Hostname: page-component-8448b6f56d-tj2md Total loading time: 0 Render date: 2024-04-19T17:40:56.985Z Has data issue: false hasContentIssue false
  • English
  • Français

Determining the cost optimum among a discrete set of building technologies to satisfy stringent energy targets

Published online by Cambridge University Press:  07 October 2015

Brian Simmons ,
Matthias H.Y. Tan ,
C.F. Jeff Wu  and
Godfried Augenbroe
Brian Simmons*
Affiliation:
Georgia Institute of Technology, Atlanta, Georgia, USA
Matthias H.Y. Tan
Affiliation:
Georgia Institute of Technology, Atlanta, Georgia, USA
C.F. Jeff Wu
Affiliation:
Georgia Institute of Technology, Atlanta, Georgia, USA
Godfried Augenbroe
Affiliation:
Georgia Institute of Technology, Atlanta, Georgia, USA
*
Reprint requests to: Brian Simmons, 11 Barrows Street, Apartment 6A, Boston, MA 02134. E-mail: brian.simmons@retroficiency.com
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper presents the development of an optimization methodology for selecting the lowest monetary cost combinations of building technologies to meet set operational energy reduction targets. The new optimization algorithm introduced in this paper departs from the notion that optimal design choices over a large set of design parameters and properties can be driven by energy targets. We assume that design parameters are determined by many concurrent considerations fighting over the attention span of the design team. Our approach starts from a design outcome and asks the question, which set of discrete technologies are the right mix to reach an energy target in the cost optimal way? Such an approach has to face the challenge that the properties of market-available building technologies have a discrete nature that makes their optimal selection a combinatorial problem. The optimization algorithm searches the discrete combinatoric space by maximizing the following objective function: calculated energy savings divided by premium cost, where cost is defined as the additional cost over a baseline solution. The algorithm is codified into a custom MATLAB script and when compared to prescriptive methodologies is shown to be more cost effective and generically applicable given a palette of building technology alternatives and their corresponding cost data.

Keywords

Architectural DesignBuilding SimulationComputational DesignDesign AlternativesDesign Decision MakingDesign OptimizationOptimization Algorithm
Type
Special Issue Articles
Information
AI EDAM , Volume 29 , Special Issue 4: Generative and evolutionary design exploration , November 2015 , pp. 417 - 427
Copyright
Copyright © Cambridge University Press 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Anon. (n.d.). Kemco. Accessed at http://www.kemco.or.kr// in September 2012.Google Scholar
Anon. (n.d.). Passivhaus. Accessed at http://passivehaus.org.uk/ in September 2012.Google Scholar
ASHRAE. (n.d.). Advanced Energy Design Guides. Atlanta, GA: American Society of Heating, and Refrigerating and Air-Conditioning Engineers, Inc. Accessed at http://www.ashrae.org/standards-research--technology/advanced-energy-design-guides in September 2012.Google Scholar
Augenbroe, G., & Park, C.-S. (2005). “Quantification methods of technical building performance.” Building Research & Information 33(2), 159–172.CrossRefGoogle Scholar
Augenbroe, G. (2011). The role of simulation in performance based building. In Building Performance Simulation for Design and Operation (Hensen, J.L.M., & Lamberts, L., Eds), Chap. 2. New York: Spon Press.Google Scholar
Blum, C., & Roli, A. (2003). Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Computing Surveys 35(3), 268–308.CrossRefGoogle Scholar
Christensen, C., Anderson, R., Horowitz, S., Courtney, A., & Spencer, J. (2006). BEopt Software for Building Energy Optimization: Features and Capabilities, Report No. NREL/TP-550-39929. Washington, DC: US Department of Energy.CrossRefGoogle Scholar
Colorni, A., Dorigo, M., Maffioli, F., Maniezzo, V., Righini, G., & Trubian, M. (1996). “Heuristics from nature for hard combinatorial optimization problems.” International Transactions in Operational Research 3(1), 1–21.CrossRefGoogle Scholar
Heo, Y., Augenbroe, G., & Choudhary, R. (2011). Risk analysis of energy-efficiency projects based on Bayesian calibration of building energy models. Proc. Building Simulation, Sydney, Australia, November 14–16, 2011.Google Scholar
Kim, J.-H., Augenbroe, G., & Suh, H.-S. (2012). Comparative study of the LEED and ISO-CEN building energy performance rating methods. Proc. Building Simulation, Chambery, France, August 26–28.Google Scholar
Kottek, M., Grieser, J., Bect, C., Rudolph, B., & Rubel, F. (2006). World map of the Koppen–Geiger climate classification updated. Meteorologische Zeitschrift 15(3), 259–263.CrossRefGoogle Scholar
Lee, S.H., Fei, Z., Augenbroe, G. (2011). The use of normative energy calculation beyond building performance rating systems. Proc. 12th Conf. Int. Building Performance Simulation Association, pp. 2753–2760, Sydney, November 14–16.Google Scholar
Leiserson, C.E., Rivest, R.L., & Stein, C. (2001). Introduction to Algorithms (Cormen, T.H., Ed.), Cambridge MA: MIT Press.Google Scholar
Nguyen, A.-T., Reiter, S., & Rigo, P. (2014). A review on simulation-based optimization methods applied to building. Applied Energy 113, 1043–1058.CrossRefGoogle Scholar
Richardson, I., Thomson, M., & Infield, D. (2008). A high-resolution domestic building occupancy model for energy demand simulations. Energy and Buildings 40(8), 1560–1566.CrossRefGoogle Scholar
Salminen, M., Palonen, M., & Sirén, K. (2012). Combined energy simulation and multi-criteria optimisation of a LEED-certified building. Proc. 1st Building Simulation and Optimization Conf., Loughborough, UK, September 10–11.Google Scholar
Wetter, M., & Wright, J. (2003). Comparison of a generalized pattern search and genetic algorithm optimization method. Proc. 8th IBPSA Conf. (Augenbroe, G., & Hensen, J, Eds.), pp. 1401–1408, Eindhoven, The Netherlands, August 11–14.Google Scholar
Wu, C.F.J., & Hamada, M.S. (2009). Experiments: Planning, Analysis, and Optimization, 2nd ed.Hoboken, NJ: Wiley.Google Scholar