The work on integration of energy modelling in the building engineering process focuses on one or more of the challenges mentioned in the previous section.
These efforts augment continuous work on the improvement of building performance simulation tools, in general, including work on better input data (climate files, user behaviour and system control), algorithms, data post-processing, data mining and result visualization techniques.
While the major tools are getting bigger, user friendlier and feature richer, a grassroots community is developing extensible Matlab libraries/toolboxes (Riederer, 2005). Due to the large proliferation of Matlab in the engineering curricula, this opens the door to the introduction of research-oriented energy modelling in graduate class exercises and PhD research projects. Whether this will grow out into a Matlab-based open source ‘energy modelling research community’ remains to be seen.
On the input side of the major energy tools, one category of research and development efforts attempts to integrate simulation with early or conceptual design by linking emerging computer-assisted design (CAD) sketching tools with energy simulation engines (see, for
instance, Rizos, 2007). However, as is demonstrated by the example of the link between Sketchup and EnergyPlus, such linking depends upon inter-operability, as reported by Bazjanac (2005). A more fundamental approach, which looks at scalable and reusable spatial models, has been described by Suter and Mahdavi (2004). It is interesting to note that in the adjacent discipline of lighting modelling, simulation has already been fully integrated with CAD systems for a while: Desktop Radiance operates from within AutoCad 14, using pull-down menus (Mistrick, 2000).
No similar tool has been demonstrated for energy simulation thus far.
On data post-processing and visualization, recent work at the University of Strathclyde by Morbitzer (2003) and Prazeres (2006) has focused on the presentation of simulation results by means of an integrated performance view. This tailors simulation output towards specific aims such as design exploration, analysis, representation and reporting, while providing flexibility to match individual preferences. It employs data mining and clustering techniques to filter through a range of simulation results. A related area under development is the use of uncertainty and sensitivity analysis to guide design, as previously discussed and demonstrated in Figure 4.5.
Finally, it is worth mentioning the work on the coupling of energy modelling with the realm of intelligent computing. Here, advanced search and visualization techniques can be applied to the field of energy simulation, allowing one to push the boundaries of what is currently undertaken in the design office. As an example, Figure 4.6 shows two clusters, A and B, of energy efficient solutions obtained from a nine-dimensional search space using a genetic algorithm (de Wilde et al, 2008).
The Design Analysis Integration (DAI) initiative (Augenbroe et al, 2004) was aimed at addressing problems perceived in the ongoing efforts towards tool interoperability. The project built on the recognition that current solutions suffer from two major shortcomings:
1 They assume an idealistic structured data context, which allows perfect mapping between design information and analysis needs.
2 They are data driven, neglecting the process dimension of design – energy modelling interaction – where there is a clear ‘analysis request’
and where modelling results must be useful to the professionals who are involved in the building engineering process.
In order to overcome these issues, the DAI suggested a modular approach that starts from the premise that a set of recurring design analysis requests can be identified. These requests would represent the main questions that repeatedly are asked from modelling experts, say 80 per cent, leaving room for another 20 per cent of highly specialized requests that cannot be automated and need first-principle modelling of a specific problem from scratch. The recurring 80 per cent of requests would then allow the modelling of structured, if needed, scalable ‘analysis functions’, which uniquely define the quantification of specific building performance aspects in terms of performance indicators (PIs). A prototype software environment was developed and demonstrated to experts in the field;
follow-up initiatives are currently under development.
Conclusions
The role of energy modelling in building engineering has co-evolved with the technology. Increasingly stringent regulations on energy efficiency, carbon
Figure 4.6 Visualization of results of searching in a multidimensional space
emissions and more energy-conscious clients have yielded a buoyant consultancy sector that is a major factor in today’s collaborative design of buildings. Yet, while energy modelling now has become an important ingredient of the engineering of sustainable buildings, a set of important challenges remain. Full integration of energy modelling and building design requires further process integration, which is a non-trivial issue due to the highly unstructured nature of the current building design process. Further investigation of how designers make decisions, and how modelling results can help to make those choices, is needed; but such research needs to take into account the fact that design practice itself is subject to change. Process integration also requires better collaboration between the actors, including improvements in data exchange, communication and the pursuit of common objectives. Furthermore, the trust in modelling outcomes, the role and impact of uncertainties, and the training of simulation experts are fields that need addressing to move the discipline forwards.
References
ASHRAE (American Society of Heating, Refrigerating and Air-Conditioning Engineers) (2004) Handbook of HVAC Systems and Equipment, ASHRAE, Atlanta, GA
Augenbroe, G., de Wilde, P., Moon, H. J. and Malkawi, A.
(2004) ‘An interoperability workbench for design analysis integration’, Energy and Buildings, vol 36, no 8, pp737–748
Augenbroe, G., McManus, S., Zhao, F., Li, Z., Heo, Y. and Kim, S. H. (2008) ‘Lessons from an advanced building simulation course’, in Proceedings of SimBuild, Berkeley, CA
Bazjanac, V. (2005) ‘Model based cost and energy performance estimation during schematic design’, in Proceedings of CIB W-79: Information Technology in Construction, Dresden, Germany
Bazjanac, V. (2007) ‘Impact of the U.S. national building information model standard (NBIMS) on building energy performance simulation’, in Proceedings of Building Simulation 2007, Beijing, China, pp1377–1382 Bentley, R.W. (2002) ‘Global oil and gas depletion: An
overview’, Energy Policy, vol 30, no 3, pp189–205 Bordass, B., Cohen, R., Standeven, M. and Leaman, A.
(2001) ‘Assessing building performance in use 3: Energy performance of the Probe buildings’, Building Research and Information, vol 29, no 2, pp114–128
Building Services Journal (2007) ‘Home Office goes green at new Sheffield HQ’, Building Services Journal, November, pp41–44
Clarke, J. A. (2001) Energy Simulation in Building Design, Butterworth-Heinemann, Oxford
Clarke, J. A., Tang, D., James, K. and MacRandal, D. F.
(1992) Energy Kernel System, Final Report for GR/F/07880, Engineering and Physical Science Research Council, Swindon, UK
de Wilde. P. (2004) Computational Support for the Selection of Energy Saving Building Components, PhD thesis, Delft University of Technology, The Netherlands
de Wilde, P., Beck, M. and Rafiq, Y. (2008) ‘Visualisation of the impact of climate change on the cooling requirements of domestic buildings in the United Kingdom’, in Proceedings of Intelligent Computing in Engineering 2008, Plymouth, UK
Feist, W., Peper S. and Görg, M. (2001) CEPHEUS Final Technical Report, PassivHaus Institut, Darmstadt, Germany
Fritzson, P. (2004) ‘Principles of object oriented modeling and simulation with Modelica 2.1.’, IEEE
Incropera, F. P., DeWitt, D. P, Bergman, T. L and Lavine, A. S.
(2007) Fundamentals of Heat and Mass Transfer, John Wiley & Sons, Hoboken, NJ
Kalay, Y. (2006) ‘The impact of information technology on design methods, products and practices’, Design Studies, vol 27, no 3, pp357–380
Karjalainen, S. and Koistinen, O. (2007) ‘User problems with individual temperature control in offices’, Building and Environment, vol 42, no 8, pp2880–2887
Kennett, S. (2007) ‘The UK’s first carbon-neutral house’, Building Services Journal, July, pp56–57
Mahdavi, A., Matthew, P., Kumar, S. and Wong, N. H.
(1997) ‘Bi-directional computational design support in the SEMPER environment’, Automation in Construction, vol 6, no 4, pp353–373
Malkawi, A. and Augenbroe G. (eds) (2004) Advanced Building Simulation, Spon Press, New York, NY McElroy, L., Cockroft, J. and Hand, J. (2007) ‘Business
success through process based application of simulation’, in Proceedings of Building Simulation 2007, Beijing, China, pp1740–1746
Mistrick, R. (2000) Desktop Radiance Overview, Pennsylvania State University, PA
Morbitzer, C. A. (2003) Towards the Integration of Simulation into the Building Design Process, PhD thesis, University of Strathclyde, Glasgow, UK
Oxman, R. (2006) ‘Theory and design in the first digital age’, Design Studies, vol 27, no 3, pp229–265
Prazeres, L. (2006) An Exploratory Study about the Benefits of Targeted Data Perceptualisation Techniques and Rules in
Building Simulation, PhD thesis, University of Strathclyde, Glasgow, UK
Riederer, P. (2005) ‘Matlab/Simulink for building and HVAC simulation – State of the art’, in Proceedings of Building Simulation 2005, Montreal, Canada, pp1019–1026
Rizos, I. (2007) Next Generation of Energy Simulation Tools:
Coupling 3D Sketching with Energy Simulation Tools, MSc thesis, University of Strathclyde, Glasgow, UK
Sahlin, O., Eriksson, L., Grozman, P., Johnsson, H., Shapovalov, A. and Vuolle, M. (2003) ‘Will equation based building simulation make it? Experiences from the introduction of IDA indoor climate and energy’, in Proceedings of Building Simulation 2003, Eindhoven, The Netherlands, pp1147–1154
Schwede, D. (2007) ‘Towards a digital representation of physical phenomena to assess comfort in future environments’, in Proceedings of Building Simulation 2007, Beijing, China, pp1403–1409
Sowell, E. F. and Haves, P (2001) ‘Efficient solution strategies for building energy system simulation’, Energy and Buildings, vol 33, no 4, pp309–317
Stein, B. and Reynolds, J. S. (2006) Mechanical and Electrical Equipment of Buildings, John Wiley & Sons, Hoboken, NJ
Stern, N. (2006) The Economics of Climate Change – The Stern Review, Cambridge University Press, Cambridge, UK Suter, G. and Mahdavi, A. (2004) ‘Elements of a
representation framework for performance-based design’, Building and Environment, vol 39, no 8, pp969–988 US Department of Energy (2008) Building Energy Software
Tool Directory, www.eren.doe.gov/buildings/tools_
directory/, accessed 31 March 2008
WCED (World Commission on Environment and Development) (1987) Our Common Future, The Brundtland report, Oxford University Press, Oxford, UK
Wetter, M. and Haugstetter, C. (2006) ‘Modelica versus TRNSYS: A comparison between an equation-based and a procedural modeling language for building energy simulation’, in Proceedings of SimBuild 2006, Cambridge, MA
World Green Building Council (2008) CH2: Council House 2 Factsheet, http://worldgbc.org, accessed 6 February 2008
Introduction
Worldwide, almost 40 per cent of the energy used is for heating, cooling, lighting and ventilating buildings and in the UK it is almost half of the energy used. This emphasizes the fact that buildings are important in the drive to reduce both national and global consumption of resources.
The general way in which the energy consumption of buildings can be reduced is to pay attention to the following design issues.