Baba, Fuad (2022) Assessment and Mitigation of Overheating Risks in Archetype and Existing Canadian Buildings under Recent and Projected Future Climates. PhD thesis, Concordia University.
Preview |
Text (application/pdf)
5MBBaba_PhD_S2022 .pdf - Accepted Version Available under License Spectrum Terms of Access. |
Abstract
This research aims to develop a framework to assess and mitigate the overheating risks under projected future climates for both archetype and existing buildings. More specific objectives are to 1) determine the contribution and correlation of individual building envelope parameters to the change in indoor temperature in conjunction with ventilation, therefore, to determine whether high-energy-efficient buildings required by Canadian building codes to reduce heating consumption in new buildings are at lower or greater overheating risk compared to old buildings; 2) develop an automated calibration procedure to calibrate a building simulation model based on the indoor hourly temperature to achieve high accuracy to be used overheating studies in existing buildings; 3) assess overheating risks under current and future extreme years and recommend effective mitigation measures; and 4) provide an optimal design for retrofitting existing buildings to achieve lowest heating energy demand and highest thermal and visual comfort in new building design. To achieve these objectives, a robust sensitivity-analysis (SA) and calibration method, a systematic framework for evaluating overheating and passive mitigation measures, and an optimization methodology are developed and applied to an archetype detached-house and existing-school-buildings.
The results showed that the archetype and existing Canadian buildings have experienced overheating under current climates and the overheating risks will increase dramatically under future climates. Due to the positive contribution of lower U-values of windows, walls, and roofs and SHGC, high-energy-efficient houses have a lower overheating risk than old buildings if adequate ventilation (>2.2-ACH) is provided. Natural ventilation in the high-energy-efficient house is sufficient to reduce the overheating risk under the recent climate but will require adding interior and exterior shading under future climates. For existing-school buildings, the calibrated model achieved high accuracy. The results also showed that the use of exterior blind roll or a combination of night cooling and other mitigation measures that reduce solar heat gain is required under the recent climate and adding a cool roof will be required in future extreme years. For optimization design, the applied optimization methodology can generate several optimal building design solutions based on Window-Wall-Ratio.
Divisions: | Concordia University > Gina Cody School of Engineering and Computer Science > Building, Civil and Environmental Engineering |
---|---|
Item Type: | Thesis (PhD) |
Authors: | Baba, Fuad |
Institution: | Concordia University |
Degree Name: | Ph. D. |
Program: | Building Engineering |
Date: | 29 May 2022 |
Thesis Supervisor(s): | Ge, Hua |
Keywords: | Variance-based analysis, Calibration methodology, overheating assessment, mitigation measures, climate change, future climate generation, Reference Summer Weather Years |
ID Code: | 990685 |
Deposited By: | FUAD BABA |
Deposited On: | 27 Oct 2022 14:01 |
Last Modified: | 27 Oct 2022 14:01 |
References:
Alcayde, A., Baños, R., Gil, C., et al. (2011). Optimization methods applied to renewable and sustainable energy: a review. Renewable Sustainable Energy Reviews. 15, 1753-1766Amoako-Attah, J., B-Jahromi, A. (2016). The Impact of Different Weather Files on London Detached Residential Building Performance—Deterministic, Uncertainty, and Sensitivity Analysis on CIBSE TM48 and CIBSE TM49 Future Weather Variables Using CIBSE TM52 as Overheating Criteria. Sustainability. 8, 1194.
Andrade-Cabrera, C., Burke, D., Turner, N., Finn D.P. (2017). Ensemble Calibration of lumped parameter retrofit building models using Particle Swarm Optimization. Energy and Buildings. 155, 513–532
Andrade-Cabrera, C., Turner, N., Burke, D., Neu, O., Finn, D.P. (2016). Lumped parameter building model calibration using Particle Swarm Optimization. Proceedings of the 3rd Asia Conference of International Building Performance Simulation Association (ASIM 2016).
ANSI/ASHRAE Standard 55. (2017) Thermal Environmental Conditions for Human Occupancy. American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc., Atlanta.
Archer, G., Saltelli, A., Sobol, I. (1997). Sensitivity measures, ANOVA-like Techniques and the use of bootstrap. Statistical Computation and Simulation. 58, 99-120
Arida, M., Nassif, N., Talib, R., et al. (2017). Building Energy Modeling Using Artificial Neural Networks. Energy Research. 7, 24-34
Armour, K. C. (2017). Energy budget constraints on climate sensitivity in light of inconstant climate feedbacks. Nature Climate Change. 7, 331–335
Ascione, F., Bianco, N., De Stasio, C., et al. (2017). Energy retrofit of educational buildings: Transient energy simulations, model calibration and multi-objective optimization towards nearly zero-energy performance Energy and Buildings. 144, 303–319.
Ascione, F., Bianco, N., Mauro, G.M., et al. (2019). Building envelope design: Multi-objective optimization to minimize energy consumption, global cost and thermal discomfort. Application to different Italian climatic zones. Energy. 174, 359-374
ASHRAE ANSI/ASHRAE Standard 62.1. (2019). Ventilation for acceptable indoor air quality. American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc., Atlanta, GA
ANSI/ASHRAE Standard 62.2. (2019). Ventilation and acceptable indoor air quality in residential buildings. American Society of Heating, Refrigerating and Air Conditioning Engineers, Atlanta, GA.ASHRAE Guideline 14. (2014). Measurement of Energy and Demand Savings. American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc., Atlanta.
ASHRAE. ANSI/ASHRAE/IES Standard 90.1 (2016). Energy Standard For Building Except Low-Rise Residential Buildings, I-P. ASHRAE, Atlanta, GA.
ASHRAE/Technical Committee. (2002). ASHRAE Guideline 14 Measurement of Energy and Demand Savings. Vol. 8400. United States of America: ASHRAE Press
Attia S., Carlucci S., Hamdy M., et al. (2013). Assessing gaps and needs for integrating building performance optimization tools in net-zero energy buildings design. Energy and Buildings, 60, 110-124
Atkinson J., Chartier Y., Pessoa C., et al. (2009). WHO- Natural Ventilation for Infection Control in Health-Care Settings.
Auzeby, M., Wei, S., Underwood, C., et al. (2017). Using phase change materials to reduce overheating issues in UK residential buildings. Energy Procedia. 105, 4072-4077
Awada, M., Srour, I. (2018). A genetic algorithm-based framework to model the relationship between building renovation decisions and occupants' satisfaction with indoor environmental quality. Building and Environment. 146, 247-257
Baba, F., Ge, H. (2019a). Effect of climate change on the energy performance and thermal comfort of high-rise residential buildings in cold climates. Central European Symposium on Building Physics. 282, 02066.
Baba, F., Ge, H. (2019b). Effect of Climate Change and Extreme Weather Events on the Thermal Conditions of Canadian Multi-unit Residential Buildings. ASHRAE Transactions. 125, 30-33.
Baba, F. M., Ge, H., Zmeureanu, R., Wang, L. L. (2022a). Calibration of building model based on indoor temperature for overheating assessment using genetic algorithm: Methodology, evaluation criteria, and case study. Building and Environment, 207, 108518
Baba, F. M., Ge, H., Wang, L. L., & Zmeureanu, R. (2022). Do high energy-efficient buildings increase overheating risk in cold climates? Causes and mitigation measures required under recent and future climates. Building and Environment, 109230.
Baborska-Marozny, M., Stevenson, F., Grudzinska, M. (2017). Overheating in retrofitted flats: occupant practices, learning and interventions. Building and Research and Information. 45, 40-59.
Bäck, T. (1996). Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford Univ. Press.
Bäck, T., Fogel, D., Michalewicz, Z. (1997). Handbook of Evolutionary Computation. Oxford Univ. Press.
Bakir, G. (2007). Foundation Predicting structured data. Massachusetts Institute of Technology
Baniassadi, A., Sailor, D. (2018). Synergies and trade-offs between energy efficiency and resiliency to extreme heat – A case study. Building and Environment. 132, 263- 272
Banihashemi, S., Ding, G., Wang, J. (2017). Developing a hybrid model of prediction and classification algorithms for building energy consumption. Energy Procedia, 110, 371-376
Banzhaf, W., Nordin, P., Keller, R., et al (1998). Genetic Programming - An Introduction, Morgan Kaufmann, San Francisco
Basu, R., Samet, J. M. (2002). Relation between Elevated Ambient Temperature and Mortality: A Review of the Epidemiologic Evidence. Epidemiologic Reviews, 24 (2002); 190–202.
BB101. (2006). Building Bulletin 101: A design guide: Ventilation of school buildings. Department for Education and Skills (DfES). London.
BB101. (2016). Building Bulletin 101: Guidelines on ventilation, thermal comfort and indoor air quality in schools. Department for Education and Skills (DfES). London.
BB101. (2018). Building Bulletin 101: Guidelines on ventilation, thermal comfort and indoor air quality. Department for Education and Skills (DfES). London.
BC Energy Step Code. (2019). Design Guide Supplement S3 on Overheating and Air Quality. 40 Pages
BC Hydro Power Smart. (2014). Building Envelope Thermal Bridging Guide: analysis, application and insights. A technical report. 870 pages.
Beizaee, A., Lomas, K. J., Firth, S. K. (2013). National survey of summertime temperature and overheating risk in English homes. Building and Environment. 65, 1-17.
Belcher, S.E., Hacker, J.N., Hacker, N. (2005). Constructing design weather data for future climates. Building Service Engineering. 26, 49-61
Berardi, U. (2017). Sustainability Assessments of Buildings. Sustainability.
Berger, J., Orlande, H., Mendes, N., et al. (2016). Bayesian inference for estimating thermal properties of a historic building wall. Building and Environment. 106, 327-339,
Bolstad, W.M. (2010). Understanding computational Bayesian statistics. Wiley
Booth, A., Choudhary, R., Spiegelhalter, D. (2012). Handling uncertainty in housing stock models. Building and Environment. 48, 35–47.
Borden, K. A., Cutter, S. L. (2008). Spatial patterns of natural hazards mortality in the United States. International Journal of Health Geographics. 7; 1-13
Boyce, P. (2003). Human Factors in Lighting. 2rd ed.; Taylor & Francis/CRC Press: Boca Raton, FL, USA.
Brambilla, A., Bonvin, J., Flourentzou, F., et al. (2018). On the Influence of Thermal Mass and Natural Ventilation on Overheating Risk in Offices. Buildings. 8, 47-62
British Columbia. (2021). Statistics & Geospatial Data-Wildfire Statistics. URL: https://www.gov.bc.ca/gov/content/safety/wildfire-status/about-bcws/wildfire-statistics (accessed January 24, 2022).
British Columbia. (2021). School district boundaries. URL: https://globalnews.ca/news/7987756/first-time-ever-bc-schools-close-extreme-heat/ (accessed January 24, 2022).
BS EN 15251. (2007). Indoor environmental input parameters for design and assessment of energy performance of buildings addressing indoor air quality, thermal environment, lighting and acoustics. Standard by British-Adopted European Standard.
Burhenne, S., Elci, M., Jacob, D., et al. (2010). Sensitivity analysis with building simulations to support the commissioning process. Proceedings of the tenth international conference for enhanced building operations, Kuwait.
Cacabelos, A., Eguía, P., Luís Míguez, J., et al. (2015). Calibrated simulation of a public library HVAC system with a ground-source heat pump and a radiant floor using TRNSYS and GenOpt. Energy and Buildings, 108, 114-126
Campolongo, F., Cariboni, J., Saltelli, A. (2007). An effective screening design for sensitivity analysis of large models. Environmental Modelling & Software, 22, 1509-1518
Cannon, A.J. (2018). Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables. Climate dynamics. 50, 31-49
Carlucci, S., Cattarin, G., Causone, F., et al. (2015). Multi-objective optimization of a nearly zero-energy building based on thermal and visual discomfort minimization using a non-dominated sorting genetic algorithm (NSGA-II), Energy and Buildings, 104, 378-394
Carstens, H., Xia, X., Yadavalli, S. (2017). Low-cost energy meter calibration method for measurement and verification. Applied Energy, 188, 563-575,
Center for International Earth Science Information Network (CIESIN). (2014). A Review of Downscaling Methods For Climate Change Projections Report. URL: http://www.ciesin.org/documents/Downscaling_CLEARED_000.pdf (accessed January 24, 2022).
Cerezo-Mota, R., Cavazos, T., Arritt, R., et al. (2016). CORDEX-NA: Factors inducing dry/wet years on the North American Monsoon region. International journal of climatology. 36, 824–836.
Chaloeytoy, K., Ichinose, M., Chien. S. (2020). Determination of the Simplified Daylight Glare Probability (DGPs) Criteria for Daylit Office Spaces in Thailand. Buildings. 10, 180
Chvatal, K., Corvacho, H. (2009). The impact of increasing the building envelope insulation upon the risk of overheating in summer and an increased energy consumption. Building Performance Simulation. 3, 267-282
CIBSE TM52. (2013). Limits of Thermal Comfort: Avoiding Overheating in European Buildings.
CIBSE. (2002). Weather, solar and illuminance data CIBSE Guide J. London: Chartered Institution of Building Services Engineers.
CIBSE. (2014). Design for future climate: Case studies. The Chartered Institution of Building Services Engineers. London, UK.
CIBSE. (2014). TM49-2014: Design Summer Years for London. London, UK.
CIBSE. (2017). Air Induction Unit (AIU); Air Ventilation Assessment (AVA)., 147, 162. Climate Leadership Group (C40), 47–49. Architecture, 162, 226.
CIBSE. (2017). TM 59: design methodology for the assessment of overheating risk in homes. London: CIBSE.
CIBSE. (2013). TM52: 2013 the limits of thermal comfort in European buildings. London: CIBSE
CIBSE-Guide A. (2011). Environmental design. London: Chartered Institution of Building Services England.
Coakley, D., Raftery, P., Keane, M. (2014). A review of methods to match building energy simulation models to measured data. Renewable and Sustainable Energy Reviews. 37, 123–141
Coakley, D., Raftery, P., Molloy, P. (2012). Calibration of whole building energy simulation models: detailed case study of a naturally ventilated building using hourly measured data. First Building Simulation Optimal Conference. 57-64
Coakley, D., Raftery, P., Molloy, P. et al. (2011). Calibration of a Detailed BES Model to Measured Data Using an Evidence-Based Analytical Optimisation Approach. In Proceedings of the Building Simulation, Sydney, Australia.374–381.
Concalves, V., Ogunjimi, Y., Heo, Y. (2021). Scrutinizing modeling and analysis methods for evaluating overheating risks in passive houses. Energy and Buildings. 234, 110701
Cornaro, C., Puggioni, V.A., Strollo, R.M. (2016). Dynamic simulation and on-site measurements for energy retrofit of complex historic buildings: Villa Mondragone case study. Building Engineering. 6, 17–28
Council of Ministers of Education Canada. (2021). Elementary and Secondary Education. URL: https://www.cmec.ca/299/education-in-canada-an- overview/index.html#:~:text=Schools%20and%20Enrolments,10%2C100%20elementary (accessed January 24, 2022).
Czitrom, V. (1999). One-Factor-at-a-Time Versus Designed Experiments. American Statistician, 53, 126-131.
Cukier, R.I., Fortuin, C.M., Shuler, K. (1973). Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients. Chemical physics. 59, 3814-3878
Daly, D., Cooper, P., Ma, Z. (2014). Implications of global warming for commercial building retrofitting in Australian cities. Building and Environment, 74, 86-95
DCLG. (2021). The English Housing Survey. London: Department for Communities and Local Government.
De Luca, F., Kiil, M., Simson, R., et al. (2019). Evaluating daylight factor standard through climate based daylight simulations and overheating regulations in Estonia. Proceedings of the 16th IBPSA Conference, Rome, Italy. Building Simulation.
Deb, K., Pratap, A., S. Agarwal, A. et al. (2002) A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions Evolutionary Computation, 6, 182-197
Dengel, A., Swainson, M., Ormandy, D., et al. (2016). Overheating in dwellings Guidance Document. Building Research Establishment. URL: https://www.bre.co.uk/filelibrary/Briefing%20papers/116885-Overheating-Guidance-v3.pdf (accessed January 24, 2022).
Delgarm, N., Sajadi, B., Azarbad, K., et al. (2018). Sensitivity analysis of building energy performance: a simulation-based approach using OFAT and variance-based sensitivity analysis methods. Building Engineering. 15, 181-193
Després, C., Lajoie, A., Lemieux, S., et al. (2017). Healthy Schools - Healthy Lifestyles: A Literature Review on the Built Environment Contribution. In Health and Wellbeing for Interior Architecture, edited by Dan Kopec, 123–136. New York: Routledge, Taylor and Francis Ltd.
Dickinson R, Brannon B. (2016). Generating future weather files for resilience. In: Proceedings of the international conference on passive and low energy architecture, Los Angeles, CA, USA. 11–3. URL: WeatherShift http://www.weather-shift.com/ (accessed January 24, 2022).
DOE. (2021a). EnergyPlus-Engineering Reference. Daylighting- https://bigladdersoftware.com/epx/docs/8-8/input-output-reference/group-daylighting.html (accessed January 24, 2022).
DOE. (2021b). EnergyPlus-Engineering Reference. Airflow Network Model- URL: https://bigladdersoftware.com/epx/docs/8-3/engineering-reference/airflownetwork-model.html (accessed January 24, 2022).
Donovan, A., O'Sullivan, P., Murphy, M. (2019). Predicting air temperatures in a naturally ventilated nearly zero energy building: Calibration, validation, analysis and approaches. Applied Energy. 250, 991-1010
Drouin, M. (2018). EXTREME HEAT: 66 DEATHS IN MONTRÉAL IN 2018. CIUSSS DU CENTRE SUD DE L’ÎLE-DE MONTRÉAL URL: https://santemontreal.qc.ca/en/public/fh/news/news/extreme-heat-66-deaths-in-montreal-in-2018/ (accessed January 24, 2022).
Dubrul, C. (1988). Inhabitant behavior with respect to ventilation – a summary report of lEA Annex VIII. Berks UK
Eiben, A.E., Smith, J.E. (2003). Introduction to Evolutionary Computing. Springer
Elzeyadi, I., Gatland, S.D. (2019). The impact of the exterior envelope on thermal comfort perceptions in offices. Thermal performance of the exterior envelopes of whole buildings XIV international conference, 107-114
Encinas, F., Herde A. (2013). Sensitivity analysis in building performance simulation for summer comfort assessment of apartments from the real estate market. Energy and Buildings. 65. 55-65
Environment Canada. (2018). Warm-season weather hazards. Environment Canada. URL: https://www.canada.ca/en/environment-climate-change/services/seasonal-weather-hazards/warm-season-weather-hazards.html. (accessed January 24, 2022).
European Committee for Standardization. EN 15193-1. (2017). Energy Performance of Buildings—Energy Requirements for Lighting; European Committee for Standardization (CEN): Brussels, Belgium.
European Commission. (2022). Building stock characteristics. https://ec.europa.eu/energy/eu-buildings-factsheets-topics-tree/building-stock-characteristics_en
European Commission. (2018). BS EN 17037: Daylight in Buildings https://www.en-standard.eu/bs-en-17037-2018-daylight-in-buildings/ (accessed January 24, 2022).
European Union. (2022). Nearly Zero Energy Buildings. https://energy.ec.europa.eu/topics/energy-efficiency/energy-efficient-buildings/nearly-zero-energy-buildings_en
Fedorczak-Cisak, M., Nowak, K., Furtak, M. (2020). Analysis of the Effect of Using External Venetian Blinds on the Thermal Comfort of Users of Highly Glazed Office Rooms in a Transition Season of Temperate Climate—Case Study. Energies. 13, 81-102
Feist, W., Pfluger, R., Kaufmann, B., et al. (2015). PHPP passive house planning package Version 9; the energy balance and design tool for efficient buildings and retrofits. Darmstadt: Passive House Institute.
Ferrara, M., Sirombo, E., Fabrizio, E. (2018). Energy-optimized versus cost-optimized design of high-performing dwellings: The case of multifamily buildings. Science and Technology for the Built Environment, 24, 513-528
Flanagan, R. (2018). Toronto beats temperature record as students swelter in schools without AC. URL: https://www.ctvnews.ca/canada/toronto-beats-temperature-record-as-students-swelter-in-schools-without-ac-1.4081007 (accessed January 24, 2022).
Fletcher, M.J., Johnston, D.K., Glew, D.W., et al. (2017). An empirical evaluation of temporal overheating in an assisted living Passivhaus dwelling in the UK. Building and Environment. 121, 106-118.
Fosas, D., Coley, A., Natarajan, S., et al. (2018). Mitigation versus adaptation: Does insulating dwellings increase overheating risk? Building and Environment, 143, 740–759.
Gagge, A. P., Stolwijk, J. A. J. Nishi, Y. (1986). A Standard Predictive Index of Human Responses to the Thermal Environment. ASHRAE Transactions, 92, 709–731.
Gagnon, D., Schlader, Z.J., Crandall, C.G. (2015). Sympathetic activity during passive heat stress in healthy aged humans: Ageing and MSNA during heat stress. Physiol. 593, 2225–2235
Gamero-Salinas, J., Monge-Barrio, A., Sánchez-Ostiz, A. (2020). Overheating risk assessment of different dwellings during the hottest season of a warm tropical climate. Building and Environnent. 171, 106664
Garcia Sanchez, D., Lacarrière, B., Musy, M., et al. (2012). Application of sensitivity analysis in building energy simulations: combining first and second-orderer elementary effects methods. Energy and Buildings. 68, 741-750
Gaur, A., Lacasse, M., Armstrong, M. (2019). Climate Data to Undertake Hygrothermal and Whole Building Simulations Under Projected Climate Change Influences for 11 Canadian Cities. Data-MDPI, 4, 72-83
Giorgi, F. (2019). Thirty Years of Regional Climate Modeling: Where Are We and Where Are We Going next? Journal of Geophysical Research: Atmospheres. 124, 5696–5723
Giorgi, F., Jones, C., Asrar, G. (2009). Addressing climate information needs at the regional level: the CORDEX framework. World meteorological organization. 58, 175–183
GIS climate change UCAR. (2019). Climate change scenarios- what is downscaling? URL: https://gisclimatechange.ucar.edu/question/63 (accessed January 24, 2022).
Giuli, V., Pos, O., Carli, M. (2012). Indoor environmental quality and pupil perception in Italian primary schools. Building and Environment. 56, 335-345
Goia, F., Time, B., Gustavsen, A. (2015). Impact of opaque building envelope configuration on the heating and cooling energy need of a single family house in cold climates. Energy Procedia. 78, 2626–2631.
Gou, S., Nik, V.M., Scartezzini J.L., et al. (2018). Passive design optimization of newly-built residential buildings in Shanghai for improving indoor thermal comfort while reducing building energy demand. Energy and Buildings, 169, 484-506
Gouvernement du Québec. (2021). Québec infrastructure plan 2021- 2031 Report. URL: https://www.tresor.gouv.qc.ca/fileadmin/PDF/budget_depenses/21-22/6-Quebec_Infrastructure_Plan.pdf (accessed January 24, 2022).
Government du Québec. (2021). Québec infrastructure plan 2017- 2027 Report- Appendix 2: detailed inventory school boards. (accessed January 24, 2022). URL: https://www.tresor.gouv.qc.ca/fileadmin/PDF/budget_depenses/17-18/quebecPublicInfrastructure.pdf
Government of Canada. (2014). Action for Seniors report. 27 pages: https://www.canada.ca/en/employment-social-development/programs/seniors-action-report.html#tc2a (accessed January 24, 2022).
Grussa, D., Andrews, D., Lowry, G., et al. (2019). A London residential retrofit case study: evaluating passive mitigation methods of reducing risk to overheating through the use of solar shading combined with night-time ventilation. Building Services Engineering Research Technology. 40, 385–388
Gu, L., Crawley, D. (2009). Overview of EnergyPlus development: new capabilities and applications for integrated building system design. Presented at CHAMPS 2009. Syracuse University.
Guerra Santin O., Itard, L., Visscher, H. (2009). The effect of occupancy and building characteristics on energy use for space and water heating in Dutch residential stock. Energy and Buildings. 41, 1223-1232
Gunay, H., O’Brien, W., Beausoleil-Morrison, I. (2016). Implementation and comparison of existing occupant behaviour models in EnergyPlus. Building Performance Simulation. 9, 567–588
Gupta, R., Kapsali, M. (2015). Empirical assessment of indoor air quality and overheating in low-carbon social housing dwellings in England, UK. Advances in Building Energy Research. 23 pages
Haensler, A.; Hagemann, S.; Jacob, D. (2011a). Dynamical downscaling of ERA40 reanalysis data over southern Africa: Added value in the simulation of the seasonal rainfall characteristics. International journal of climatology. 31, 2338–2349.
Hamdy, M., Carluccia, S., Hoes, P., Hensen, J. (2017). The impact of climate change on the overheating risk in dwellings-A Dutch case study. Building and Environment. 122, 307-323
Hamdy, M., Nguyen, A.T., Hensen, J.L.M. (2016). A performance comparison of multi-objective optimization algorithms for solving nearly-zero-energy-building design problems. Energy and Buildings. 121, 57-71
Hausfather Z. (2019). CMIP6: the next generation of climate models explained. Carbon brief. URL: https://www.carbonbrief.org/cmip6-the-next-generation-of-climate-models-explained (accessed January 24, 2022).
Hawkins, E.; Sutton, R. (2009). The potential to narrow uncertainty in regional climate predictions. Bulletin of the American Meteorological Society. 90, 1095–1108.
Hawkins, E.; Sutton, R. (2011). The potential to narrow uncertainty in projections of regional precipitation change. Climate dynamics. 37, 407–418
HC. (2011b). Adapting to Extreme Heat Events: Guidelines for Assessing Health Vulnerability. Prepared by: Water, Air and Climate Change Bureau Healthy Environments and Consumer Safety Branch. Health Canada, Ottawa.
Helton, J. (1993). Uncertainty and sensitivity analysis techniques for use in performance assessment for radioactive waste disposal. Reliability Engineering and System Safety. 42, 327-367.
Helton, J., Davis, F. (2002). Illustration of sampling-based methods for uncertainty and sensitivity analysis. Risk Analysis. 22, 591-622.
Heo, Y., Grazizno, D., Guzowski, L., et al. (2015). Evaluation of calibration efficacy under different levels of uncertainty. Building Performance Simulation. 8, 135-144
Heo, Y., Augenbreo, G., Choudhary, R. (2012). Calibration of building energy models for retrofit analysis under uncertainty. Energy and Buildings. 47, 550-560
Hempel S, Frieler K, Warszawski L, Schewe J, Piontek F. (2013). A trend-preserving bias correction-the ISI-MIP approach. Earth System Dynamics. 4, 219–236.
Hoffmann, S., Lee. E.S., McNeil, A., et al. Balancing Daylight, Glare, and Energy-Efficiency Goals: an Evaluation of Exterior Coplanar Shading Systems Using Complex Fenestration Modeling Tools. Energy and Buildings. 112, 279-298.
Holland J.H. (1975). Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. University of Michigan Press, Ann Arbor, MI
Holland, H. (2006) Genetic Algorithms - Computer programs that "evolve" in ways that resemble natural selection can solve complex problems even their creators do not fully understand. URL: http://www2.econ.iastate.edu/tesfatsi/holland.GAIntro.htm (accessed January 24, 2022).
Hong, T., Kim, J., Jeong, J., et al. (2017). Automatic calibration model of a building energy simulation using optimization algorithm. Energy Procedia. 105, 3698–3704.
Hong, T., Kim, J., Lee, M. (2019). A multi-objective optimization model for determining the building design and occupant behaviors based on energy, economic, and environmental performance. Energy. 174, 823-834.
Hooff, T., Blocken, B., Hensen, J., Timmermans, H. (2014). On the predicted effectiveness of climate adaptation measures for residential buildings. Building and Environment. 82, 300–316.
Hoseinzadeh, S., Ghasemiasl, R., Bahari, A., et al. (2017). Type WO3 semiconductor as a cathode electrochromic material for ECD devices. Materials Science: Materials in Electronics. 28, 14446-14452.
Hopfe, C., Hensen, J. (2011). Uncertainty analysis in building performance simulation for design support. Energy and Buildings. 43, 2798-2805.
Hopfe, C., Hensen, J., Plokker, W. (2007). Uncertainty and sensitivity analysis for detailed design support. Proceedings of the 10th IBPSA Building Simulation Conference, Tsinghua University, Beijing.
Hoppe, P. (1999). The physiological equivalent temperature - a universal index for the biometeorological assessment of the thermal environment. International Journal of Biometeorology. 43, 71–75
Huang, K., Huang, W., Lin, T., et al. (2015). Implementation of green building specification credits for better thermal conditions in naturally ventilated school buildings. Building and Environment. 86, 141-150
Huanga, K., Hwang R.. (2017). Future trends of residential building cooling energy and passive adaptation measures to counteract climate change: The case of Taiwan. Applied Energy. 1230-1240.
Huanga, K., Hwang, R. L. (2016). Future trends of residential building cooling energy and passive adaptation measures to counteract climate change: The case of Taiwan. Applied Energy. 184, 1230-1240.
Hughes, C., Natarajan, S. (2019). Summer thermal comfort and overheating in the elderly. Building Services Engineering Research Technology. 40, 426–445
Ibrahim, A., Pelsmakers, S. (2018). Low-energy housing retrofit in North England: Overheating risks and possible mitigation strategies. Building services engineering research and technology. URL: https://journals.sagepub.com/doi/full/10.1177/0143624418754386 (accessed January 24, 2022).
Illuminating Engineering Society of North America (IESNA). (2013). IES LM-83-12 IES Spatial Daylight. Autonomy (sDA) and Annual Sunlight Exposure (ASE), IESNA Lighting; IESNA: New York, NY, USA
Iman, R., Helton, J. (1991). The repeatability of uncertainty and sensitivity analysis for complex probabilistic risk assessment. Risk Analysis. 11, 591-606.
International Energy Agency (IEA). (2020). Sustainable Recovery-World Energy Outlook Special Report. https://www.iea.org/reports/sustainable-recovery/buildings
Institute of Medicine (IOM). (2011). Climate Change. The Indoor Environment, and Health. Institute of Medicine of the National Academies Press, Washington, DC.
International Passive House Association. (2022). Passive House Guidelines. URL: https://passivehouse-international.org/index.php?page_id=80 (accessed January 24, 2022).
International WELL building institute. (2019). WELL Building Standard: V1 with Q1 2019 Addenda. Delos Living LLC: New York, NY, USA,
IPCC. (1996). Climate Change 1995: A report of the Intergovernmental Panel on Climate Change, Second Assessment Report of the Intergovernmental Panel on Climate Change, IPCC.
IPCC. (2001). Climate Change 2001: Synthesis Report. Contribution of Working Groups I, II and III to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Geneva, IPCC Secretariat.
IPCC. (2007). Climate Change 2007: Synthesis Report. Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC, Geneva, Switzerland.
IPCC. (2012). Glossary of terms. In: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. 555-569
IPCC. (2014). Climate change 2014: synthesis report. Contribution of working groups I, II and III to the fifth assessment report of the Intergovernmental Panel on climate change. IPCC.
IPCC. (2018). Global warming of 1.5°C report. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty
IPCC. (2019). Guidance on the use of data. What is a GCM?
URL: http://www.ipcc-data.org/guidelines/pages/gcm_guide.html (accessed January 24, 2022).
IPCC. (2021). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press.
IPCC. (2022). Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change
IPMVP. (2001). International Performance Measurement & Verification Protocol—Concepts and Options for Determining Energy and Water Savings, Volume I, Oak Ridge.
Jacob D, Petersen J, Eggert B. et al. (2014). EURO-CORDEX: New high-resolution climate change projections for European impact research. Regional Environmental Change. 14, 563–578.
Jafari, A., Valentin, V. (2017). An optimization framework for building energy retrofits decision-making. Building and Environment. 115, 118-129
Jaynes, E.T. (2003). Probability theory: The logic of science, Cambridge University Press, Cambridge, U.K.
Jentsch M.F., James P.A., Bourikas L., Bahaj A.S. (2013). Transforming existing weather data for worldwide locations to enable energy and building performance simulation under future climates. Renew Energy. 55, 514-524 –CCWorldWeatherGen program
Jentsch, M. F. (2012). Climate Change Weather File Generators Technical reference manual for the CCWeatherGen and CCWorldWeatherGen tools report. University of Southampton.
Jentsch, M.F., Eames, M.E., Levermore, G.J. (2015). Generating near-extreme Summer Reference Years for building performance simulation. Building Services Engineering Research & Technology. 36, 701-727
Jianling, C. (2009) Multi-objective optimization of cutting parameters with improved NSGA-II. 2009 International Conference on Management and Service Science
Jindal, A. (2018). Thermal comfort study in naturally ventilated school classrooms in composite climate of India. Building and Environment. 142, 34-46
Johnson, H., Kovats, R.S., McGregor, G., et al. (2005). Black. The impact of the 2003 heat wave on mortality and hospital admissions in England. Health Stat. Q.
Johnson, N.R. (2017). Building Energy Model Calibration for Retrofit Decision Making. Master Thesis, Portland State University. USA.
Joint Research Centre. (2015). European Commission SIMLAB: Sensitivity analysis software. URL: https:// ec.europa.eu/jrc/en/samo/simlab (accessed January 24, 2022).
Jones B., Kirby R. (2012). Indoor Air Quality in U.K. School Classrooms Ventilated by Natural Ventilation Windcatchers. International Journal of Ventilation,10, 323-337
Kang, Y., Krarti, M. (2016). Bayesian-Emulator based parameter identification for calibrating energy models for existing buildings. Building Simulation. 411-428,
Kennedy, J., Eberhart, R.C. (1995). Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks. 1942–1948.
Kennedy, M. C. O'Hagan A. (2001). Bayesian calibration of computer models. Royal Statistical Society. 63, 425-464
Kenny, G. P., Yardley, J., Brown, C. (2010). Heat stress in older individuals and patients with common chronic diseases. CMAJ, 182, 1053–1060.
Kim, Y.J., Park, C.S. (2016). Stepwise deterministic and stochastic calibration of an energy simulation model for an existing building. Energy and Buildings. 133, 455–468.
Klepeis, N.E., Nelson, W.C., Robinson, J.P., et al. (2011). The National Human Activity Pattern Survey (NHAPS): A resource for assessing exposure to environmental pollutants. Exposure Analysis and Environmental Epidemiology.11, 231–52.
Ko, W., Brager, G., Schiavo, S. (2017). Building Envelope Impact on Human Performance and Well-Being: Experimental Study on View Clarity. Escholash.-CBE center for the built environment. eScholarship.
Kodali, S.P., Kudikala, R., Deb, K. (2008). Multi-objective optimization of surface grinding process using NSGA II. First International Conference on Emerging Trends in Engineering and Technology
Kosatsky, T. (2010). Hot Day Deaths, Summer 2009: What Happened and How to Prevent a Recurrence. BCMJ, 52, 261.
Kristensen, M.H., Choudhary, R., Petersen, S. (2017). Bayesian calibration of building energy models: comparison of predictive accuracy using metered utility data of different temporal resolution. Energy Procedia. 122, 277-282,
Kumar, P., Podzun, R., Hagemann, S., et al. (2014). Impact of modified soil thermal characteristic on the simulated monsoon climate over south Asia. Journal of Earth System. 123, 151–160.
Lakhdari, K., Sriti, L., Painter, B. (2021). Parametric optimization of daylight, thermal and energy performance of middle school classrooms, case of hot and dry regions. Building and Environment. 204, 108173
Lane, K., Wheeler, K., Charles-Guzman, K., et al. (2014). Extreme Heat Awareness and Protective Behaviors in New York City. Urban Health. 91, 403–414.
Laouadi, A., Bartko, M., Lacasse, M.A. (2020a). A new methodology of evaluation of overheating in buildings. Energy and Buildings. 226, 110360
Laouadi, A., Gaur A., Lacasse, M. A., et al. (2020). Development of reference summer weather years for analysis of overheating risk in buildings. Building Performance Simulation. 13, 301-319
Lapointe, L. (2021). 70% of sudden deaths recorded during B.C. heat wave were due to extreme temperatures, coroner confirms. CBC News. URL: https://www.cbc.ca/news/canada/british-columbia/bc-heat-dome-sudden-deaths-570-1.6122316 (accessed January 24, 2022).
Lara, R.A., Naboni, E., Pernigotto, G., et al. (2017). Optimization Tools for Building Energy Model Calibration. Energy Procedia. 111, 1060–1069.
Lebel, G., Bustinza, R., Dubé, M. (2017). Analyse des impacts des vagues régionales de chaleur extrême sur la santé au Québec de 2010 à 2015. Institut national de santé publique du Québec.
Leech, J.A., Nelson, W.C., Burnett, R.T., et al. (2002). It's about time: A comparison of Canadian and American time-activity patterns. Exposure Analysis and Environmental Epidemiology, 12, 427–32
Levermore, G.J., Parkinson, J.B. (2006). Analyses and algorithms for new Test Reference Years and Design Summer Years for the UK. Building Services Engineering Research and Technology. 27, 311–325
Li, Q., Gu, L., Augenbroe, G., et al. (2015). Calibration of dynamic building energy models with multiple responses using Bayesian inference and linear regression models. Building physics conference international 6th, 13-19
Li, Z., Huang, G., Huang, W., et al. (2018). Future changes of temperature and heat waves in Ontario, Canada. Theoretical and Applied Climatology. 132, 1029-1038
Liang, H., Lin, T., Hwang, R. (2012). Linking occupants’ thermal perception and building thermal performance in naturally ventilated school buildings. Applied Energy. 94, 355-363
Littlefair, P.J. (2005). Avoiding air conditioning. Constructing the Future. 24, 11-20
Liu, C., Kershaw, T., Fosas. D., et al. (2017). High resolution mapping of overheating and mortality risk Building and Environment. 122, 1-14.
Loenhout, J.A.F., le Grand, A., Duijm, F., et al. (2016). The effect of high indoor temperatures on self-perceived health of elderly persons. Environmental Research. 146, 27–34.
Lozinsky, C., Touchie, M. (2018). Improving energy model calibration of multi-unit residential buildings through component air infiltration testing. Building and Environment. 134. 218-229
Machard, A., Inard, C., Alessandrini, J. (2020). A Methodology for Assembling Future Weather Files Including Heatwaves for Building Thermal Simulations from the European Coordinated Regional Downscaling Experiment (EURO-CORDEX) Climate Data. Energies. 13, 3424
Macintyrea, H.L. Heaviside, C. (2019). Potential benefits of cool roofs in reducing heat-related mortality during heatwaves in a European city. Environment International. 127, 430-441
Maivel, M., Kurnitski, J., Kalamees, T. (2015). Field survey of overheating problems in Estonian apartment buildings. Architectural Science Review. 5, 1-10
Manfren, M., Aste, N., Moshksar, R. (2013). Calibration and uncertainty analysis for computer models—A meta-model based approach for integrated building energy simulation. Applied Energy. 103, 627–641.
Martínez-Mariño, S., Eguía-Oller, P., Granada-Álvarez, E., et al. (2021). Simulation and validation of indoor temperatures and relative humidity in multi-zone buildings under occupancy conditions using multi-objective celebration. Building and Environment. 200, 107973.
Masui, T., Matsumoto, K., Hijioka, Y., et al. (2011). An emission pathway for stabilization at 6 W/m2 radiative forcing. Climatic Change. 109, 59-76.
Matthew, R., Muehleisen, R. (2014). Guide to Bayesian calibration of building energy models. 2014 ASHRAE/IBPSA-USA building simulation conference, Atlanta, GA
McGill, G., Sharpe, T., Robertson, L., et al. 2017. Meta-analysis of indoor temperatures in new-build housing. Building Research & information. 19–39.
McGregor, G. R., Bessemoulin, P., Ebi, K., Menne, B. (2015). Heatwaves and health: guidance on warning‐system development. Technical Report. WMO and WHO. 96 page.
McLeod, R.S., Hopfe, C.J., Kwan, A. (2013). An investigation into future performance and overheating risks in Passivhaus dwellings. Building and Environment. 70, 189-209
Mehrotra, R., Sharma, A. (2016). A multivariate quantile-matching bias correction approach with auto and cross dependence across multiple time scales: implications for downscaling. Journal of Climate. 29, 3519–3539
Meteonorm. (2021). Handbook Part II: Theory, Version 8.0. Global Meteorological Database Version 8, Software and Data for Engineers, Planers and Education. The Meteorological Reference for Solar Energy Applications, Building Design, Heating & Cooling Systems, Education Rene. 2021. URL: https://meteonorm.com/assets/downloads/mn73_software.pdf (accessed January 24, 2022).
Michelangeli PA, Vrac M, Loukos H. (2009). Probabilistic downscaling approaches: application to wind cumulative distribution functions. Geophysical research letters. 36, 1–6.
Mitchell, M. (1996). An Introduction to Genetic Algorithms. Cambridge, MA: MIT Press
Mitchell, R., Natarajan, S. (2019). Overheating risk in Passivhaus dwellings. Building Services Engineering Research Technology. 40, 446–469.
Mitra, K. (2009). Multi-objective optimization of an industrial grinding operation under uncertainty. Chemical Engineering Science. 64, 5043-5056
Moazami, A., Carlucci, S., Geving, S. (2017). Critical analysis of software tools aimed at generating future weather files with a view to their use in building performance simulation. Energy Procedia. 132, 640-645.
Moazami, A., Nik, V.M., Carlucci, S., et al. (2019). Impacts of future weather data typology on building energy performance—investigating long-term patterns of climate change and extreme weather conditions. Apply Energy. 238, 696–720.
Mohamed, S., Rodrigues, L., Omer, S., et al. (2021). Overheating and indoor air quality in primary schools in the UK. Energy and Buildings. 250, 111291
Montazami, A., Nicole, F. (2013). Overheating in schools: comparing existing and new guidelines. Building Research and Information. 41:3, 317-329.
Montazami, A., Wilson, M., Nicol, F. (2012). Aircraft noise, overheating and poor air quality in classrooms in London primary schools. Building and Environment. 52, 129-141
Mora, C., Dousset, B., Caldwell, I.R., et al. (2017). Global risk of deadly heat. Nature Climate Chang. 7, 501–506
Morris, M.D. (1991). Factorial sampling plans for preliminary computational experiments. Technometrics. 33, 161-174
Mulville, M., Stravoravdis S. (2016). The impact of regulations on overheating risk in dwellings. Building Research and Information. 44, 520-534.
Mylona A. (2012). The use of UKCP09 to produce weather files for building simulation building services engineering research and technology. 33, 51-62
Nadel, S. (2020). Programs to Promote Zero-Energy New Homes and Buildings. American Council for an Energy-Efficient Economy (ACEEE). https://www.aceee.org/sites/default/files/pdfs/zeb_topic_brief_final_9-29-20.pdf
Nagpal, S., Mueller, C., Aijazi, A., et al. (2018). A methodology for auto-calibrating urban building energy models using surrogate modeling techniques. Building Performance Simulation. 1-16
Narisada, K.; Schreuder, D. (2004). Light Pollution Handbook: Volume 322; Springer Science+Business Media Dordrecht: Berlin, Germany.
NASA’s Goddard Institute for Space Studies (GISS). (2022) Global climate change. URL: https://data.giss.nasa.gov/gistemp/ (accessed January 24, 2022).
National Ocean and Atmospheric Administration (NAOO). (2020). Weather Related Fatality and Injury Statistics-80-Year List of Severe Weather Fatalities. URL: https://www.weather.gov/hazstat/(accessed January 24, 2022).
Natural Resources Canada (NRC). (2019). National Energy Use Database-Comprehensive Energy Use Database for commercial institutional Sector report. URL: http://oee.rncan-nrcan.gc.ca/corporate/statistics/neud/dpa/menus/trends/handbook/tables.cfm?wbdisable=true (accessed January 24, 2022).
Natural Resources Canada (NRC). (2020). Residential End-Use Model-Residential Housing Stock and Floor Space sector report. URL: http://oee.rncan-nrcan.gc.ca/corporate/statistics/neud/dpa/showTable.cfm?type=HB§or=res&juris=00&rn=11&page=0 (accessed January 24, 2022).
Natural Resources Canada (NRCan). (2011). Survey of Household Energy Use 2011. Detailed Statistical Report. Office of Energy Efficiency. Natural Resources Canada. Ottawa, Ontario.
Natural Resources Canada (NRCan). (2012). Survey of commercial and institutional energy use – buildings 2009. Detailed Statistical Report. Office of Energy Efficiency. Natural Resources Canada. Ottawa, Ontario.
Natural Resources Canada (NRCan). (2013). Energy efficiency trends in Canada 1990-2012. Office of Energy Efficiency. Natural Resources Canada. Ottawa, Ontario.
Natural Resources Canada (NRCan). (2014). Energy efficiency trends in Canada 1990-2013. Office of Energy Efficiency. Natural Resources Canada. Ottawa, Ontario.
Nelson, T.M.; Nilsson, T.H.; Johnson, M. (1984). Interaction of temperature, illuminance and apparent time on sedentary work fatigue. Ergonomics. 27, 89–101.
NFRC 201. (2020). Procedure for Interim Standard Test Method for Measuring the Solar Heat Gain Coefficient of Fenestration Systems Using Calorimetry Hot Box Methods. National Fenestration Rating Council. URL: https://www.nfrc.org/ (accessed January 24, 2022).
Nguyen, A., Rockwood, D., Doanc, M., et al. (2021). Performance assessment of contemporary energy-optimized office buildings under the impact of climate change. Building Engineering. 35, 102089
Nguyen, A.T., Reiter, S., Rigo, P. (2014). A review on simulation-based optimization methods applied to building performance analysis. Apply Energy. 113, 1043-1058
NOAA. (2016). Climate Modeling. Geophysical Fluid Dynamics Laboratory. URL: https://www.gfdl.noaa.gov/wp-content/uploads/files/model_development/climate_modeling.pdf (accessed January 24, 2022).
NOAA. (2019). Geophysical Fluid Dynamics Laboratory GFDL. Climate Modeling Report URL: https://www.gfdl.noaa.gov/wp-content/uploads/files/model_development/climate_modeling.pdf (accessed January 24, 2022).
North American Regional Climate Change Assessment Program NARCCAP. (2019). Download RCM Data. URL: http://www.narccap.ucar.edu/data/download.html (accessed January 24, 2022).
O’Neill, B.C., Tebaldi, C., Van Vuuren, D., et al. (2016). The scenario model inter-comparison project (ScenarioMIP) for CMIP6. Geoscientific Model Development. 9, 3461–3482
Oberti, F., Oberegger, U.F., Gasparella, A. (2015). Calibrating historic building energy models to hourly indoor air and surface temperatures: Methodology and case study. Energy and Buildings. 108, 236–243.
Occupational Health and Safety Council of Ontario (OHSCO). (2007). Heat stress awareness guide. URL: https://www.uwo.ca/biology/pdf/administration/heatstressguide.pdf (accessed January 24, 2022).
Ohnaka, T., Takeshita, J. (2005). Upper limit of thermal comfort zone in bedrooms for falling into a deep sleep as determined by body movements during sleep. Elsevier Ergonomics Book Series. 3, 121-126
Ouzeau, G., Soubeyroux, J.M., Schneider, M., et al. (2016). Heat waves analysis over France in present and future climate: Application of a new method on the EURO-CORDEX ensemble. Climate Services. 4, 1-12
Overbey, D. (2016). Standard Effective Temperature (SET) and Thermal Comfort. Building Enclosure. URL: https://www.buildingenclosureonline.com/blogs/14-the-be-blog/post/85635-standardeffective- temperature-set-and-thermal-comfort (accessed January 24, 2022).
Paliouras, P., Matzaflaras, N., Peuhkuri, R.H., et al. (2015). Using measured indoor environment parameters for calibration of building simulation model – a passive house case study. Energy Procedia. 78, 1227-1232
Pan, Y., Huang, Z., Wu, G. (2007). Calibrated building energy simulation and its application in a high-rise commercial building in Shanghai. Energy and Buildings. 39, 651–657.
Parekh, A. (2012a). Representative Housing Thermal Archetypes for Energy Analysis Models-Final report. CanmetENERGY Leadership in ecoInnovationm Natural Resources Canada.
Parekh, A., Chris, K. (2012b). Thermal and Mechanical Systems Descriptors for Simplified Energy Use Evaluation of Canadian Houses. Proceedings of SimBuild. 5, 279-286.
Passive House Guidelines. (2022). URL: https://passivehouse-international.org/index.php?page_id=80 (accessed January 24, 2022).
Passive House Institute. (2021). Windows components database. URL: https://database.passivehouse.com/en/components/list/window (accessed January 24, 2022).
Pathan, A., Mavrogianni, A., Summerfield, A., et al. (2017). Monitoring summer indoor overheating in the London housing stock. Energy and Buildings. 141, 361-378.
Paulos, J., Berardi, U. (2020). Optimizing the thermal performance of window frames through aerogel-enhancements. Applied Energy. 266, 114776
Pavlak, G.S., Florita, A.R., Henze, G.P., et al. (2013). Comparison of traditional and bayesian calibration techniques for gray-box modeling. Architectural Engineering. 20.
Peacock, A. D., Jenkins, D. P., Kane, D. (2010). Investigating the potential of overheating in UK dwellings as a consequence of extant climate change. Energy Policy. 38, 3277-3288
Penna, P., Cappelletti, F., Gasparella, A., et al. (2015a) A. Multi-Stage Calibration of the Simulation Model of a School Building Through Short-Term Monitoring. Information Technology in Construction. 20, 132–145.
Penna, P., Prada, A., Cappelletti, F., et al. (2015b). Multi-objectives optimization of Energy Efficiency Measures in existing buildings. Energy and Buildings. 95, 57-69
Pezeshki, Z. (2018). Energy Modeling for Estimation of Indoor Temperature of the Building on the BIM Platform. Thesis at Shahrood University of Technology, Shahrud, Iran.
Piani C, Haerter JO, Coppola E (2010) Statistical bias correction for daily precipitation in regional climate models over Europe. Theoretical and Applied Climatology. l 99, 187–192
Pickup, J., de Dear R. (2000). An Outdoor Thermal Comfort Index (OUT-SET) Part I- TheModel and its Assumptions. Sydney, NSW2109 Australia: Division of Environmental and Life Sciences, Macquarie University.
Pierson, C., Wienold, J., Bodart, M. (2018). Daylight Discomfort Glare Evaluation with Evalglare: Influence of Parameters and Methods on the Accuracy of Discomfort Glare Prediction. Buildings. 8(8), 94
Plokker, W., Evers, J.J., Struck, C., et al. (2009). First experiences using climate scenarios for The Netherlands in building performance simulation. 11th IBPSA Building Simulation Conference, International Building Performance Association, Glasgow, UK, 1284-1291
PR EN 16798-1. (2015). Indoor environmental input parameters for design and assessment of energy performance of buildings addressing indoor air quality, thermal environment, lighting and acoustics. Standard by British-Adopted European Standard.
Psomas, T., Heiselberg, P., Lyme, T., et al. (2017). Automated roof window control system to address overheating on renovated houses: Summertime assessment and intercomparison. Energy and Buildings. 138, 35-46
Public Health England. (2015). Heatwave Plan for England, Protecting Health and Reducing Harm from Severe Heat and Heatwaves; Public Health England and National Health Service: London, UK.
Public Health England. (2018). Heatwave Plan for England – protecting health and reducing harm from severe heat and heatwaves. 44 pages
Quigley, E.S., Lomas, K.J. (2018) Performance of medium-rise, thermally lightweight apartment buildings during a heat wave. Proceedings of 10th Windsor Conference: Rethinking Comfort, Windsor, London, UK. 32-47.
Rabitz, H. (2010). Global Sensitivity Analysis for Systems with Independent and/or Correlated Inputs. Procedia - Social and Behavioral Sciences, 2, 7587-7589.
Raftery, P., Keane, M., O’Donnell, J. (2011). Calibrating whole building energy models: An evidence-based methodology. Energy and Buildings. 43, 2356–2364.
Raftery, P., Keane, M., O'Donnell, J. (2011). Calibrating whole building energy models: An evidence-based methodology. Energy and Buildings, 43, 2356–2364.
Ramasoot, T.; Panyakeaw, S. (2015). Prescribing the illumination levels for Thailand: A Review of Approaches and Methodologies. BTEC, 2, 200–210
Ramos, G., Fernández, C., Gómez-Acebo, T., Sánchez-Ostiz, A. (2016). Genetic algorithm for building envelope calibration. Applied Energy. 168, 691–705.
Rasmussen, C.E., Williams, C.K.I. (2006). Gaussian processes for machine learning. Massachusetts Institute of Technology.
RdbQ. (2018). Construction Code and Safety Code - Régie du bâtiment du Québec. [accessed May 05, 2022]. <https://www.rbq.gouv.qc.ca/en/laws-regulations-and-codes/construction-code-and-safety-code.html>.
RBQ. (2018). Construction Code and Safety Code - Régie du bâtiment du Québec 2018. URL: https://www.rbq.gouv.qc.ca/en/laws-regulations-and-codes/construction-code-and-safety-code.html (accessed January 24, 2022).
Rechenberg, I. (1960) Evolutions strategies. Stuttgart: Frommann-Holzboog
Reddy, T.A. (2006). Literature Review on Calibration of Building Energy Simulation Programs: Uses, Problems, Procedures, Uncertainty, and Tools. ASHRAE Transactions. Atlanta 112, 226-240
Riahi, K., Krey, V., Rao, S., et al. (2011). RCP-8.5: exploring the consequence of high emission trajectories. Climatic Change. 109, 80-98
Roberti, F., Oberegger, U., Gasparell, A. (2015). Calibrating historic building energy models to hourly indoor air and surface temperatures: Methodology and case study. Energy and Buildings. 108, 236-243.
Robine, J. M., Cheung, S. L., Roy, S. L., et al. (2007). Report on Excess Mortality in Europe During Summer 2003; European Commission, Directorate General for Health and Consumer Protection: Brussels, Belgium.
Rocha, R. (2018). Montreal Building Age Story Map. URL: https://www.cbc.ca/news2/interactives/montreal-375-buildings/ (accessed January 24, 2022).
Royapoor, M., Roskilly, T. (2015). Building model calibration using energy and environmental data. Energy and Buildings. 94, 109-120
Saltelli, A., Ratto, M., Andres, T., et al. (2008). Global sensitivity analysis: the primer John Wiley & Sons. 285 pages
Sameni, S.M., Gaterell, M., Montazami, A., et al. (2015). Overheating investigation in UK. social housing flats built to the Passivhaus standard. Building and Environment. 92, 222-235
Samuelson, H.W., Ghorayshi, A., Reinhart, F. (2016). Analysis of a simplified calibration procedure for 18 design-phase building energy models. Building Performance Simulation. 9, 17–29.
Sbar, N., Podbelski, L., Yang, H., et al. (2012). Electrochromic dynamic windows for office buildings. International Journal of Sustainable Built Environment. 1, 125-139.
Schaffer, J.D. (1985). Multiple Objective Optimization with Vector Evaluated Genetic Algorithms. The 1st International Conference on Genetic Algorithms. 93-100.
Schweiker, M., J. Kolarik, M. Dovjak, and M. Shukuya. (2016). Unsteady-state Human-Body Exergy Consumption Rate and its Relation to Subjective Assessment of Dynamic Thermal Environments. Energy and Buildings. 116, 164–180.
Seaby, L.P., Refsgaard, J.C., Sonnenborg, T.O., et al. (2013). Assessment of robustness and significance of climate change signals for an ensemble of distribution-based scaled climate projections. Hydrolog. 486, 479-493.
Sepúlveda, A., De Luca, F., Thalfeldt, M., et al. (2020). Analyzing the fulfillment of daylight and overheating requirements in residential and office buildings in Estonia. Building and Environment.180, 107036
Shen, P., Braham, W., Yi, Y., et al. (2019). Rapid multi-objective optimization with multi-year future weather condition and decision-making support for building retrofit. Energy. 172, 892-912
Shen, H., Tzempelikos, A. (2012). Sensitivity analysis on daylighting and energy performance of perimeter offices with automated shading. Building and Environment. 59, 303-314
Simson, R., Kurnitski, J., Maivel, M. (2017). Summer thermal comfort: Compliance assessment and overheating prevention in new apartment building in Estonia. Building Performance Simulation. 10, 378-391.
Sobol, I. M. (1967). Distribution of points in a cube and approximate evaluation of integrals. USSR Computational Mathematics and Mathematical Physics. 7, 86–112
Sobol, I. M. (2001). Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Mathematics and Computers in Simulation. 55, 271-280
Solman, S.A.; Sanchez, E.; Samuelsson, P. et al. Evaluation of an ensemble of regional climate model simulations over South America driven by the ERA-Interim reanalysis: Model performance and uncertainties. Climate Dynamics. 2013. 41, 1139–1157.
Statistic Canada. (2021b). Demographic estimates by age and sex, provinces and territories. URL: https://www150.statcan.gc.ca/n1/pub/71-607-x/71-607-x2020018-eng.htm (accessed January 24, 2022).
Statistics Canada. (2013). Canadian Survey on Disability, 2012: Concepts and Methods Guide.
Statistics Canada. (2017). Census in Brief Dwellings in Canada.. URL: https://www12.statcan.gc.ca/census-recensement/2016/as-sa/98-200-x/2016005/98-200-x2016005-eng.pdf (accessed January 24, 2022).
Statistics Canada. (2019). Census in Brief Dwellings in Canada. URL: https://www12.statcan.gc.ca/census-recensement/2016/as-sa/98-200-x/2016005/98-200-x2016005-eng.pdf (accessed January 24, 2022).
Statistics Canada. (2021a). Air conditioners. URL: https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=3810001901 (accessed January 24, 2022).
Stazi, F., Tomassoni, E., Perna, C. (2017). Super-insulated wooden envelopes in Mediterranean climate: Summer overheating, thermal comfort optimization, environmental impact on an Italian case study. Energy and Buildings. 138, 716-732
Sarbu, I., Pacurar, C. (2015). Experimental and numerical research to assess indoor environment quality and schoolwork performance in university classrooms. Building and Environment. 93, 141-154
Tabadkani, A., Roetzel, A., Li, H.X., et al. (2021). Design approaches and typologies of adaptive facades: a review. Automation in Construction. 121, 103450
Taylor, J., Davies, M., Mavrogianni, A., et al. (2014). The relative importance of input weather data for indoor overheating risk assessment in dwellings. Building and Environment. 76, 81-91
Thomson, A., Calvin, K., Smith, S., et al. (2011). RCP4.5: a pathway for stabilization of radiative forcing by 2100. Climatic Change. 109, 77-94
The Weather Network . (2020). 36.6 ° C in Montreal: the 2nd hottest brand in history. https://www.meteomedia.com/ke/nouvelles/article/record-de-chaleur-battu-a-montreal-mai-2020 (accessed January 24, 2022).
Tian, W. (2013). A review of sensitivity analysis methods in building energy analysis. Renewable and Sustainable Energy Reviews. 20, 411-419
Tian, W., Wilde, P. Li, Z. et al. (2018). Uncertainty and sensitivity analysis of energy assessment for office buildings based on Dempster-Shafer theory. Energy Conversion and Management. 174, 705-718
Tian, W., Yang, S., Li, Z., et al. (2016). Identifying informative energy data in Bayesian calibration of building energy models. Energy and Buildings. 119. 363-376,
Tink, V., Porritt, S., Allinson, D., Loveday, D. (2018). Measuring and mitigating overheating risk in solid wall dwellings retrofitted with internal wall insulation. Building and Environment. 14, 247-261
Touchie, M., Tzekova, E., Jeffery, S., et al. (2016). Evaluate Summertime Overheating in Multi-Unit Residential Buildings Using Surveys and In-Suite Monitoring. Thermal Performance of the Exterior Envelopes of Whole Buildings XIII International Conference, ASHRAE, 135-151.
Troup, L., Fannon, D. (2016). Morphing Climate Data to Simulate Building Energy Consumption. ASHRAE and IBPSA-USA SimBuild 2016. Building Performance Modeling Conference, Salt Lake City, UT, August 8-12.
Uguen-Csenge, E., Lindsay, B. (2021). For 3rd straight day, B.C. village smashes record for highest Canadian temperature at 49.6 C. CBC News
U.S. Department of Energyr. (1997). IPMVP International Performance Measurement and Verification Protocol.
U.S. Energy Information Administration (EIA). (2016). Commercial Buildings Energy Consumption Survey (CBECS)-Table B6. Building size, number of buildings 2012.
UN-Environment. (2017). Towards a zero-emission, efficient, and resilient buildings and construction sector-global status report 2017. 45 pages
United Nations Educational, Scientific and Cultural Organization UNESCO. (2017). More Than One-Half of Children and Adolescents Are Not Learning Worldwide. Fact sheet No.6.
Uribe, D., Vera, S., Bustamante, W., et al. (2019). Impact of different control strategies of perforated curved louvers on the visual comfort and energy consumption of office buildings in different climates. Solar Energy. 190, 495-510.
Valeria, DG, Osvaldo, DP, Michele, DC. (2012). Indoor environmental quality and pupil perception in Italian primary schools. Building and Environment. 56: 335–345.
Valitabar, M., Moghimi, M., Mahdavinejad, M., et al. (2018). Design optimal responsive facade based on visual comfort and energy performance. 23rd International Conference on Computer-Aided Architectural Design Research in Asia: Learning, Prototyping and Adapting, CAADRIA, 2, Beijing, China. 93-102
Vanhoutteghem, L., Skarning, G.C.J., Hviid, C.A.,et al. (2015). Impact of Façade Window Design on Energy, Daylighting and Thermal Comfort in Nearly Zero-Energy Houses. Energy and Buildings. 102,149-156.
Vikhar, P. A. (2016). Evolutionary algorithms: A critical review and its future prospects. Proceedings of the 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC). 261–265
Ville de Montréal. (2017). Climate Change Adaptation Plan for The Montreal Urban Agglomeration 2015-2020. 245 pages.
Virk, G., Jansz, A., Mavrogianni, A., et al. (2014) The effectiveness of retrofitted green and cool roofs at reducing overheating in a naturally ventilated office in London: Direct and indirect effects in current and future climates. 23,504-520
Vrac, M., Friederichs, P. (2015) Multivariate-intervariable, spatial, and temporal-bias correction. Journal of Climate. 28, 218–237
Vuuren, D., Edmonds, J., Kainuma, M., et al. (2011a). The representative concentration pathways: an overview. Climatic Change. 109, 22-50
Vuuren, D., Stehfest, E., Elzen, M., et al. (2011). RCP2.6: exploring the possibility to keep global mean temperature increase below 2 °C. Climatic Change. 109, 95-116.
Wang, L., Liu, X., Brown, H. (2017). Prediction of the impacts of climate change on energy consumption for a medium-size office building with two climate models. Energy and Buildings. 175, 218-226
Ward, R. (1925). The Climates of the United States. Creative Media Partners publisher. 544 page.
Wargocki, P. Wyon, P. (2013). Providing better thermal and air quality conditions in school classrooms would be cost- effective. Building and Environment. 59, 581-589.
Wargocki, P., Wyon, P. (2017). Ten questions concerning thermal and indoor air quality effects on the performance of office work and schoolwork. Building and Environment. 112, 359- 366.
Watts, N., Amann, M., Arnell, N., et al. (2018). The 2018 Report of the Lancet Countdown on health and climate change: Shaping the Health of Nations for Centuries to Come. Lancet. 392. 2479–2514
WCRP (2014). WCRP Coupled Model Inter-comparison Project (CMIP). URL: https://www.wcrp-climate.org/wgcm-cmip (accessed January 24, 2022).
WCRP. (2021). CMIP6-endorsed MIPs. URL: https://www.wcrp-climate.org/modelling-wgcm-mip-catalogue/modelling-wgcm-cmip6-endorsed-mips (accessed January 24, 2021).
Weedon, G.P., Balsamo, G., Bellouin, N., et al. (2014). The WFDEI meteorological forcing data set: WATCH forcing data methodology applied to ERA-interim reanalysis data. Water Resources Researcher. 50, 505-7514. URL: https://cds.climate.copernicus.eu/#!/search?text=era5 (accessed January 24, 2021).
Westphal, F.S., Lamberts, R. (2005). Building Simulation Calibration Using Sensitivity Analysis. In Proceedings of the Ninth International IBPSA Conference, Montréal, QC, Canada. 1331–1338
White, L., Wright, G. (2019). Assessing resiliency and passive survivability in multifamily buildings. Thermal performance of the exterior envelopes of whole buildings XIV international conference. 123-134
White-Newsome, J. L., Sanchez, B. N., Jolliet, O., et al. (2012). Climate change and health: indoor heat exposure in vulnerable populations. Environmental Research. 112, 20-27
Wilcke RAI, Mendlik T, Gobiet A. (2013) Multi-variable error correction of regional climate models. Climate Change. 120, 871–887.
Wilson, A. (2015). LEED Pilot Credits on Resilient Design Adopted. U.S. Green Building Council (USGBC). http://www.resilientdesign.org/leed-pilot-credits-on-resilient-design-adopted/ (accessed January 24, 2021).
Winkler, J., Horowitz, S., DeGraw, J., Merket, N. (2016). Evaluating EnergyPlus Airflow Network Model for Residential Ducts, Infiltration, and Interzonal Airflow. NREL National renewable energy laboratory. PR-550-70230
Woodward, P. (2011). Bayesian analysis made simple: an excel GUI for WinBUGS. Taylor & Francis
Xu, C., Gertner, G. (2011). Understanding and comparisons of different sampling approaches for the Fourier Amplitudes Sensitivity Test (FAST). Computational statistics & data analysis. 55, 184-198.55 184-198
Yang, M.D., Lin, M.D., Lin, Y.H., et al. (2017). Multi-objective optimization design of green building envelope material using a non-dominated sorting genetic algorithm. Applied Thermal Engineering. 111, 1255-1264
Yang, S., Fiorito, F., Prasad, D., et al. ( 2021). A sensitivity analysis of design parameters of BIPV/T-DSF in relation to building energy and thermal comfort performances. Building Engineering. 41, 10242
Yang, T., Pan, Y., Mao, J., et al. (2016). An automated optimization method for calibrating building energy simulation models with measured data: Orientation and a case study. Apply Energy. 179, 1220–1231.
Zero Carbon Hub. (2015). Overheating in Homes, the Big Picture. Full Report91 pages; Zero Carbon Hub: London, UK.
Zeferina, V., Wood, F., Edwards, R. et al. (2021). Sensitivity analysis of cooling demand applied to a large office building. Energy and Buildings. 235, 110703
Zhai, Y.N., Wang, Y., Huang, Y.Q., et al. (2019). A multi-objective optimization methodology for window design considering energy consumption, thermal environment and visual performance. Renewable Energy. 134, 1190-1199
Zhai, Z., Johnson, M.H., Krarti, M. (2011). Assessment of natural and hybrid ventilation models in whole-building energy simulations. Energy and Buildings. 43, 2251-2261.
Zhang, A., Bokel, R., Dobbelsteen, A., et al. (2017). Optimization of thermal and daylight performance of school buildings based on a multi-objective genetic algorithm in the cold climate of China. Energy and Buildings. 139, 371-384
Zhang, X., Flato, G., Kirchmeier-Young, M., et al. (2019). Changes in Temperature and Precipitation Across Canada. In Canada’s Changing Climate Report; Bush, E., Lemmen, D.S., Eds.; Government of Canada: Ottawa, ON, Canada. 112–193.
Zhang, Y. (2012). Use jEPlus as an Efficient Building Design Optimisation Tool. CIBSE ASHRAE Technical Symposium, Imperial College, London UK. URL: https://www.jeplus.org/wiki/doku.php?id=docs:jeplus_ea:start (accessed January 24, 2021).
Zhang, Y., Korolija, I. (2015). jEPlus - An EnergyPlus simulation manager for parametric. URL: http://www.jeplus.org/wiki/doku.php (accessed January 24, 2021).
Zhang, Y., Korolija, I. (2020). jEPlus+EA. URL: http://www.jeplus.org/wiki/doku.php?id=start (accessed January 24, 2021).
Repository Staff Only: item control page