WS 09 - Total energy use in residential buildings and influential factors Measurement and analytical methods - Date: 10 May 2010
Chair: Hiroshi Yoshino, Tohoku University, Japan, yoshino@sabine.pln.archi.tohoku.ac.jp
Co-Chairs: Yi Jiang, Tsinghua University, China, jiangyi@tsinghua.edu.cn
Co-sponsor: IEA-ECBCS Annex 53
Presentations at the workshop
1.
Field Surveys and Statistical Analyses on Energy Consumptions in Japanese Residential
Buildings, Hiroshi Yoshino, Tohoku University (Japan),
yoshino@sabine.pln.archi.tohoku.ac.jp
2.
Energy consumption of Italian existing residential buildings: data collection and analysis,
Stefano Paolo Corgnati, Politecnico di Torino (Italy), stefano.corgnati@polito.it
3.
Research on the Integrated Evaluation Model of Energy Information System for
Residential Buildings in China, Hiroshi Yoshino, Tohoku University (Japan)
4.
For a better understanding of actual consumptions in residential buildings, Gabrielle
Masy Haute Ecole Renequin Sualem, (Belgium), Gabrielle.Masy@provincedeliege.be
5.
Influence of Occupants’ Behaviour on the Energy Consumption of Domestic Buildings,
Henrik Brohus, Aalborg University (Denmark), hb@civil.aau.dk
INTRODUCTION
One of the most significant barriers to improving energy efficiency in buildings is a lack of
knowledge about the determinant factors for energy use. Building energy consumption is
mainly influenced by six factors: (1) climate, (2) building envelop, (3) building services and
energy systems, (4) building operation and maintenance, (5) occupants’ activities and
behavior and (6) indoor environmental quality provided. Current research focuses in the
main on the first three factors. However, factors four, five and six, reflect human behaviors –
how people operate equipment, how many children they have etc, and these factors can
also strongly influence building energy use. Beside that there is also lack of scientific method
to account for the interactions between these six factors and energy use in a clear and
thorough way, so that energy use in a building can be assessed accordingly.
This workshop provides a spacious platform for the intensive discussion on the above
problems and the potential methods to solve the problems, so as to enrich understanding of
the effective energy data for the performance of buildings and building systems, to broaden
knowledge about determinant factors for total energy use in buildings, as well as the effect
of energy saving technologies and occupant’s behavior & lifestyle on building energy use.
This workshop focuses residential buildings.
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SUMMARY OF PRESENTATIONS
1. Field Surveys and Statistical Analyses on Energy Consumptions in Japanese Residential
Buildings
Yoshino presented field surveys on energy consumptions, which have been carried out in 80
households located in 6 different districts with different climate conditions in Japan during
two years from December 2002 to November 2004. He showed the statistical analyses to
understand how the factors influence energy consumptions in Japanese residential buildings
based on the information gained from field surveys. The results of statistical analyses were
presented and statistical methods to compare energy consumption in different buildings
were described.
Figure 1 shows one of results by the statistical analysis using the neural network theorem,
which means the heating degree days, the heat loss coefficient and the number of family
members are major influencing factors on energy use.
There was a question on why the results by the different statistical analysis model are
Neural Network
CDD
5%
HDD
27%
Building
age
16%
Floor area
4%
Heat loss
coefficience
19%
Airtightness
Family
performance
Figure 1: Importance Members
of input layers to Annual Energy Consumption
7%
22%
2. Energy consumption of Italian existing residential buildings: data collection and analysis
different each other. I
Corgnati gave an overview of the results obtained in a number of research projects carried
out with different public bodies to verify the actual energy consumptions of residential
building stock in Northern Italy. He pointed out the problems and critical aspects found in
the measurement, collection and elaboration of the energy data, highlighting the need of a
clear definition of the database structure and aim. Moreover, He described the
normalization methods allowing to compare energy consumption in different years of
different buildings. Figure 2 shows the one example of comparison of measured energy use
and predicted values, which was calculated by geometric data, thermo-physical data, plant
typologies, etc.
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Figure 2: Case study – Predicted vs Actual Consumptions
3.TheResearch
on the Integrated Evaluation Model of Energy Information System for
correlations, calculated from the sample of buildings (50 selected buildings), can be applied to
Residential
Buildings
in China of buildings (about 1000 buildings), in order to analyze and predict
investigate the
whole population
the heating energy requirement of the large real estates.
Yoshino gave a presentation on behalf of Dr. Shuqin Chen, Lawrence Berkeley National
Laboratory, U.S.A. He introduced the evaluation model of the residential energy system
created by Quantification Theory III, based on the investigations for summer residential
energy use in seven typical cities in China. The results showed that seven cities could be
ranked from the point of view of the residential energy information systems. Fig. 3 indicates
the distribution of the household calculated by the theory. There was question what the
meaning of x-axis and y-axis for the distributed chart.
4. For a better understanding of actual consumptions in residential buildings
Macy introduced a global simulation model of the building and its HVAC system taking also
into account the less technical factors. A fully transparent reference model was proposed to
quantify the actual influences of these factors and to support the analysis of actual
consumption records. This model could be run in such a way to identify the most
appropriate correlations and to reject the non significant ones. Figure 4 shows one example
of calculation for the effect of window opening on energy use. The result indicated that the
fresh air control gave 28% decrease of energy use compared to the reference case.
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Figure 3: Case study of integrated evaluation of energy information systems of residential buildings in
seven cities of China
Primary Energy consumption kWh/m2.yr
Reference
Window opening - heating on
Window opening - heating off
Ventil D+ recovery + fresh air flow control
0
50
100
150
200
250
300
Figure 4: Example of calculation for the effect of window opening on energy use
Window opening 1hour /day:
+ 5 - 7%
Fresh air flow control:
-28 %
5. Influence of Occupants’ Behaviour on the Energy Consumption of Domestic Buildings
Brohus introduced a theoretical and empirical study of the uncertainty of energy
consumption assessment of domestic buildings related to occupants’ behaviour. The
calculated energy consumption of a number of almost identical domestic buildings in
Denmark was compared with the measured energy consumption. Furthermore, the
uncertainty is determined by means of stochastic modelling based on input distributions
found by literature study, industry guidelines, measurements and – when necessary – simple
assumptions.
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Results from Quantitative SA, 2
6%
15%
Set-point space heating
37%
Occupant heat load
Natural ventilation and infiltration
20%
Appliances heat load
Occupied period
Figure 5: A theoretical and empirical study of the uncertainty of energy consumption assessment of
domestic22%
buildings related to occupants’ behaviour
A high number of parameters are investigated and ranked in terms of variance and
importance to determine which ones contribute the most to the overall level of uncertainty.
As show in Fig.5, it is found that the temperature set point, time for use, natural ventilation,
infiltration and the internal heat load are major contributors to the uncertainty. The major
part of the variance can be assigned to the occupants’ behaviour.
DISCUSSIONS AND WS MAIN RESULTS
1. Occupant’s behaviour on energy use
The influencing factors related to occupant behaviour are considered a basic topic for all the
audience, but the audience highlighted the difficulties related to the development of such
item. Moreover, the audience put in evidence the difficulties connected to the monitoring of
occupant behaviour.
2. Data base structure
The topic of clarifying actual energy consumptions, divided by each single specific end use, is
crucial to understand the real energy demand of buildings. The idea of a three level database
(based on annual, monthly and daily/hourly end uses values), as presented, had a positive
answer from the audience. But the audience asked for a clear definition of which was the
minimum information for each building required to enter in each level.
Also the audience asked to share the database structure when defined by the Annex 53
group. It was stressed to have to clarify what kind of data may be obtained from
measurements and/or from questionnaires/survey, especially with reference to the
influencing factors related to occupant behaviour.
3. Statistical methods and building simulation for prediction
The enlargement of the knowledge of statistical methods for prediction of energy end uses is
important as well as their application to identify the relevant influencing factors; statistical
analyses allows to reduce the number of parameters able to describe the systems, and this
fact is very important when large building samples are studies.
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A detailed explanation of the different suitable data-driven prediction methods is required
from most of the audience, because they are still not well known there is neither the
possible application nor the potentialities. Such clarification is asked to Annex 53.
Dedicated modules for occupant behavior, to be used in building simulation programs, are
required. Audience agreed that the exact prediction of actual energy consumption was a
mirage and the robust prediction methods (direct and indirect) are foreseen.
3. Monitoring system for occupant’s behaviour
Audience highlights the need of providing solution for low cost equipments aimed at long
terms monitoring of indoor environment quality and the energy end uses, especially for
existing buildings. Also, audience highlights the need of providing procedures occupant
behavior.
CONCLUSIONS AND FUTURE WORK DIRECTIONS IN THE FIELD
The discussion gave new and important insight that we can bring into the future work of the
Annex. Furthermore, we were informed about other ongoing works in the area that may
provide additional and new opportunities for external collaboration.
It is concluded that in order to advance the Annex 53 works there are subjects shown below;
1) Data base structure to be obtained by measurement
2) Reliable prediction methods for effect of occupant’s behaviour on energy use
3) Development of monitoring system for occupant’s behaviour
ACKNOWLEDGEMENT
Author would like to acknowledge Stefano Corgnati and Henrik Brohus to give information of
discussion at the workshop.
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