Automated optimisation; net zero; buildings; design; decision support
Abstract :
[en] Automated mathematical building performance optimisation (BPO) paired with building performance simulation (BPS) is a promising solution to use as a means to evaluating many different design options and obtain the optimal or near optimal for a given objective or combination of objectives (e.g., lowest life-cycle cost, lowest capital cost, highest thermal comfort) while achieving fixed objectives (e.g., net zero-energy) (Wetter 2001; Charron and Athienitis 2006; Christensen and Anderson 2006; Brown, Glicksman et al. 2010; Bucking, Athienitis et al. 2010 ). Traditionally, buildings are designed based on heuristic rules separating the design process into early and late design stages with two different discipline specialisations mainly architecture and engineering. By employing optimisation techniques building designers can cross those barriers between disciplines and address the design process as one continuous stage. Optimisation can allow that by addressing all building design parameters, as shown in Figure 4.1, in a holistic approach allowing the optimisation of geometry, envelope, comfort, systems and renewable. The previously often ill defined design problem would under this perspective be defined as a problem with explicit multi-objective criteria. This will push fully integrated net zero-energy building (Net-ZEB) designs where the builder designers can act to influence the direction of the optimisation. Despite optimisation’s potential in Net-ZEB buildings, it remains largely a research tool and has yet to emerge in common industry practice. As this section reports, major obstacles to BPO in industry include lack of appropriate tools, lack of resources (time, expertise), and the requirement that the problem be very well defined (e.g., constraints, objective function, finite list of design options). The objective of this section is to document the current state-of-the-art and future research in terms of Net-ZEB optimisation tools in practice and its use for design and operation of buildings for energy, comfort and cost optimisation.
Research Center/Unit :
Sustainable Buildings Design Lab
Disciplines :
Architecture
Author, co-author :
Attia, Shady ; Université de Liège > Département Argenco : Secteur A&U > Techniques de construction des bâtiments
Hamdy, Mohamed
Carlucci, Salvatore
Pagliano, Lorenzo
Bucking, Scott
Hasan, Ala
Language :
English
Title :
Modelling, Design, and Optimisation of Net-Zero Energy Buildings
Alternative titles :
[en] Building Performance Optimisation of Net Zero-Energy Buildings
Publication date :
01 February 2015
Main work title :
Building Performance Optimisation of Net Zero-Energy Buildings
American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) (2010) Standard 55 - Thermal environmental conditions for human occupancy (Atlanta, GA).
Athienitis, A.K., Torcellini, P., Hirsch, A., O'Brien, W., Cellura, M., Klein, R., Delisle, V., Attia, S., Bourdoukan, P., and Carlucci, S. (2010) Design, Optimization and Modelling Issues of Net-Zero Energy Solar Buildings. In EuroSun (Graz, Austria).
Attia, S. (2012) Computational Optimisation for Zero Energy Building Design: Interviews with Twenty Eight International Experts, Architecture et Climat, Louvain-la-Neuve: Université, catholique de Louvain, Louvain-la-Neuve, Belgium.
Attia, S., Hamdy, M., O'Brien, W., and Carlucci, S. (2013) Assessing gaps and needs for integrating building performance optimization tools in net zero energy buildings design. Energy and Buildings, 60, 110-124.
Audet, C. and Dennis, J.E. (2002) Analysis of generalised pattern searches. SIAM Journal of Optimization, 13, 889-903.
Brown, C., Glicksman, L., and Lehar, M. (2010) Toward zero energy buildings: optimized for energy use and cost. Paper presented at: SIMBuild fourth National Conference of IBPSA-USA (New York City).
Bucking, S. (2013) Pathways to net-zero energy buildings: an optimization methodology. PhD thesis, Concordia University. Available at http://spectrum.library.concordia.ca/978083/1/Bucking_PhD_S2014.pdf.
Bucking, S., Athienitis, A., Zmeureanu, R., O'Brien, W., and Doiron, M. (2010) Design Optimisation Methodology for a Near Net Zero Energy Demonstration Home Paper presented at: EuroSun (Graz, Austria).
Bucking, S., Zmeureanu, R., and Athienitis, A. (2013) An information driven hybrid evolutionary algorithm for optimal design of a Net Zero Energy House. Solar Energy, 96, 128-139.
Caldas, L. (2001) An Evaluation Based Generative Design System: Using Adaptation to Shape Architectural Form, MIT Press.
Caldas, L. (2008) Generation of energy-efficient architecture solutions applying GENE ARCH: An evolution-based generative design system. Advanced Engineering Informatics, 22, 59-70.
Carlucci, S. (2013) Thermal Comfort Assessment of Buildings, vol. 1, Springer, London.
Carlucci, S., Zangheri, P., and Pagliano, L. (2013) Achieving the Net zero-energy target in Northern Italy: lessons learned from an existing Passivhaus with earth-to-air heat exchanger. Advanced Materials Research, 689, 184-187.
CEN (2007) EN 15251: Indoor Environmental Input Parameters for Design and Assessment of Energy Performance of Buildings Addressing Indoor Air Quality, Thermal Environment, Lighting and Acoustics, (Brussels, Belgium: European Committee for Standardization).
Charron, R. and Athienitis, A.K. (2006) The Use of Genetic Algorithms for a Net-Zero Energy Solar Home Design Optimisation Tool. Paper presented at: PLEA (Geneva, Switzerland.).
Choudhary, R. (2004) A Hierarchical Design Optimisation Framework for Simulation based Architectural Design, University of Michigan, USA.
Christensen, C. (2005) BEopt: Software for identifying optimal building designs on the path to zero net energy. Paper presented at: ISES Solar World Congress, (Orlando, FL, USA.).
Christensen, C. and Anderson, R. (2006) BEopt software for building energy optimisation: features and capabilities (National Renewable Energy Laboratory).
Constantinescu, T. (2010) Cost Optimality Discussing Methodology and Challenges with the Recast Energy Performance of building Directive, The Buildings Performance Institute Europe (BPIE), Brussels.
Crawley, D.B., Lawrie, L.K., Winkelmann, F.C., Buhl, W.F., Huang, Y.J., Pedersen, C.O., Strand, R.K., Liesen, R.J., Fisher, D.E., Witte, M.J. et al. (2001) EnergyPlus: Creating a new-generation building energy simulation program. Energy and Buildings, 33, 319-331.
de Dear, R.J. and Brager, G.S. (1998) Developing an adaptive model of thermal comfort and preference. Paper presented at: 1998 ASHRAE Winter Meeting (San Francisco, CA, USA: ASHRAE).
Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T. (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6, 182-197.
Department of Energy (DOE) (2013) EnergyPlus Input/Output Reference.
Eberhart, R. and Kennedy, J. (1995) Particle swarm optimisation. Paper presented at: IEEE International Conference on Neural Networks (Perth, Australia).
Eiben, A.E. and Rudolph, G. (1999) Theory of evolutionary algorithms: a bird's eye view. Theoretical Computer Science, 229, 3-9.
Eiben, A.E. and Smith, J.E. (2003) Introduction to Evolutionary Computing, Springer, London, UK.
Elbeltagi, E., Hegazy, T., and Grierson, D. (2005) Comparison among five evolutionarybased optimization algorithms. Advanced Engineering Informatics, 19, 43-53.
European Parliament and Council (2010) Energy performance of buildings, Vol L. 153/13, (recast) edn (Luxembourg: Official Journal of the European Union).
Fanger, P.O. (1970) Thermal Comfort: Analysis and Applications in Environmental Engineering, Danish Technical Press, Copenhagen.
Feoktistov, V. (2006) Differential Evolution: In Search of Solutions, Springer, New York, USA.
Flager, F., Welle, B., Bansal, P., Soremekun, G., and Haymaker, J. (2009) Multidisciplinary process integration and design optimization of a classroom building. Journal of Information Technology in Construction, 1, 595-612.
Goldberg, D. (1989) Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, New York.
Hamdy, M. (2012) Combining Simulation and Optimisation for Dimensioning Optimal Building Envelopes and HVAC Systems, Aalto University, Finland.
Hamdy, M., Hasan, A., and Siren, K. (2009) Combination of optimization algorithms for a multi-objective building design problem. Paper presented at: IBPSA: 11th International Building Performance Simulation Association Conference (Glasgow, Scotland).
Hamdy, M., Hasan, A., and Siren, K. (2013) A multi-stage optimization method for costoptimal nearly-zero-energy building solutions in line with the EPBD-recast 2010. Energy and Buildings, 56, 189-203.
Hasan, A., Vuolle, M., and Siren, K. (2008) Minimisation of life cycle cost of a detached house using combined simulation and optimisation. Building and Environment, 43, 2022-2034.
Hayter, S.J., Torcellini, P.A., Hayter, R.B., and Judkoff, R. (2001) The energy design process for designing and constructing high-performance buildings. In CLIMA 2000 (Naples, Italy).
Hooke, R. and Jeeves, T.A. (1961) "Direct search" solution of numerical and statistical problems. Journal of the Association for Computing Machinery, 8, 212-229.
Hopfe, C. (2009) Uncertainty and sensitivity analysis in building performance simulation for decision support and design optimisation (Eindhoven University).
Kampf, J. and Robinson, D. (2010) Optimisation of building form for solar energy utilisation using constrained evolutionary algorithms. Energy and Buildings, 42, 807-814.
Kampf, J., Wetter, M., and Robinson, D. (2010) A comparison of global optimization algorithms with standard benchmark functions and real-world applications using EnergyPlus. Journal of Building Performance Simulation, 3, 103-120.
Kennedy, J. and Eberhart, R. (1997) A discrete binary version of the particle swarm algorithm. Paper presented at: IEEE, International Conference on Neural Networks (Indianapolis, USA).
Köppen, W.P. and Geiger, R. (1930) Handbuch der Klimatologie, Gebrüder Borntraeger, Berlin.
LBNL and Wetter, M. (2011) GenOpt, Generic Optimisation Program, Lawrence Berkeley National Laboratory, University of, California, California.
Marszal, A.J., Heiselberg, P., Bourrelle, J.S., Musall, E., Voss, K., Sartori, I., and Napolitano, A. (2011) Zero Energy Building - A review of definitions and calculation methodologies. Energy and Buildings, 43, 971-979.
Nicol, J.F. and Humphreys, M.A. (2002) Adaptive thermal comfort and sustainable thermal standards for buildings. Energy and Buildings, 34, 563-572.
Nielsen, T. (2002) Optimisation of Buildings with Respect to Energy and Indoor Environment, Technical University of Denmark.
Ooka, R. and Komamura, K. (2009) Optimal design method for building energy systems using genetic algorithms. Building and Environment, 44, 1538-1544.
Pagliano, L., Zangheri, P., and Carlucci, S. (2010) A Way To Net Zero Energy Buildings For Italy: How The Earth-To-Air Heat Exchanger Could Contribute To Reach The Target In Warm Climates. In EuroSun 2010 - International Conference on Solar Heating, Cooling and Buildings (Graz).
Pardalos, P. and Resende, M. (2012) Handbook of Applied Optimisation, Oxford University Press, Oxford, UK.
Pedersen, F. (2007) A Method for Optimizing the Performance of Buildings, Technical University of Denmark.
Poli, R., Langdon, W., and McPhee, N. (2008) A field guide to genetic programming.
Price, K., Storn, R., and Lampinen, J. (2005) Differential Evolution: A Practical Approach to Global Optimization, Springer-Verlag, Heidelberg, Germany.
Renders, J. (1994) Hybridizing genetic algorithms with hill-climbing methods for global optimization: two possible ways. Paper presented at: IEEE World Congress on Computational Intelligence (Orlando, FL).
Snyman, J. (2005) Practical mathematical optimisation - An Introduction to Basic Optimisation Theory and Classical and New Gradient-Based Algorithms, Springer Science+Business Media, New York, USA.
Verbeeck, G. (2007) Optimisation of extremely low energy residential buildings. In Department of Civil Engineering (Leuven, Belgium: Catholic University of Leuven).
Wang, W. (2005) A Simulation-Based Optimisation System for Green Building Design, Concordia University, Montreal Quebec.
Wang, W., Rivard, H., and Zmeureanu, R. (2006) Floor shape optimization for green building design. Advanced Engineering Informatics, 20, 363-378.
Wetter, M. (2001) GenOpt? - A Generic Optimization Program. Paper presented at: Building Simulation 2001 Conference (Rio de Janeiro).
Wetter, M. (2004) Simulation-Based Building Energy Optimization, University of California at Berkeley.
Wetter, M. and Polak, E. (2004) Building design optimization using a convergent pattern search algorithm with adaptive precision simulations. Energy and Buildings, 37, 603-612.
Wetter, M. and Wright, J. (2004) A comparison of deterministic and probabilistic optimisation algorithms for nonsmooth simulation-based optimisation. Building and Environment, 39, 989-999.
