abstract: Thermal extremes are responsible for more than 90% of all weather-related deaths in the United States, with heat alone accounting for an annual death toll of 618. With the combination of global warming and urban expansion, cities are becoming hotter and the threat to the well-being of citizens in urban areas is growing. Because people in modern societies (and in particular, vulnerable groups such as the elderly) spend most of their time inside their home, indoor exposure to heat is the underlying cause in a considerable fraction of heat-related morbidity and mortality. Notably, this can be observed in many US cities despite the high prevalence of mechanical air conditioning in the building stock. Therefore, part of the effort to reducing the overall vulnerability of urban populations to heat needs to be dedicated to understanding indoor exposure, its underlying behavioral and physical mechanisms, health outcomes, and possible mitigation strategies. This dissertation is an effort to advance the knowledge in these areas. The cities of Houston, TX, Phoenix, AZ, and Los Angeles, CA, are used as test beds to assess exposure and vulnerability to indoor heat among people 65 and older. Measurements and validated whole-building simulations were used in conjunction with heat-vulnerability surveys and epidemiological modelling (of collaborators) to (1) understand how building characteristics and practices govern indoor exposure to heat among the elderly; (2) evaluate mechanical air conditioning as a reliable protective factor against indoor exposure to heat; and (3) identify potential impacts from the evolving building stock and a warming urban climate. The results show strong associations between indoor heat exposure and certain health outcomes and highlight the vulnerability of elderly populations to heat despite the prevalence of air conditioning systems. Given the current construction practices and urban warming trends, this vulnerability will continue to grow. Therefore, policies promoting climate adaptive buildings features, as well as better access to reliable and affordable AC are needed. In addition, this research draws attention to the significant potential health consequences of large-scale power outages and proposes the implementation of passive survivability in regulations as one important preventative action. / Dissertation/Thesis / Doctoral Dissertation Engineering 2019

Changes in weather patterns worldwide and global warming increased the demand for high-performance buildings resilient to climate change. Building Performance Simulation (BPS) is a robust technique to test, assess, and enhance energy efficiency measures and comply with stringent energy codes of buildings. Climate has a considerable impact on the buildings' thermal environment and energy performance; therefore, choosing reliable and accurate weather data is crucial for building performance evaluation and reducing the performance gap. Typical Weather Years (TWYs) have been traditionally used for energy simulation of buildings. Even if detailed energy assessments can be performed using available multi-year weather data, most simulations are carried out using a typical single year. As a result, this fictitious year must accurately estimate the typical multi-year conditions. TWYs are widely used because they accelerate the modeling process and cut down on computation time while generating relatively accurate long-term predictions of building energy performance. However, there is no certainty that a single year can describe the changing climate and year-by-year variations in weather patterns. Nowadays, with increased computational power and higher speeds in calculation processes, it is possible to adopt multi-year weather datasets to fully assess long-term building energy performance and avoid errors and inaccuracies during the preliminary selection procedures. This study aims to investigate the impact of Typical Weather Years and Actual Weather Years (AWYs) on a single-family house and a university building under two opposite climates, Winnipeg (cold) and Catania (hot). First, a single-family house in Winnipeg, Canada, was selected to evaluate how typical weather years affect the energy performance of the building and compare it with AWYs simulation. Two widely used typical weather data, CWEC and TMY, were selected for the simulation. The results were compared with the outcomes of simulation using AWYs derived from the same weather station from 2015 to 2019, which covered the latest climate changes. The results showed that typical weather years could not sufficiently capture the year-by-year variation in weather patterns. The typical weather years overestimated the cooling load while underestimating the heating demands compared to the last five actual weather years. A more extensive study was conducted for more confidence in the findings and understanding of the weather files. The research was expanded by comparing the results of building performance simulation of the single-family house and an institutional building with more complex envelope characteristics belonging to the University of Manitoba under cold (Winnipeg, Canada) and hot (Catania, Italy) climates. Overall, 48 simulations were performed using ten actual weather years from 2010 to 2019 and two TWYs from each climate for both buildings. The results showed that while the TWYs either overestimate or underestimate the cooling and heating demands of both buildings, cooling load predictions were highly overestimated in the heating-dominant climate of Winnipeg, ranging from 10.5% to 82.4% for both buildings by CWEC and TMY weather data. In the cooling-dominant climate of Catania, energy simulations using IWEC and TMY typical weather data highly overestimated the heating loads between 2.8% and 82.4%.

The work presented in this thesis focuses on reducing the energy demand of a residential building by using a coupled ground-source heat pump/energy recovery ventilation (GSHP-ERV) system to present a novel approach to space condition and domestic hot water supply for a residence. The proposed system is capable of providing hot water on-demand with a high coefficient of performance (COP), thus eliminating the need for a hot water storage tank and circulation system while requiring little power consumption. The necessary size of the proposed system and the maximum and normal heating and cooling loads for the home were calculated based on the assumptions of an energy efficient home, the assumed construction specifications, and the climate characteristics of the Blacksburg, Virginia region. The results from the load analysis were used to predict energy consumption and costs associated with annual operations.The results for the predicted heating annual energy consumption and costs for the GSHP-ERV system were compared to an air-source heat pump and a natural gas furnace. On average, it was determined that the proposed system was capable of reducing annual energy consumption by 56-78% over air-source heat pumps and 85-88% over a natural gas furnace. The proposed GSHP-ERV system reduced costs by 45-61% over air-source heat pump systems and 52-58% over natural gas furnaces. The annual energy consumption and costs associated with cooling were not calculated as cooling accounts for a negligible portion (6%) of the total annual energy demand for a home in Blacksburg. / Master of Science

In order to achieve global carbon reduction targets, buildings must be designed to be energy efficient. Building performance simulation methods, together with sensitivity analysis and evolutionary optimization methods, can be used to generate design solution and performance information that can be used in identifying energy and cost efficient design solutions. Sensitivity analysis is used to identify the design variables that have the greatest impacts on the design objectives and constraints. Multi-objective evolutionary optimization is used to find a Pareto set of design solutions that optimize the conflicting design objectives while satisfying the design constraints; building design being an inherently multi-objective process. For instance, there is commonly a desire to minimise both the building energy demand and capital cost while maintaining thermal comfort. Sensitivity analysis has previously been coupled with a model-based optimization in order to reduce the computational effort of running a robust optimization and in order to provide an insight into the solution sensitivities in the neighbourhood of each optimum solution. However, there has been little research conducted to explore the extent to which the solutions found from a building design optimization can be used for a global or local sensitivity analysis, or the extent to which the local sensitivities differ from the global sensitivities. It has also been common for the sensitivity analysis to be conducted using continuous variables, whereas building optimization problems are more typically formulated using a mixture of discretized-continuous variables (with physical meaning) and categorical variables (without physical meaning). This thesis investigates three main questions; the form of global sensitivity analysis most appropriate for use with problems having mixed discretised-continuous and categorical variables; the extent to which samples taken from an optimization run can be used in a global sensitivity analysis, the optimization process causing these solutions to be biased; and the extent to which global and local sensitivities are different. The experiments conducted in this research are based on the mid-floor of a commercial office building having 5 zones, and which is located in Birmingham, UK. The optimization and sensitivity analysis problems are formulated with 16 design variables, including orientation, heating and cooling setpoints, window-to-wall ratios, start and stop time, and construction types. The design objectives are the minimisation of both energy demand and capital cost, with solution infeasibility being a function of occupant thermal comfort. It is concluded that a robust global sensitivity analysis can be achieved using stepwise regression with the use of bidirectional elimination, rank transformation of the variables and BIC (Bayesian information criterion). It is concluded that, when the optimization is based on a genetic algorithm, that solutions taken from the start of the optimization process can be reliably used in a global sensitivity analysis, and therefore, there is no need to generate a separate set of random samples for use in the sensitivity analysis. The extent to which the convergence of the variables during the optimization can be used as a proxy for the variable sensitivities has also been investigated. It is concluded that it is not possible to identify the relative importance of variables through the optimization, even though the most important variable exhibited fast and stable convergence. Finally, it is concluded that differences exist in the variable rankings resulting from the global and local sensitivity methods, although the top-ranked solutions from each approach tend to be the same. It also concluded that the sensitivity of the objectives and constraints to all variables is obtainable through a local sensitivity analysis, but that a global sensitivity analysis is only likely to identify the most important variables. The repeatability of these conclusions has been investigated and confirmed by applying the methods to the example design problem with the building being located in four different climates (Birmingham, UK; San Francisco, US; and Chicago, US).