Estimating Building Electricity Performance Gaps with Internet of Things Data Using Bayesian Multilevel Additive Modeling
Publication: Journal of Construction Engineering and Management
Volume 146, Issue 12
Abstract
Energy models should be simplified to handle data limitations and should predict reliable energy use. Currently, it remains challenging to ensure an appropriate level of detail for simplifying building energy models and to avoid performance gaps when predicting electricity consumption. In this respect, this research proposes to identify an appropriate level of simplifying a building energy model, predict electricity demands and performance gaps using the simplified energy model, and expand the model usability through the operational stage. Building electricity demands predicted through EnergyPlus (version 8.7.0) simulation are compared with actual electricity data collected through Internet of Things (IoT) sensors. Consideration of performance gaps increases the predictability of electricity consumption of a simplified energy model. Also, the Bayesian multilevel additive model updates the performance gaps along with the collection of new IoT data. The findings of this study contribute to forecasting electricity demands with a simplified energy model by predicting performance gaps that can be applied to predicting the electricity needs of similar buildings in the design stage and controlling operational electricity use in the operational stage by comparing sensor measurement with reference data provided by the energy model.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Data generated or analyzed during the study are available from the corresponding author by request. Information about the Journal’s data-sharing policy can be found here: http://ascelibrary.org/doi/10.1061/(ASCE)CO.1943-7862.0001263.
Acknowledgments
The authors appreciate the contributions from Dr. Kanae Matsui, Assistant Professor in the Department of Electrical and Electronics Systems at Tokyo Denki University, Japan, for supporting Internet of Things data acquisition.
References
Ahmad, M., and C. H. Culp. 2006. “Uncalibrated building energy simulation modeling results.” HVAC R Res. 12 (4): 1141–1155. https://doi.org/10.1080/10789669.2006.10391455.
Bedrick, J. 2008. Organizing the development of a building information model. New York: AIA.
Bhargava, A., P. C. Anastasopoulos, S. Labi, K. C. Sinha, and F. L. Mannering. 2010. “Three-stage least-squares analysis of time and cost overruns in construction contracts.” J. Constr. Eng. Manage. 136 (11): 1207–1218. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000225.
Bürkner, P. C. 2017. “BRMS: An R package for Bayesian multilevel models using Stan.” J. Stat. Software 80 (1): 1–28. https://doi.org/10.18637/jss.v080.i01.
Bürkner, P. C. 2018. “Advanced Bayesian multilevel modeling with the R Package brms.” R J. 10 (1): 395–411. https://doi.org/10.32614/RJ-2018-017.
Cao, S., A. Hasan, and K. Sirén. 2013. “On-site energy matching indices for buildings with energy conversion, storage and hybrid grid connections.” Energy Build. 64 (Sep): 423–438. https://doi.org/10.1016/j.enbuild.2013.05.030.
Chang, S., D. Castro-Lacouture, K. Matsui, and Y. Yamagata. 2019a. “Planning and monitoring of building energy demands under uncertainties by using IoT data.” In Proc., 2019 ASCE Int. Conf. on Computing in Civil Engineering, 17–19. Reston, VA: ASCE.
Chang, S., N. Saha, D. Castro-Lacouture, and P. P. J. Yang. 2019b. “Multivariate relationships between campus design parameters and energy performance using reinforcement learning and parametric modeling.” Appl. Energy 249 (Sep): 253–264. https://doi.org/10.1016/j.apenergy.2019.04.109.
Chen, J., X. Gao, Y. Hu, Z. Zeng, and Y. Liu. 2019. “A meta-model-based optimization approach for fast and reliable calibration of building energy models.” Energy 188 (Dec): 116046. https://doi.org/10.1016/j.energy.2019.116046.
Diamond, R., M. Opitz, T. Hicks, B. Von Neida, and S. Herrera. 2006. “ACEEE 2006 Summer Study, Pacific Grove.” In Evaluating the energy performance of the first generation of LEED-certified commercial buildings. Washington, DC: ACEEE.
Eastman, C., C. M. Eastman, P. Teicholz, R. Sacks, and K. Liston. 2011. BIM handbook: A guide to building information modeling for owners, managers, designers, engineers and contractors. Hoboken, NJ: Wiley.
Gabry, J., D. Simpson, A. Vehtari, M. Betancourt, and A. Gelman. 2019. “Visualization in Bayesian workflow.” J. R. Stat. Soc.: Series A 182 (2): 389–402. https://doi.org/10.1111/rssa.12378.
Gelman, A., and D. B. Rubin. 1992. “Inference from iterative simulation using multiple sequences.” Stat. Sci. 7 (4): 457–472. https://doi.org/10.1214/ss/1177011136.
Heo, Y., R. Choudhary, and G. A. Augenbroe. 2012. “Calibration of building energy models for retrofit analysis under uncertainty.” Energy Build. 47 (Apr): 550–560. https://doi.org/10.1016/j.enbuild.2011.12.029.
Hoffman, M. D., and A. Gelman. 2014. “The No-U-Turn sampler: Adaptively setting path lengths in Hamiltonian Monte Carlo.” J. Mach. Learn. Res. 15 (1): 1593–1623.
Hong, T., and S. H. Lee. 2019. “Integrating physics-based models with sensor data: An inverse modeling approach.” Build. Environ. 154 (May): 23–31. https://doi.org/10.1016/j.buildenv.2019.03.006.
Hopfe, C. J., and J. L. M. Hensen. 2011. “Uncertainty analysis in building performance simulation for design support.” Energy Build. 43 (10): 2798–2805. https://doi.org/10.1016/j.enbuild.2011.06.034.
Jeon, J., J. Lee, and Y. Ham. 2018. “Quantifying the impact of building envelope condition on energy use.” Build. Res. Inf. 47 (4): 404–420. https://doi.org/10.1080/09613218.2018.1448959.
Matsui, K. 2018. “An information provision system to promote energy conservation and maintain indoor comfort in smart homes using sensed data by IoT sensors.” Future Gener. Comput. Syst. 82 (May): 388–394. https://doi.org/10.1016/j.future.2017.10.043.
Matsui, K., Y. Yamagata, and S. Kawakubo. 2019. “Real-time sensing in residential area using IoT technology for finding usage patterns to suggest action plan to conserve energy.” Energy Procedia 158 (Feb): 6438–6445. https://doi.org/10.1016/j.egypro.2019.01.171.
Menezes, A. C., A. Cripps, D. Bouchlaghem, and R. Buswell. 2012. “Predicted vs. actual energy performance of non-domestic buildings: Using post-occupancy evaluation data to reduce the performance gap.” Appl. Energy 97 (Sep): 355–364. https://doi.org/10.1016/j.apenergy.2011.11.075.
Nagel, J. B., and B. Sudret. 2014. “A Bayesian multilevel framework for uncertainty characterization and the NASA Langley multidisciplinary UQ challenge.” In Proc., 16th AIAA Non-Deterministic Approaches Conference (SciTech 2014). Reston, VA: AIAA.
Nagel, J. B., and B. Sudret. 2016. “A unified framework for multilevel uncertainty quantification in Bayesian inverse problems.” Probab. Eng. Mech. 43 (Jan): 68–84. https://doi.org/10.1016/j.probengmech.2015.09.007.
Norford, L. K., R. H. Socolow, E. S. Hsieh, and G. V. Spadaro. 1994. “Two-to-one discrepancy between measured and predicted performance of a ‘low-energy’ office building: Insights from a reconciliation based on the DOE-2 model.” Energy Build. 21 (2): 121–131. https://doi.org/10.1016/0378-7788(94)90005-1.
Polasek, W., and K. Pötzelberger. 1994. “Robust Bayesian methods in simple ANOVA models.” J. Stat. Plann. Inference 40 (2–3): 295–311. https://doi.org/10.1016/0378-3758(94)90127-9.
Raftery, P., M. Keane, and A. Costa. 2011a. “Calibrating whole building energy models: Detailed case study using hourly measured data.” Energy Build. 43 (12): 3666–3679. https://doi.org/10.1016/j.enbuild.2011.09.039.
Raftery, P., M. Keane, and J. O’Donnell. 2011b. “Calibrating whole building energy models: An evidence-based methodology.” Energy Build. 43 (9): 2356–2364. https://doi.org/10.1016/j.enbuild.2011.05.020.
Reddy, T. A. 2006. “Literature review on calibration of building energy simulation.” ASHRAE Trans. 112: 226–240.
Royer, S., S. Thil, T. Talbert, and M. Polit. 2014. “A procedure for modeling buildings and their thermal zones using co-simulation and system identification.” Energy Build. 78 (Aug): 231–237. https://doi.org/10.1016/j.enbuild.2014.04.013.
Ryan, E. M., and T. F. Sanquist. 2012. “Validation of building energy modeling tools under idealized and realistic conditions.” Energy Build. 47 (Apr): 375–382. https://doi.org/10.1016/j.enbuild.2011.12.020.
Salmerón Gómez, R., J. García Pérez, M. D. M. López Martín, and C. G. García. 2016. “Collinearity diagnostic applied in ridge estimation through the variance inflation factor.” J. Appl. Stat. 43 (10): 1831–1849. https://doi.org/10.1080/02664763.2015.1120712.
Soebarto, V. I. 1997. “Calibration of hourly energy simulations using hourly monitored data and monthly utility records for two case study buildings.” In Proc., 4th Int. IBPSA Conf. Denver: International Building Performance Simulation Association.
Turner, C., and M. Frankel. 2008. “Energy performance of LEED® for new construction buildings.” New Build. Inst. 4 (4): 1–42.
Wilde, P. 2014. “The gap between predicted and measured energy performance of buildings: A framework for investigation.” Autom. Constr. 41 (May): 40–49. https://doi.org/10.1016/j.autcon.2014.02.009.
Wood, S. N., F. Scheipl, and J. J. Faraway. 2013. “Straightforward intermediate rank tensor product smoothing in mixed models.” Stat. Comput. 23 (3): 341–360. https://doi.org/10.1007/s11222-012-9314-z.
Zhang, S., P. Huang, and Y. Sun. 2016. “A multi-criterion renewable energy system design optimization for net zero energy buildings under uncertainties.” Energy 94 (Jan): 654–665. https://doi.org/10.1016/j.energy.2015.11.044.
Zhao, H., 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
Journal of Construction Engineering and Management
Volume 146 • Issue 12 • December 2020
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Jan 3, 2020
Accepted: Jun 16, 2020
Published online: Sep 16, 2020
Published in print: Dec 1, 2020
Discussion open until: Feb 16, 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.