Data-Driven Approach for Evaluating the Energy Efficiency in Multifamily Residential Buildings
Publication: Practice Periodical on Structural Design and Construction
Volume 26, Issue 2
Abstract
Cities account for more than 70% of global fossil fuel use and greenhouse gas emissions. This number is likely to increase due to urban population growth. Much of the energy used in cities is consumed in buildings (e.g., for space conditioning and lighting). Better understanding of energy use patterns therefore is paramount. This paper leveraged advances in machine learning to model energy consumption in residential buildings and gain insights into building energy consumption trends in Chicago. By merging demographic and socioeconomic data collected from the US Census Bureau with energy benchmarking data for Chicago, three models were developed using three different machine learning algorithms: back-propagation neural network (BPNN), extreme gradient boosting (XGBoost), and random forest (RF). The results showed that XGBoost better predicts the building energy use, with an accuracy of 68%. Furthermore, Shapley Additive Explanations (SHAP) was used to interpret the impact of each variable used on building energy consumption. Overall, the insights gained in this study can help policy makers and planners to address building energy use better.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
All the data, models, and/or code that support the findings of this study are available from the corresponding author upon reasonable request.
References
Abbasabadi, N., M. Ashayeri, R. Azari, B. Stephens, and M. Heidarinejad. 2019. “An integrated data-driven framework for urban energy use modeling (UEUM).” Appl. Energy 253 (Nov): 113550. https://doi.org/10.1016/j.apenergy.2019.113550.
Afkhamiaghda, M., A. Mahdaviparsa, K. Afsari, and T. McCuen. 2019. “Occupants behavior-based design study using BIM-GIS integration: An alternative design approach for architects.” In Advances in informatics and computing in civil and construction engineering, 765–772. Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-030-00220-6_92.
Alvanchi, A., and A. Seyrfar. 2019. “Improving facility management of public hospitals in Iran using building information modeling.” Sci. Iranica. https://doi.org/10.24200/sci.2019.50186.1562.
Amasyali, K., and N. M. El-Gohary. 2018. “A review of data-driven building energy consumption prediction studies.” Renewable Sustainable Energy Rev. 81 (Jan): 1192–1205. https://doi.org/10.1016/j.rser.2017.04.095.
Badhrudeen, M., N. Naranjo, A. Movahedi, and S. Derrible. 2020. “Machine learning based tool for identifying errors in CAD to GIS converted data.” In Proc., CIGOS 2019, Innovation for Sustainable Infrastructure, 1185–1190. Singapore: Springer.
Bocchini, P., D. M. Frangopol, T. Ummenhofer, and T. Zinke. 2014. “Resilience and sustainability of civil infrastructure: Toward a unified approach.” J. Infrastruct. Syst. 20 (2): 04014004. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000177.
Chen, T., and C. Guestrin. 2016. “XGBoost: A scalable tree boosting system.” In Proc., 22nd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 785–794. New York: Association for Computing Machinery. https://doi.org/10.1145/2939672.2939785.
City of Chicago. 2017. “2017 Chicago energy benchmarking report.” Accessed February 23, 2020. https://www.chicago.gov/content/dam/city/progs/env/EnergyBenchmark/2017_Chicago_Energy_Benchmarking_Report.pdf.
City of Chicago. 2018. “Chicago energy benchmarking—2017 data reported in 2018.” Accessed February 23, 2020. https://data.cityofchicago.org/Environment-Sustainable-Development/Chicago-Energy-Benchmarking-2017-Data-Reported-in-/j2ev-2azp.
City of Chicago. 2020. “Chicago data portal; 2015.” Accessed February 23, 2020. https://data.cityofchicago.org/Buildings/Building-Footprints-current-/hz9b-7nh8?category=Buildings&view_name=Building-Footprints-current-.
CSUN (Complex and Sustainable Urban Networks) Lab. 2020. “Datasets.” Accessed December 12, 2020. http://csun.uic.edu/datasets.html.
De Prado, M. L. 2018. Advances in financial machine learning. New York: Wiley.
Derrible, S. 2019. Urban engineering for sustainability. Cambridge, MA: MIT Press.
Esmalian, A., M. Ramaswamy, K. Rasoulkhani, and A. Mostafavi. 2019. “Agent-based modeling framework for simulation of societal impacts of infrastructure service disruptions during disasters.” In Computing in civil engineering 2019: Smart cities, sustainability, and resilience, 16–23. Reston, VA: ASCE. https://doi.org/10.1061/9780784482445.003.
Farokhnia, K., J. W. van de Lindt, and M. Koliou. 2020. “Selection of residential building design requirements to achieve community functionality goals under tornado loading.” Pract. Period. Struct. Des. Constr. 25 (1): 04019035. https://doi.org/10.1061/(ASCE)SC.1943-5576.0000464.
Han, J., J. Pei, and M. Kamber. 2011. Data mining: Concepts and techniques. Amsterdam, Netherlands: Elsevier.
Institute for Market Transformation. 2019. “Map: US city and county policies for existing buildings: Benchmarking, transparency and beyond.” Accessed February 23, 2020. https://www.imt.org/resources/map-u-s-city-and-county-benchmarking-policies-for-existing-private-buildings/.
James, G., D. Witten, T. Hastie, and R. Tibshirani. 2013. Vol. 112 of An introduction to statistical learning, 3–7. New York: Springer.
Kapousouz, E., A. Seyrfar, S. Derrible, and H. Ataei. 2021. “A clustering analysis of energy and water consumption in US states from 1985 to 2015.” In Data science applied to sustainability analysis, edited by J. Dunn and P. Balaprakash. Amsterdam, Netherlands: Elsevier.
Khanmohammadi, S., H. Farahmand, and H. Kashani. 2018. “A system dynamics approach to the seismic resilience enhancement of hospitals.” Int. J. Disaster Risk Reduct. 31 (Oct): 220–233. https://doi.org/10.1016/j.ijdrr.2018.05.006.
Koezjakov, A., D. Urge-Vorsatz, W. Crijns-Graus, and M. van den Broek. 2018. “The relationship between operational energy demand and embodied energy in Dutch residential buildings.” Energy Build. 165 (Apr): 233–245. https://doi.org/10.1016/j.enbuild.2018.01.036.
Kontokosta, C. E., and R. K. Jain. 2015. “Modeling the determinants of large-scale building water use: Implications for data-driven urban sustainability policy.” Sustainable Cities Soc. 18 (Nov): 44–55. https://doi.org/10.1016/j.scs.2015.05.007.
Lee, D., and S. Derrible. 2020. “Predicting residential water demand with machine-based statistical learning.” J. Water Resour. Plann. Manage. 146 (1): 04019067. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001119.
Lee, D., S. Derrible, and F. C. Pereira. 2018. “Comparison of four types of artificial neural network and a multinomial logit model for travel mode choice modeling.” Transp. Res. Rec. 2672 (49): 101–112. https://doi.org/10.1177/0361198118796971.
Li, Q., S. J. Quan, G. Augenbroe, P. P.-J. Yang, and J. Brown. 2015. “Building energy modelling at urban scale: Integration of reduced order energy model with geographical information.” In Proc., BS2015: 14th Conf. of Int. Building Performance Simulation Association. Hyderabad, India: International Building Performance Simulation Association.
Liaw, A., and M. Wiener. 2002. “Classification and regression by randomForest.” R News 2 (3): 18–22.
Lundberg, S. M., and S. I. Lee. 2017. “A unified approach to interpreting model predictions.” In Advances in neural information processing systems, 4765–4774. New York: Association for Computing Machinery.
Meng, T., D. Hsu, and A. Han. 2017. “Estimating energy savings from benchmarking policies in New York City.” Energy 133: 415–423. https://doi.org/10.1016/j.energy.2017.05.148.
Mohammadi, S., B. de Vries, and W. Schaefer. 2013. “A comprehensive review of existing urban energy models in the built environment.” In Planning support systems for sustainable urban development, 249–265. Berlin: Springer.
Mohareb, E., S. Derrible, and F. Peiravian. 2016. “Intersections of Jane Jacobs’ conditions for diversity and low-carbon urban systems: A look at four global cities.” J. Urban Plann. Dev. 142 (2): 05015004. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000287.
Movahedi, A., and S. Derrible. 2020. “Interrelated patterns of electricity, gas, and water consumption in large-scale buildings.” J. Ind. Ecol. https://doi.org/10.31224/osf.io/ahn3e.
Open Data DC. 2019. “Building energy benchmarks.” Accessed February 23, 2020. https://opendata.dc.gov/datasets/building-energy-benchmarks/data.
Parsa, A. B., A. Movahedi, H. Taghipour, S. Derrible, and A. K. Mohammadian. 2020. “Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis.” Accid. Anal. Prev. 136: 105405. https://doi.org/10.1016/j.aap.2019.105405.
Parsa, A. B., H. Taghipour, S. Derrible, and A. K. Mohammadian. 2019. “Real-time accident detection: Coping with imbalanced data.” Accid. Anal. Prev. 129 (Aug): 202–210. https://doi.org/10.1016/j.aap.2019.05.014.
Pedregosa, F., et al. 2011. “Scikit-learn: Machine learning in Python.” J. Mach. Learn. Res. 12 (Oct): 2825–2830.
Peduzzi, P., J. Concato, E. Kemper, T. R. Holford, and A. R. Feinstein. 1996. “A simulation study of the number of events per variable in logistic regression analysis.” J. Clin. Epidemiol. 49 (12): 1373–1379. https://doi.org/10.1016/S0895-4356(96)00236-3.
Peng, Y., A. Rysanek, Z. Nagy, and A. Schlüter. 2018. “Using machine learning techniques for occupancy-prediction-based cooling control in office buildings.” Appl. Energy 211: 1343–1358. https://doi.org/10.1016/j.apenergy.2017.12.002.
Seto, K. C., et al. 2014. “Human settlements, infrastructure and spatial planning.” In Climate change 2014: Mitigation of climate change. IPCC working group III contribution to AR5. Cambridge, England: Cambridge University Press.
Shadravan, S., L. Fithian, M. Callahan, and M. Afkhamiaghda. 2019. “Design technology: Architects’ early impact on indoor air quality.” In Proc., Architectural Engineering Conf. 2019: Integrated Building Solutions, 224–231. Tysons, VA: Architectural Engineering Institute of ASCE. https://doi.org/10.1061/9780784482261.026.
Shapley, L. S. 1953. A value for n-person games. Contributions to the Theory of Games, 307–317. Santa Monica, CA: RAND Corporation.
Swan, L. G., and V. I. Ugursal. 2009. “Modeling of end-use energy consumption in the residential sector: A review of modeling techniques.” Renewable Sustainable Energy Rev. 13 (8): 1819–1835. https://doi.org/10.1016/j.rser.2008.09.033.
United States Census Bureau. 2020. “American community survey.” Accessed November 23, 2020. https://www.census.gov/acs/www/data/data-tables-and-tools/data-profiles/2017/.
US Energy Information Administration. 2019. “Annual energy review.” Accessed February 23, 2019. https://www.eia.gov/totalenergy/data/annual/index.php.
Wei, Y., X. Zhang, Y. Shi, L. Xia, S. Pan, J. Wu, M. Han, and X. Zhao. 2018. “A review of data-driven approaches for prediction and classification of building energy consumption.” Renewable Sustainable Energy Rev. 82: 1027–1047. https://doi.org/10.1016/j.rser.2017.09.108.
Yohanis, Y. G., and B. Norton. 2002. “Life-cycle operational and embodied energy for a generic single-storey office building in the UK.” Energy 27 (1): 77–92. https://doi.org/10.1016/S0360-5442(01)00061-5.
Zhao, H.-X., and F. Magoulès. 2012. “A review on the prediction of building energy consumption.” Renewable Sustainable Energy Rev. 16 (6): 3586–3592. https://doi.org/10.1016/j.rser.2012.02.049.
Information & Authors
Information
Published In
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Mar 2, 2020
Accepted: Sep 29, 2020
Published online: Dec 28, 2020
Published in print: May 1, 2021
Discussion open until: May 28, 2021
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.