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A Study of Building Thermal Dynamics from Large Data Sets: An Application for Residential Smart Thermostats

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A Study of Building Thermal Dynamics from Large Data Sets: An Application for Residential Smart Thermostats

John, Camille (2021) A Study of Building Thermal Dynamics from Large Data Sets: An Application for Residential Smart Thermostats. Masters thesis, Concordia University.

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Abstract

This thesis focuses on identifying the Thermal Time Constant (TTC), a thermal performance indicator related the building’s effective thermal insulation, airtightness, and thermal storage capacity. Using data from over 15,000 smart thermostats, data mining is applied to identify patterns in the short-term transient thermal response of Canadian and American dwellings. The data used consist of contextual information (i.e. metadata) and one year of measurements recorded at 5-minute intervals (i.e. indoor air temperature, outdoor air temperature, and Heating, Ventilation and Air-Conditioning (HVAC) equipment run times). The TTC is captured from the data by tracking the indoor temperature response of the free-running dwelling over a specific time period, and by also assuming this response can be accurately described by the characteristic exponential decay (or growth) of a first-order resistance-capacitance thermal model. Consequently, the results show significant differences between estimated TTC values for the summer and winter months across ASHRAE climate zones 1 through 7. In winter, the mean TTC related to these climate zones ranges from 7 to 47 hours. In contrast, the summer mean values vary between a lower and narrower range of 6 to 19 hours which can presumably be attributed to occupants opening the windows, and thus effectively reducing their dwelling’s overall thermal resistance. Towards the larger objectives of thermal resilience, energy savings and grid reliability, the estimated TTC values can be used in the residential sector to quickly identify buildings eligible for building enclosure retrofits or to rapidly generate a simple model to inform thermal load estimation and management.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Building, Civil and Environmental Engineering
Item Type:Thesis (Masters)
Authors:John, Camille
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Building Engineering
Date:1 August 2021
Thesis Supervisor(s):Athienitis, Andreas and Candanedo, José
Keywords:Time constant estimation; dynamic thermal response; residential building; smart thermostat; statistical analysis; grey-box model; thermal resilience
ID Code:988865
Deposited By: CAMILLE JOHN
Deposited On:01 Dec 2021 13:53
Last Modified:01 Dec 2021 13:53

References:

[1] J. A. Leech,W. C. Nelson, R. T. Burnett, S. Aaron, and M. E. Raizenne, “It’s about time: A comparison of Canadian and American time-activity patterns,” Journal of Exposure Analysis and Environmental Epidemiology, vol. 12, no. 6, pp. 427–432, 2002.
[2] Office of Energy Efficiency, “Energy Use Data Handbook Tables – 1990 to 2017,” National Resources Canada, Tech. Rep., 2019. [Online]. Available: https : / / oee.nrcan.gc.ca/corporate/statistics/neud/dpa/menus/trends/
handbook/tables.cfm.
[3] U.S. Energy Information Administration, “April 2019 Monthly Energy Review,” U.S.
Department of Energy, Tech. Rep., 2019. [Online]. Available: https://www.eia.gov/totalenergy/data/monthly/.
[4] U.S. Environmental Protection Agency, Greenhouse Gas Emissions : Sources of Greenhouse Gas Emissions, 2020. [Online]. Available: https://www.epa.gov/ghgemissions/sources-greenhouse-gas-emissions (visited on 07/01/2020).
[5] U.S. Energy Information Administration, “2015 Residential Energy Consumption Survey,” U.S. Department of Energy, Tech. Rep., 2018. [Online]. Available: https://www.eia.gov/consumption/residential/data/2015/.
[6] Canada Energy Regulator, “Canada’s Energy Future 2019 Energy Supply and Demand Projections to 2040,” Canada Energy Regulator, Tech. Rep., 2019. [Online]. Available: https://www.cer-rec.gc.ca/en/data-analysis/canada-energyfuture/2019/index.html.
[7] U.S. Energy Information Administration, “Annual Energy Outlook 2019 with projections to 2050,” U.S. Department of Energy, Tech. Rep., 2019. [Online]. Available: https://www.eia.gov/outlooks/aeo/pdf/0383(2017).pdf.
[8] J. Manyika, M. Chui, P. Bisson, J. Woetzel, R. Dobbs, J. Bughin, and D. Aharon, “The Internet of Things: Mapping the value beyond the hype,” Tech. Rep. June, 2015, p. 144. [Online]. Available: https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/the-internet-of-things-thevalue-of-digitizing-the-physical-world.
[9] Statistics Canada, Table 11-10-0222-01: Household spending, Canada, regions and provinces, 2019. [Online]. Available: https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=1110022201.
[10] Canada Energy Regulator, Market Snapshot: Fuel poverty across Canada – lower energy efficiency in lower income households, 2017. [Online]. Available: https://www.cer-rec.gc.ca/nrg/ntgrtd/mrkt/snpsht/2017/08-05flpvrt-eng.html?=undefined%7B%5C&%7Dwbdisable=true (visited on 07/01/2018).
[11] D. J. Bednar and T. G. Reames, “Recognition of and response to energy poverty in the United States,” Nature Energy, vol. 5, no. June, pp. 432–439, 2020.
[12] S. Jessel, S. Sawyer, and D. Hern´andez, “Energy, Poverty, and Health in Climate Change: A Comprehensive Review of an Emerging Literature,” Frontiers in Public Health, vol. 7,
no. December, 2019.
[13] A. T. D. Perera, V. M. Nik, D. Chen, J.-L. Scartezzini, and T. Hong, “Quantifying the impacts of climate change and extreme climate events on energy systems,” Nature Energy,
vol. 5, no. 2, pp. 150–159, 2020.
[14] W. Maclay and Maclay Architects, The New Net Zero: Leading-Edge Design and Construction of Homes and Buildings for a Renewable Energy Future. White River Junction, VT: Chelsea Green Publishing, 2014.
[15] J. Zheng, D. W. Gao, and L. Lin, “Smart meters in smart grid: An overview,” in IEEE Green Technologies Conference, Denver, CO, United states, 2013, pp. 57–64.
[16] J. Rotondo, R. Johnson, N. Gonzalez, A. Waranowski, C. Badger, N. Lange, E. Goldman, and R. Foster, “Overview of Existing and Future Residential Use Cases for Connected
Thermostats,” U.S. Department of Energy, Tech. Rep., 2016. [Online]. Available:https://www.energy.gov/sites/prod/files/2016/12/f34/
Overview%20of%20Existing%20Future%20Residential%20Use%20Cases%20for%20CT%7B%5C_%7D2016-12-16.pdf.
[17] A. Gonzalez-Vidal, A. P. Ramallo-Gonzalez, F. Terroso-Saenz, and A. Skarmeta, “Data driven modeling for energy consumption prediction in smart buildings,” in 2017 IEEE International Conference on Big Data (Big Data), Piscataway, NJ, USA, 2017, pp. 4562–9.
[18] G. Baasch, P. Westermann, and R. Evins, “Identifying whole-building heat loss coefficient from heterogeneous sensor data: An empirical survey of gray and black box approaches,” Energy and Buildings, vol. 241, p. 110 889, 2021.
[19] C. Miller, Z. Nagy, and A. Schlueter, “A review of unsupervised statistical learning and visual analytics techniques applied to performance analysis of non-residential buildings,” Renewable and Sustainable Energy Reviews, pp. 1–13, 2017.
[20] A. Kavousian, R. Rajagopal, and M. Fischer, “Determinants of residential electricity consumption: using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants’ behavior,” Energy (UK), vol. 55, pp. 184–94, 2013.
[21] S. Verbeke and A. Audenaert, “Thermal inertia in buildings: A review of impacts across climate and building use,” Renewable and Sustainable Energy Reviews, vol. 82, no. August 2017, pp. 2300–2318, 2018.
[22] Building Science Corporation, “Thermal Metric Summary Report,” Tech. Rep., 2013, p. 153. [Online]. Available: https://www.buildingscience.com/documents/special/thermal- metric- documents/thermal- metric- summaryreport.
[23] A. J. Ghajar and Y. A. Cengel, Heat and Mass Transfer: Fundamentals and Applications. McGraw-Hill Education, 2014.
[24] J. Straube, Thermal Control in Buildings, 2006. [Online]. Available: https://www.buildingscience.com/documents/digests/bsd-011-thermal-controlin-buildings (visited on 04/01/2018).
[25] T. Kesik, L. O’Brien, A. Ozkan, and A. Chong, “Thermal Resilience Design Guide,” University of Toronto, Tech. Rep., 2019. [Online]. Available: https://pbs.daniels.utoronto.ca/faculty/kesik%7B%5C_%7Dt/PBS/Kesik-Resources/Thermal-Resilience-Guide-v1.0-May2019.pdf.
[26] P. Gass, D. Echeverr´ıa, and A. Asadollahi, “Cities and Smart Grids in Canada,” International Institute for Sustainable Development, Tech. Rep. September, 2017, p. 36. [Online].
Available: https://www.iisd.org/system/files/publications/cities-smart-grids-canada.pdf.
[27] W. A. Beckman, L. Broman, A. Fiksel, S. A. Klein, E. Lindberg, M. Schuler, and J. Thornton, “TRNSYS. Most complete solar energy system modeling and simulation software,” Renewable energy, vol. 5, no. 1 -4 pt 1, pp. 486–488, 1994.
[28] D. B. Crawley, L. K. Lawrie, F. C.Winkelmann,W. F. Buhl, Y. J. Huang, C. O. Pedersen, R. K. Strand, R. J. Liesen, D. E. Fisher, M. J.Witte, and J. Glazer, “EnergyPlus: Creating a new-generation building energy simulation program,” Energy and Buildings, vol. 33,
no. 4, pp. 319–331, 2001.
[29] James J. Hirsh and Associates, The Home of DOE-2 based Building Energy Use and Cost Analysis Software. [Online]. Available: https://doe2.com/ (visited on 06/01/2019).
[30] D. Coakley, P. Raftery, and M. Keane, A review of methods to match building energy simulation models to measured data, 2014.
[31] M. R. Braun, H. Altan, and S. B. Beck, “Using regression analysis to predict the future energy consumption of a supermarket in the UK,” Applied Energy, vol. 130, pp. 305–313, 2014.
[32] A. S. Ahmad, M. Y. Hassan, M. P. Abdullah, H. A. Rahman, F. Hussin, H. Abdullah, and R. Saidur, “A review on applications of ANN and SVM for building electrical energy consumption forecasting,” Renewable and Sustainable Energy Reviews, vol. 33, pp. 102–109, 2014.
[33] R. Kramer, J. van Schijndel, and H. Schellen, “Simplified thermal and hygric building models: A literature review,” Frontiers of Architectural Research, pp. 318–325, 2012.
[34] A. Athienitis and W. O’Brien, Modeling, design, and optimization of net-zero energy buildings. 2015.
[35] G. Burnand, “The study of the thermal behaviour of structures by electrical analogy,” British Journal of Applied Physics, vol. 3, no. 2, pp. 50–53, 1952.
[36] K. A. Antonopoulos and E. P. Koronaki, “Effect of indoor mass on the time constant and thermal delay of buildings,” International Journal of Energy Research, vol. 24, no. 5, pp. 391–402, 2000.
[37] G. Reynders, J. Diriken, and D. Saelens, “Quality of grey-box models and identified parameters as function of the accuracy of input and observation signals,” Energy Build. (Netherlands), vol. 82, pp. 263–74, 2014.
[38] H. Harb, N. Boyanov, L. Hernandez, R. Streblow, and D. M¨uller, “Development and validation of grey-box models for forecasting the thermal response of occupied buildings,” Energy and Buildings, vol. 117, pp. 199–207, 2016.
[39] G. E. Box, “Science and statistics,” Journal of the American Statistical Association, vol. 71, no. 356, pp. 791–799, 1976.
[40] T. Hong and S. H. Lee, “Integrating physics-based models with sensor data: An inverse modeling approach,” Building and Environment, vol. 154, no. March, pp. 23–31, 2019.
[41] K. A. Antonopoulos and C. Tzivanidis, “Time constant of Greek buildings,” Energy (Oxford), vol. 20, no. 8, p. 785, 1995.
[42] ——, “Finite-difference prediction of transient indoor temperature and related correlation based on the building time constant,” International Journal of Energy Research, vol. 20, no. 6, pp. 507–520, 1996.
[43] T. Catalina, J. Virgone, and E. Blanco, “Development and validation of regression models to predict monthly heating demand for residential buildings,” Energy and Buildings, vol. 40, no. 10, pp. 1825–1832, 2008.
[44] J. Karlsson, L.Wadso, and M. Oberg, “A conceptual model that simulates the influence of high thermal inertia in building structures,” in fib Symposium 2012: Concrete Structures for Sustainable Community - Proceedings, Stockholm, Sweden, 2012, pp. 631–634.
[45] I. S. Walker and A. K. Meier, “Residential Thermostats : Comfort Controls in California Homes,” Lawrence Berkeley National Laboratory, Tech. Rep., 2008.
[46] Honeywell International Inc, Non-Programmable Thermostats. [Online]. Available: https://www.honeywellhome.com/us/en/products/air/thermostats/
non-programmable-thermostats/manual-4-wire-premium-baseboardthermostat-ct410b1017-e1/ (visited on 05/01/2021).
[47] ——, Thermostat. [Online]. Available:https://www.honeywellhome.com/us/en/products/air/thermostats/non-programmable-thermostats/the-round-mercury-free-thermostat-manual-changeover-singlestage-ct87n1001-u1/ (visited on 05/01/2021).
[48] M. Pritoni, A. K. Meier, C. Aragon, D. Perry, and T. Peffer, “Energy efficiency and the misuse of programmable thermostats: The effectiveness of crowdsourcing for understanding household behavior,” Energy Research & Social Science, vol. 8, pp. 190–197,
2015.
[49] ecobee Inc, Smart Thermostat with voice control and Smart Sensor. [Online]. Available:https://www.ecobee.com/en-us/smart-thermostats/smart-wifithermostat-with-voice-control/ (visited on 05/01/2021).
[50] Sensibo, Sensibo: Smart Air Sensing&Control. [Online]. Available: https://sensibo.com/products/sensibo-air-bundle (visited on 05/01/2021).
[51] Honeywell International Inc, T5+ Smart Home Thermostat. [Online]. Available: https://www.honeywellhome.com/us/en/products/air/thermostats/
wifi-thermostats/t5-smart-thermostat-with-c-wire-adapterrcht8612wf2005-u/ (visited on 05/01/2021).
[52] Mysa Smart Thermostats, Mysa for Electric Baseboard Heaters. [Online]. Available:https://shop.getmysa.com/products/mysa- baseboard (visited on 05/01/2021).
[53] Google Inc, Nest thermostats. [Online]. Available: https://store.google.com/ca/magazine/compare%7B%5C_%7Dthermostats?hl=en-GB (visited on 05/01/2021).
[54] T. A. Reddy, Applied Data Analysis and Modeling for Energy Engineers and Scientists.
2011.
[55] J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques. 2012.
[56] Z. Yu, F. Haghighat, and B. C. Fung, “Advances and challenges in building engineering and data mining applications for energy-efficient communities,” Sustainable Cities and Society, vol. 25, pp. 33–38, 2016.
[57] P. Chapman, J. Clinton, R. Kerber, T. Khabaza, T. Reinartz, C. Shearer, and R. Wirth, “Step-by-step data mining guide,” SPSS inc, Tech. Rep., 2000, pp. 1–78. [Online]. Available: http://www.crisp-dm.org/CRISPWP-0800.pdf.
[58] D. T. Larose and C. D. Larose, Data mining and predictive analytics, English, Hoboken, New Jersey, 2015.
[59] ecobee Inc, Installation Guide ecobee 3, 2014. [Online]. Available: https://www.ecobee.com/wp-content/uploads/2013/12/ecobee3%7B%5C_%7DInstallationGuide.
pdf.
[60] NumFocus, Pandas - Python Data Analysis Library. [Online]. Available: https://pandas.pydata.org/ (visited on 12/01/2020).
[61] American Society of Heating Refrigerating and Air-Conditioning Engineers, ANSI/ASHRAE Standard 90.2-2018: Energy-Efficient Design of Low-Rise Residential Buildings. Atlanta, GA, 2018.
[62] National Renewable Energy Laboratory, Ashrae Climate Zones, 2011. [Online]. Available: https://openei.org/wiki/ASHRAE%7B%5C_%7DClimate%7B%5C_
%7DZones (visited on 10/01/2018).
[63] R. Talami, C. Karmann, F. Bauman, S. Schiavon, and P. Raftery, “Recent trends in radiant system technology in North America,” Tech. Rep., 2017.
[64] J. Vivian, A. Zarrella, G. Emmi, and M. De Carli, “An evaluation of the suitability of lumped-capacitance models in calculating energy needs and thermal behaviour of buildings,” Energy and Buildings, vol. 150, pp. 447–465, 2017.
[65] C. John, C. Vallianos, J. Candanedo, and A. Athienitis, “Estimating time constants for over 10,000 residential buildings in North America: towards a statistical characterization of thermal dynamics,” in Proceedings of the 7th International Building Physics Conference - Healthy, Intelligent and Resilient Buildings and Urban Environments, 2018.
[66] The SciPy Community, scipy.stats.kstest - Scipy v1.6.1 Reference Guide. [Online]. Available:
https://docs.scipy.org/doc/scipy/reference/generated/
scipy.stats.kstest.html (visited on 03/01/2020).
[67] ——, scipy.stats.johnsonsb - SciPy v1.6.1 Reference Guide. [Online]. Available: https:/ / docs. scipy.org/doc/scipy/reference/ generated/scipy.stats.johnsonsb.html (visited on 03/01/2021).
[68] M. J. Oliveira Pan˜ao, N. M. Mateus, and G. Carrilho da Grac¸a, “Measured and modeled performance of internal mass as a thermal energy battery for energy flexible residential buildings,” Applied Energy, vol. 239, no. October 2018, pp. 252–267, 2019.
[69] P. Palensky and D. Dietrich, “Demand side management: Demand response, intelligent energy systems, and smart loads,” IEEE Transactions on Industrial Informatics, vol. 7, no. 3, pp. 381–388, 2011.
[70] E. Chung, What caused the deadly power outages in Texas and how Canada’s grid compares, 2021. [Online]. Available: https://www.cbc.ca/news/technology/power-outages-texas-canada-1.5920833 (visited on 02/01/2021).
[71] J. A. Candanedo, V. R. Dehkordi, and P. Lopez, “A multi-level architecture to facilitate MPC implementation in commercial buildings : basic principles and case study,” no. May, 2013.
[72] N. Henao, M. Fournier, S. Kelouwani, and T. De, Characterizing Smart Thermostats Operation in Residential Zoned Heating Systems and its Impact on Energy Saving Metrics,” in Proceedings of eSim 2018, the 10 conference of IBPSA-Canada, 2018, pp. 17–
25.
[73] American Society of Heating Refrigerating and Air-Conditioning Engineers, 2017 ASHRAE Handbook: Fundamentals. Atlanta, GA, 2017.
[74] J. Date, J. A. Candanedo, and A. K. Athienitis, “Control-Oriented Modelling of Thermal Zones in a House : A Multi-Level Approach,” in International High Performance Buildings Conference, 2016, pp. 1–10.
[75] Y. Chen, Z. Tong, W. Wu, H. Samuelson, A. Malkawi, and L. Norford, “Achieving natural ventilation potential in practice: Control schemes and levels of automation,” Applied Energy, vol. 235, no. November 2018, pp. 1141–1152, 2019.
[76] A. Rabl and L. K. Norford, “Peak load reduction by preconditioning buildings at night,” International Journal of Energy Research, vol. 15, no. 9, pp. 781–98, 1991.
[77] C. John, “Grey-box models: toward a systematic approach for design and operation of thermal zones,” Countribution to foundation paper by J. A. Candanedo, A. K. Athienitis et al., in preparation,
[78] ecobee Inc, How accurate is the temperature sensor in the ecobee? [Online]. Available:https://support.ecobee.com/hc/en- us/articles/227869067-How-accurate-is-the-temperature-sensor-in-the-ecobee- (visited
on 07/01/2018).
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