Thermal Modelling
According to
the International Energy Agency, “50% of global final energy consumption in
2018” was used in heating, contributing 40% of global CO2 emissions (IEA,
2019). Just under half of the heat “was consumed in buildings for space and
water heating and, to a lesser extent, for cooking”. These figures help to
explain the interest in retrofitting existing buildings to improve their
thermal efficiency. The process can be difficult and expensive, and a variety
of approaches has been taken. In the work outlined below, the role of software
will be given particular attention.
Foda, El-Hamalawi and Le Dréau (2020) describe “a
computational analysis taking a French single family house as a case study”
using dynamic thermal modelling to find the optimum balance between annual
energy saving and the cost of standardised retrofit measures available on the
French market. The house had four occupants; was detached and typical of those
built before 1974, with solid walls, wooden loft and concrete/parquet ground
flooring, all uninsulated; single glazed wooden framed windows; and one air
change per hour (ACH).
The study did
not aim to optimise CO2 emissions (though their values are given), and onsite
generation was not included. The heating
systems considered were oil boiler, gas boiler, electric radiator, air-source
heat pump and ground-source heat pump (ASHP and GSHP). Data were used for each
of the main climatic regions of France, represented by Paris, Brest, Nice and
Lyon. The variables used for the building included its fabric, heating system,
ventilation and air-tightness. The study assumed that householders were offered
loans to pay for recommended retrofit measures, paid off through the resulting
savings in energy bills, and terms of 9 years and 15 years were considered. The
thermal modelling software used was ‘EnergyPlus 7.20’ (developed by the U.S.
Department of Energy), together with software designed to present it with the
many combinations of possible retrofit choices (EnergyPlus, 2021). Constraints were
placed on energy saving, payback and comfort (avoidance of summer overheating).
These constraints allowed many combinations of choices to be discarded, leaving
a set of candidate solutions which differed significantly in number for the
different climate zones. Among these solutions the “vast majority” included gas
boiler systems together with various fabric retrofit measures. Wall insulation
had a great impact on energy saving, but only a few of the ‘best’ solutions –
those lying on the ‘Pareto frontier’ of the set of candidate solutions –
included wall insulation. For most solutions, a 9 years loan period was shown
to be insufficient to provide payback, but a 15 year period allowed for
measures including wall insulation. Illustrative costs of retrofits are given,
and these varied widely between the four French regions as did the candidate
solutions themselves.
Duran and
Lomas (2021) concentrate on office buildings in the UK, and aim to provide
stake holders with “optimal, generic retrofit strategies”. They take into
account a wide range of factors many of which also apply to other types of
building. These variables include summer overheating, the predicted future
climate, location and orientation, “overall building thermal discomfort”, the adaptive
comfort standard, number of occupants, weather data, envelope parameters,
ventilation, passive and active cooling, external blinds, initial investment
costs, various cost benefits, and rent increase after retrofit.
The writers
created a model representing typical post-war UK office building stock, based
on a detailed literature review. The
buildings were simulated using the
EnergyPlus dynamic thermal model, with data input via
the DesignBuilder Graphical User
Interface (DesignBuilder, 2021). Additional software was used to implement the
analysis. A series of retrofit measures was applied to the base-case models
including envelope upgrades and passive and active cooling strategies, giving multiple
combinations of retrofit measures. Retrofit outcomes were compared with existing
UK building standards (Part L2B) and the more demanding Passivhaus and EnerPHit
standards. An Overall Building Thermal
Discomfort index is used which takes into account winter underheating and
summer overheating and the number of occupants; thermal comfort is related to
productivity improvement, which is included in cost calculations. Urban ‘heat
island’ effects were taken into account, and weather data was derived from the
Prometheus web portal of Exeter University (PROMETHEUS, undated). Current UK
building regulations do not require analysis of overheating, but the writers
suggest that this analysis should be included, as measures providing comfort in
present conditions can be shown to be inadequate for predicted 2050 weather,
demonstrating “the necessity of future-proofing retrofit designs.” The writers conclude that cost and energy can
be optimised within a retrofit which is compliant with UK building regulations if passive
summertime overheating controls are used, such as “automated window opening to
enable night-time ventilation and the use of shading”. However mixed-mode
ventilation would be needed if the 2050's are even warmer than anticipated.
Kersken et
al. (2020) note the increased range of technologies relevant to both new build
and retrofit planning, and the need to thoroughly test the modelling programs used
to predict “energy and internal environmental performance.” While accepting the
usefulness of comparisons between different simulation programs, they point out
the need “to develop some realistic empirical validation test cases of
full-scale buildings” in order to check that the programs represent reality.
Important work has already been done in this area, but has not adequately
reflected either the scale of real buildings or the interactions between their
various zones, perhaps because of the “complex, time consuming and costly”
nature of full-scale validation. More ambitious validation work has been helped
by the “widespread availability of sensor and instrumentation equipment, the
availability of sophisticated test buildings, knowledge regarding errors in
previous experimental programmes and improvements in simulation programs”.
Buildings at
the Fraunhofer Institute for Building Physics were used for the validation, and
were simulated using EnergyPlus V8.8. The writers list a number of elements of
their experiments which they consider to represent advances on previous work.
These are night setback of the heating’s set point temperature; set point
temperature profile based on a stochastic modelling of users; accounting for
heat and humidity produced by users; operation of internal doors and windows;
modelling of attic space and trap doors; two forms of underfloor heating; air
source heat pump; and two-gas tracer gas measurement for air flow analysis. Detailed
plans of the buildings, sensor data, baseline measurements, experimental
schedule and quality control methods are given. Most of the parameters used in
simulation carried a degree of uncertainty, and sensitivity analysis ranked thermal
bridges most significant in this respect. The results of the work provided documented
data that can be used for teaching and training or for software validation.
Beagon,
Boland and Saffari (2020) note the large gap between many predicted space
heating energy requirements of buildings and the actual measured energy use. The
writers are interested in the retrofitting of existing domestic buildings to
reduce energy consumption, and they list some of the shortcomings of earlier
work. These include lack of empirical data on the influence of occupants on heating
requirements; inaccurate values for model infiltration or ventilation rates and
building fabric properties; unrealistic use of constant values for both internal
and external temperatures; and assumptions that scaled down the ‘‘useful” solar
and internal heat gains throughout the entire heating season.
The writers
claim that their study “produces building energy models that close the
energy-use gap between simulation and typical measurements” and go on to
describe the refinements of method which allowed this improvement. For example,
time variations were calculated in steps of 15 minutes, using a one- year data
set from International Weather for Energy Calculation v2.0 specific to the
location. Modelling included the schedules of occupants and their effects, such
as opening windows, using lighting and appliances, cooking and producing
metabolic heat. Two levels of retrofit were considered, standard and advanced; gas
boilers replaced oil boilers in standard retrofit, heat pumps were fitted in advanced
retrofit, and the performance of gas boilers and heat pumps were modelled. The
resulting gaps between measured and predicted energy use all fell below 10%. The
EnergyPlus whole-building energy simulation software was used, together with DesignBuilder.
The authors note the ability of the software to model heating, ventilation, air
infiltration and air conditioning, outside and inside convection, and to calculate
heat loads, energy and life cycle costs, and environmental emissions.
A brief
conference paper by Gajewski and Pieniążek (2017) refers to the use of the
Energy3D computer program, which the writers describe as “an excellent tool for
qualitative and quantitative analysis of buildings.” They list parameters whose
effect on the energy use of a house can be modelled by this relatively simple
program. They include house size, house shape, roof insulation, roof colour,
solar heat gain coefficients of windows, orientation, thermostat setting,
location, environment albedo, and the proximity of trees.
Rashid et
al., (2017) describe a study on energy saving in an existing building using
eQUESTsoftware. The proposed modifications were restricted to the HVAC and
chilled water systems and to daylight control, but nevertheless predicted an energy
saving of around 17%, at a cost which could be recouped in just over 7 years.
The authors describe eQUEST (2019) as allowing development of three dimensional
simulation models incorporating “building location, orientation, wall/roof
construction, window properties, as well as HVAC systems, day-lighting and
various control strategies, along with the ability to evaluate design options
for any single or combination of energy conservation measures”. Roy, Ramani and
Shanmugapraya (2021) address a similar problem, also using eQUEST, but provide
a deeper and more detailed discussion of standards, methods and results. Lachance et al. (2021) use eQUEST in their
study of the validation of a new performance rating procedure for cold climate
air-to-air heat pumps. Khasikov (2020)
compares the energy use of a house with typical wood frame with that of a
similar building having double-wall wood framing. He also uses eQUEST, and
provides a useful stage by stage description of the process.
References
Beagon, P., Boland, F., Saffari, M., 2020, Closing the gap between simulation and
measured energy use in home archetypes, Energy
and Buildings, Volume 224, 1 October 2020, 110244
https://doi.org/10.1016/j.enbuild.2020.110244
DesignBuilder, 2021, Website, accessed 19 June 2021
http://www.designbuilder.co.uk/
Duran, Ö., and Lomas, K., 2021, Retrofitting Post-War Office Buildings:
Interventions for Energy Efficiency, Improved Comfort, Productivity and Cost
Reduction, Journal of Building
Engineering, online, accessed 18 June 2021
https://www.sciencedirect.com/science/article/pii/S2352710221006045
EnergyPlus, 2021, website, accessed 15 June 2021
Energy3D, 2021, The Concord Consortium, online, accessed 23
June 2021
https://energy.concord.org/energy3d/
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https://www.buildup.eu/en/learn/tools/equest-simulation-software
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A., Le Dréau, J., 2020, Computational
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house, Building and Environment,
online, accessed 15 June 2021
https://bura.brunel.ac.uk/bitstream/2438/21185/1/FullText.pdf
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accessed 15 June 2021
https://www.iea.org/reports/renewables-2019/heat
Kersken, M., et al., 2020, Whole building validation for
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design, E3SWeb of Conferences 172, 22003 (2020), online, accessed 22 June 2021
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Khasikov, S., 2020, Calculation of the Payback Period for
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https://iopscience.iop.org/article/10.1088/1757-899X/753/4/042032/pdf
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29 June 2021
https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/22/e3sconf_hvac2021_06004.pdf
PROMETHEUS, undated, Centre for Energy and the Environment, University
of Exeter, online, accessed 22 June 2021
https://emps.exeter.ac.uk/engineering/research/cee/research/prometheus/
Rashid, F., et al., 2017, Performance Analysis and
Investigation for the development of Energy Efficient Building, Proceedings of
the International Conference on Mechanical Engineering and Renewable Energy
2017, online, accessed 22 June 2021
https://www.cuet.ac.bd/icmere/files2017f/ICMERE2017-PI-311.pdf
Roy, A., Ramani, P.,
and Shanmugapraya , T., 2021,
Simulation and Analysis of a Factory Building’s Energy Consumption Using
eQuest Software, Chem. Eng. Technol.
2021, 44, No. 5, 1–7, online, accessed 29 June 2021
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