Synergizing Design of Building Energy Performance Using Parametric Analysis, Dynamic Visualization, and Neural Network Modeling
Publication: Journal of Architectural Engineering
Volume 29, Issue 4
Abstract
Designing buildings is a multicriteria decision-making process that usually involves a large number of design parameters and several objective functions. The associated combinatorial parametric sensitivity analysis requires numerous simulation runs, which might not be practical, feasible, or both. The parameters related to the design of buildings include geometry and envelope characteristics, uncertainty in internal loads, different HVAC system characteristics, and utility rate structures. A new methodology is proposed that involves three stages: (1) an initial one-factor-at-a-time (OAT) statistical method (Morris method), which is very efficient when identifying the relative importance and interactivity of parameters; (2) the use of parallel coordinates and other graphical plots to help visualize ascertained allowable latitude of parameters dynamically and interactively; and (3) the use of a machine learning algorithm [specifically, artificial neural networks (ANN)] to include the improved granular domain of parameters. This results in more flexibility when exploring the design space and reducing the number of computationally intensive simulation runs without compromising the mathematical resolution and accuracy. The method would empower designers to explicitly analyze the impacts of all major influencing input parameters while providing flexibility to posit different constraints on selected parameters and visualize their interaction with other parameters. In addition, it has advantages over traditional optimization approaches since decisions can be made by assessing and controlling one or more objective functions (response variables or evaluation criteria) and input parameters simultaneously under preset bounds. This is especially useful when there are multiple objective functions that are conflicting. The various stages of the proposed methodology are demonstrated through a hypothetical building design study.
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Data Availability Statement
Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request, including the eQUEST model used for analysis.
References
Addison, M. S. 1988. “A multiple criteria satisficing methodology for the design of energy-efficient buildings.” Masters thesis, Dept. of Planning, Arizona State Univ.
Bichiou, Y., and M. Krarti. 2011. “Optimization of envelope and HVAC systems selection for residential buildings.” Energy Build. 43: 3373–3382. https://doi.org/10.1016/j.enbuild.2011.08.031.
Burhenne, S., D. Jacob, and G. Henze. 2011. “Sampling based on Sobol” sequences for Monte Carlo techniques applied to building simulations.” In Proc., 12th Conf. of Int. Building Performance Simulation Association, 1816–1823, Sydney: International Building Performance Simulation Association.
Campolongo, F., J. Cariboni, and A. Saltelli. 2007. “An effective screening design for sensitivity analysis of large models.” Environ. Modell. Software 22 (10): 1509–1518. https://doi.org/10.1016/j.envsoft.2006.10.004.
Chen, R., and Y.-S. Tsay. 2021. “An integrated sensitivity analysis method for energy and comfort performance of an office building along the Chinese coastline.” Buildings 11: 371. https://doi.org/10.3390/buildings11080371.
Crawley, D. B., C. O. Pedersen, L. K. Lawrie, and F. C. Winkelmann. 2000. “Energyplus: Energy simulation program.” ASHRAE J. 42: 49–56.
Didwania, S. 2015. “Statistical and graphical methods to determine importance and interaction of building design parameters to inform and support design decisions.” Masters thesis, The Design School, Arizona State Univ.
Dutta, R., T. A. Reddy, and G. Runger. 2016. “A visual analytics based methodology for multi-criteria evaluation of building design alternatives.” In Proc., ASHRAE Winter Conf. OR-16-C051. Orlando, FL: ASHRAE.
Ferrara, M., J. Virgone, E. Fabrizio, F. Kuznik, and M. Filippi. 2014. “Modelling zero energy buildings: Parametric study for the technical optimization.” Energy Procedia 62: 200–209. https://doi.org/10.1016/j.egypro.2014.12.381.
Haziza, D., J. Rapin, and G. Synnaeve. 2020. “Hiplot, Interactive high-dimensionality plots.” Github repository. Accessed January 11, 2022. https://github.com/facebookresearch/hiplot.
Hemsath, T. L., and K. A. Bandhosseini. 2015. “Sensitivity analysis evaluating basic building geometry’s effect on energy use.” Renewable Energy 76: 526–538. https://doi.org/10.1016/j.renene.2014.11.044.
Herman, J., and W. Usher. 2017. “SALib: An open-source Python library for sensitivity analysis.” J. Open Source Software 2 (9): 97. https://doi.org/10.21105/joss.00097.
Iwanaga, T., W. Usher, and J. Herman. 2022. “Toward SALib 2.0: Advancing the accessibility and interpretability of global sensitivity analyses.” Socio-Environ. Syst. Modell. 4: 18155. https://doi.org/10.18174/sesmo.18155.
Lam, J. C., and S. C. M. Hui. 1996. “Sensitivity analysis of energy performance of office buildings.” Build. Environ. 31 (1): 27–39. https://doi.org/10.1016/0360-1323(95)00031-3.
Lam, J. C., K. K. W. Wan, and L. Yang. 2008. “Sensitivity analysis and energy conservation measures implications.” Energy Convers. Manage. 49: 3170–3177. https://doi.org/10.1016/j.enconman.2008.05.022.
Menberg, K., Y. Heo, and R. Choudhary. 2016. “Sensitivity analysis methods for building energy models: Comparing computational costs and extractable information.” Energy Build. 133: 433–445. https://doi.org/10.1016/j.enbuild.2016.10.005.
Mocanu, E., P. Nguyen, M. Gibescu, and W. Kling. 2014. “Optimized parameter selection for assessing building energy efficiency.” In Proc., of the IEEE Young Researchers Symp, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE).
Morris, M. D. 1991. “Factorial sampling plans for preliminary computational experiments.” Technometrics 33 (2): 161–174. https://doi.org/10.1080/00401706.1991.10484804.
Most, T., and J. Will. 2011. “Sensitivity analysis using the Metamodel of Optimal Prognosis.” In Proc., Weimar Optimization and Stochastic Days 8.0. Livermore, CA: Dynardo – Dynamic Software & Engineering.
Rallapalli, H. S. 2010. “A comparison of EnergyPlus and eQUEST whole building energy simulation results for a medium sized office building.” Masters thesis, The Design School, Arizona State Univ.
Robertson, J., B. Polly, and J. Collis. 2013. “Evaluation of automated model calibration techniques for residential building energy simulation.” NREL/TP-5500-60127. https://doi.org/10.2172/1220248.
Sanchez, D. G., B. Lacarriere, and B. Bourges. 2014. “Application of sensitivity analysis in building energy simulations: Combining first- and second-order elementary effects methods.” Energy Build. 68: 741–750. https://doi.org/10.1016/j.enbuild.2012.08.048.
Sobolʹ, I. M. 1993. “Sensitivity estimates for nonlinear mathematical models.” Math. Model. Comput. Exp. 4: 407–414.
Sobolʹ, I. M. 2001. “Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates.” Math. Comput. Simul 55: 271–280. https://doi.org/10.1016/S0378-4754(00)00270-6.
Tian, W. 2013. “A review of sensitivity analysis methods in building energy analysis.” Renewable Sustainable Energy Rev. 20: 411–419. https://doi.org/10.1016/j.rser.2012.12.014.
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© 2023 American Society of Civil Engineers.
History
Received: Jul 7, 2022
Accepted: Jul 20, 2023
Published online: Sep 7, 2023
Published in print: Dec 1, 2023
Discussion open until: Feb 7, 2024
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