
whatever their locations. Remember that measuring only the power absorbed by the considered
thermal use (an air conditioning system for example) allows to strongly limit the offset magnitude
of the curve and to better underline the effect of climate (slope) on energy consumption. Energy
bills are a mix of consumption from different uses attenuating the visibility of pure thermal uses.
Thus, always take care to the framework to compare ratios with statistics or between buildings.
13

14. 3rd Part: climate based energy signature
Determine the best time period (monthly/weekly for DD, daily for average temperature)
Determine the best temperature reference (usually 18°C in winter, from 14°C to 24°C in
summer)
Measure/get average daily temperature or calculate/get degree-days for each time period
Separate cooling degree-days from heating degree-days
Measure /get from the bills electricity consumption on each time period
Draw the diagram for each time period: average temperature or DD on the X-axis and kWh
on the Y-axis
As soon as a season/year is complete, determine the (linear) regression between
consumption and climate parameters
Try to find the reasons why a new dot is outside the scatter – If differences are too
important, too frequent and unexplainable, call a professional (energy supplier, ESCO etc.)
for an energy audit
Calculate climate independent energy ratios (slope of the regression curve) in kWh/m2.DD
Compare to statistics – Try to explain possible differences (size, activity, adjustments etc…)
Try to continue the energy signature even if no problem is detected
Figure 9: Third part of ACTION PLAN for establishment and the use of the climate based energy
signature of the building.
5. Establish the multi-parameter energy signature of the building
We showed that energy consumption were due to a lot of parameters and underlined especially
the influences of building parameters (area, staff), activity parameters (occupancy, production)
and climate. However, it is impossible to analyze one parameter independently of the others.
Concerning air conditioning for example, a more temperate climate than forecasts can be
compensated by a much higher occupancy than in normal conditions so that the only climate or
activity analysis will provide biased results. As all of these parameters have a common influence
on energy consumption, it is important to analyze their effects simultaneously. The idea of that
paragraph is then to find the energy signature of the building by considering not only the climate
but also main activity parameters (pure occupancy, indirect occupancy, production etc…).
Building parameters (surface, staff etc…) can be added in order to determine the energy
signature of several similar buildings of the same activity sector (see next example).
This method is exactly the same as the previous one but additional parameters are this time
included into the linear regression. As for climate parameters, building and activity parameters
must be measured precisely for each time period so that specific metrologies or procedures
should be set-up. Again, time periods must be short in order to get more dots and then accuracy.
Finally, to be able to find a correlation between consumption and all the parameters, you will
14

15. need at least as many time periods as parameters. For example, if the main parameters having
effects on your building energy consumption E are the degree-days DD and the occupancy O in
a supermarket, the regression method requires at least two time periods on which the
coordinates (E, DD, O) are distinct. However, to reach higher levels of representativeness, it is
better to scan a large range for every parameter.
For example, in the banking sector the information available for each agency is: area S, staff
Nwkr, the annual electricity consumption for three years and finally the location allowing to
calculate both cooling (CDD) and heating (HDD) annual degree-days. As building parameters
(area and staff) are constant for one agency in the time, they only intervene into the magnitude
of consumption but not in their variations with time. On the opposite, variations of cooling and
heating degree-days lead to variations in annual electricity consumption for one agency. As only
three dots were available for each agency, the results of each energy signature were neither
accurate nor representative. Although the approach is strictly the same, the energy signature
was applied not to each agency but to the whole banking sector including every agencies in
order to determine a general profile of electricity consumption. Several models were tested:
influence of the area (S), of the winter climate (HDD), of the summer climate (CDD), of both
winter and summer climates (H&CDD) depending on the type of heating system (joule or
reversible heat-pump), of the staff (Nwkr). The Table 2 presents both results and accuracy (R2)
obtained by such models. Obviously, the more the parameters, the more accurate but for
comparison purposes they cannot be all kept.
Model (kWh/year) A B C R²
A.HDD.S+B 0.101 11680 0.67
A.HDD.S+B.Nwkr+C 0.047 3822 4652 0.77
A.CDD.S+B 0.411 27170 0.27
A.CDD.S+B.Nwkr+C 0.139 4000 1766 0.64
Heating by Joule effect (COP=1)
A.H&CDD.S+B 0.099 11481 0.67
A.H&CDD.S+B.Nwkr+C 0.0461 3813 4700 0.77
Heating by Heat Pump (COP=2.5)
A.H&CDD.S+B 0.099 11174 0.68
A.H&CDD.S+B.Nwkr+C 0.0479 3700 4740 0.77
Table 2: modeling of electricity consumption in the banking sector in France using a linear
regression method
Once the multi-parameter energy signature of the building is established, drifts in energy
consumption generated by defaults may be detected as soon as new dots deviate from the
scatter. As for the simple climate base energy signature, energy ratios relative to each
parameter may be deduced from the correlation function calculated by extracting multiplier
coefficient before each variable.
Eventually, the DD regression not being be enough precise or of hard interpretation, it exists
other methods such as the CUSUM method5 that allow to reveal trends that are occurring in the
building energy performance that are not visible through the only regression method.
5 For more detail about the CUSUM method you can look at the: “CTG004- Degree days for energy
management — a practical introduction”, Carbon trust at http://www.carbontrust.co.uk/publications
15

16. 4th Part: multi-parameter energy signature
Choose main activity and climate indicators that justify variations in electricity consumption
Determine the best time period (monthly, weekly, daily)
Measure/get every chosen indicators for each time period
Measure/get from bills the electricity consumption of each time period
If enough dots are available, establish the linear regression (E=Σai.Pi + b) between electricity
consumption E and every parameters Pi
Try to find the reasons why a new dot is outside the scatter – If differences are too important, too
frequent and unexplainable, call a professional (energy supplier, ESCO etc.) for an energy audit
Calculate energy ratios by extracting multiplier coefficients ai
Compare to statistics – Try to explain possible differences (size, activity, adjustments etc…)
Try to continue the energy signature even if no problem is detected
Figure 10: fourth part of ACTION PLAN for establishment and the use of the multi-parameter
energy signature of the building.
16

17. Annex
1) Some EECCAC (Energy Efficiency and Certification of Central Air Conditioners - FINAL
REPORT - APRIL 2003)
Ratios of total consumptions for EU 15 countries weighted all sectors and systems
(kWh/m².year)
Cooling Heating Cooling & Heating
Austria 26,1 147,1 173,2
Belgium 19,5 135,6 155,0
Denmark 14,1 170,4 184,5
Finland 15,0 192,1 207,1
France 32,6 114,6 147,3
Germany 22,8 152,6 175,3
Greece 48,3 97,2 145,4
Ireland 19,5 119,4 139,0
Italy 50,1 94,5 144,7
Luxembourg 19,1 135,9 155,1
Netherlands 17,7 136,0 153,7
Portugal 49,7 96,0 145,7
Spain 81,5 28,5 110,0
Sweden 14,9 192,1 207,0
UK 19,7 119,5 139,2
Electricity consumption for cooling (system and auxiliaries) in offices buildings for CAV (Constant
Air Volume system) and RAC (room air conditioner)
AC consumption (kWh/m2.year)
Seville-CAV 99,26
London-CAV 32,77
Milan-CAV 70,49
Seville-RAC 58,52
London-RAC 8,39
Milan-RAC 28,53
Some indicative ratios for air conditioning consumptions for UK different sectors
(All ratios are based on AC (kWh/m²) AC (kWh/m²) Ventilation
based on cooled area) Central systems Packaged systems (kWh/m²)
Hospitals (all health
94 170 71
sector)
Hotels/restaurant/bar 94 170 71
Offices (commercial only) 49 270 117
Commercial
144 123 52
establishments (retail)
Collective housing
Schools (all education) 94 170 71
Leisure 94 170 71
Government buildings 94 170 71
Warehouses 94 170 71
17

18. 2) Some indicative ratios for French offices from an internal study of the Ecole de Mines:
Use Additional remarks Ratios
All consumptions The ratio take into account the 240
climate correction
kWh/m².year
Heating and cooling The ratio take into account the 150 - 170
climate correction
kWh/m².year
Cooling The ratio take into account the 25 -50
climate correction
kWh/m².year
DHW kWh/m3 70 (electric)
100-150
Lighting kWh/m².year 40 - 65
Lift As % of total energy 2
consumption
18