Early-Stage Advisory System for Energy-Conscious Design of Building Façade Systems
Publication: Construction Research Congress 2022
ABSTRACT
The selection of high-energy performance facade systems could play a pivotal role in enhancing energy efficiency and promoting sustainability in buildings. However, many decisions on the selection of building facade systems are made at the early building design stage when no detailed building information is available. Therefore, an intelligent advisory system is needed to support designers and architects in selecting high-performance facade systems at the early stage of building design. The main objective of this paper is to develop an advisory system that extracts underlying structures from the thermal behavior of façade systems and expresses them as recommendations to support architects and designers in the early stages of building design for the selection of high-performance and energy-efficient façade systems. The proposed advisory system extracts useful rules from data using the association rule mining technique and expresses them as recommendations to support the selection of façade systems. As an illustration, the proposed system was examined and validated on an ultra-high-performance fiber-reinforced-concrete (UHP-FRC) façade panel. The results showed that the proposed advisory system facilitates decision-making in selecting high-energy performance facade systems in the early building design stage by providing insights into the performance of facade panels in various building contexts and weather conditions.
Get full access to this article
View all available purchase options and get full access to this chapter.
REFERENCES
Abediniangerabi, B. (2019, December). Assembly-scale and whole-building energy performance analysis of ultra-high-performance fiber-reinforced concrete (UHP-FRC) facade systems. Ph.D. Dissertation, The University of Texas at Arlington, Texas.
Abediniangerabi, B., Makhmalbaf, A., and Shahandashti, M. (2021). Deep Learning for Estimating Energy Savings of Early-Stage Facade Design Decisions. Energy and AI, 100077.
Abediniangerabi, B., Shahandashti, S. M., and Makhmalbaf, A. (2020a). A data-driven framework for energy-conscious design of building facade systems. Journal of Building Engineering, 29, 101172.
Abediniangerabi, B., Shahandashti, M., and Makhmalbaf, A. (2020b). Coupled transient heat and moisture transfer investigation of facade panel connections. Journal of Engineering, Design and Technology.
Abediniangerabi, B., Shahandashti, S. M., Bell, B., Chao, S. H., and Makhmalbaf, A. (2018). Building energy performance analysis of ultra-high-performance fiber-reinforced concrete (UHP-FRC) façade systems. Energy and Buildings, 174, 262–275.
Abediniangerabi, B., Shahandashti, S. M., Bell, B., Chao, S. H., and Makhmalbaf, A. (2019, June). Assembly-scale and whole-building energy performance analysis of ultra-high-performance fiber-reinforced concrete (UHP-FRC) facade systems. In International Interactive Symposium on Ultra-High Performance Concrete (Vol. 2, No. 1). Iowa State University Digital Press.
Aghdasi, P., Heid, A. E., and Chao, S.-H. (2016), Developing Ultra-High-Performance Fiber-Reinforced Concrete For Large-Scale Structural Applications, ACI Materials Journal, V. 113, No. 5, September-October 2016, pp. 559–570.
Asadi, S., Amiri, S. S., and Mottahedi, M. (2014). On the development of multi-linear regression analysis to assess energy consumption in the early stages of building design. Energy and Buildings, 85, 246–255.
Ashouri, M., Haghighat, F., Fung, B. C., Lazrak, A., and Yoshino, H. (2018). Development of building energy saving advisory: A data mining approach. Energy and Buildings, 172, 139–151.
De Wit, S., and Augenbroe, G. (2002). Analysis of uncertainty in building design evaluations and its implications. Energy and buildings, 34(9), 951–958.
Gan, W., Lin, J. C. W., Chao, H. C., and Zhan, J. (2017). Data mining in distributed environment: a survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 7(6), e1216.
Han, J., Kamber, M., and Pei, J. (2012). Classification: advanced methods. Data mining concepts and techniques, 393–443.
Kotsiantis, S., and Pintelas, P. (2004). Recent advances in clustering: A brief survey. WSEAS Transactions on Information Science and Applications, 1(1), 73–81.
Lucchino, E. C., Gelesz, A., Skeie, K., Gennaro, G., Reith, A., Serra, V., and Goia, F. (2021). Modelling double skin façades (DSFs) in whole-building energy simulation tools: Validation and inter-software comparison of a mechanically ventilated single-story DSF. Building and Environment, 107906.
Nikolaou, T. G., Kolokotsa, D. S., Stavrakakis, G. S., and Skias, I. D. (2012). On the application of clustering techniques for office buildings’ energy and thermal comfort classification. IEEE Transactions on Smart Grid, 3(4), 2196–2210.
Orlandic, R., Lai, Y., and Yee, W. G. (2005, October). Clustering high-dimensional data using an efficient and effective data space reduction. In Proceedings of the 14th ACM international conference on Information and knowledge management (pp. 201–208). ACM.
Salleb-Aouissi, A., Vrain, C., Nortet, C., Kong, X., Rathod, V., and Cassard, D. (2013). QuantMiner for mining quantitative association rules. The Journal of Machine Learning Research, 14(1), 3153–3157.
Shahandashti, S. M., Abediniangerabi, B., Bell, B., and Chao, S. H. (2017). Probabilistic building energy performance analysis of ultra-high-performance fiber-reinforced concrete (UHP-FRC) façade system. In Computing in Civil Engineering 2017 (pp. 223–230).
Thalfeldt, M., Pikas, E., Kurnitski, J., and Voll, H. (2013). Facade design principles for nearly zero energy buildings in a cold climate. Energy and Buildings, 67, 309–321.
Tong, S., Wen, J., Wong, N. H., and Tan, E. (2021). Impact of façade design on indoor air temperatures and cooling loads in residential buildings in the tropical climate. Energy and Buildings, 110972.
U.S. Department of Energy, Building Technologies Office. (2018, August 08). EnergyPlus. Available from https://energyplus.net/.
Yu, Z., Fung, B. C., Haghighat, F., Yoshino, H., and Morofsky, E. (2011). A systematic procedure to study the influence of occupant behavior on building energy consumption. Energy and buildings, 43(6), 1409–1417.
Information & Authors
Information
Published In
Construction Research Congress 2022
Pages: 195 - 204
History
Published online: Mar 7, 2022
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.