Abedini, Samineh2024-08-072024-08-072024-08-07http://hdl.handle.net/10393/46442https://doi.org/10.20381/ruor-30468Climate change is the most severe global sustainability issue our planet faces today, and the construction industry plays a significant role in the increasing demand for global energy. Consequently, prioritizing enhanced energy efficiency in existing buildings is a critical strategy for addressing these challenges. One way of achieving this involves simulating older buildings and evaluating their energy use for thermal optimization, which are essential tasks. Deep retrofitting is necessary to manage climate and energy consumption. The challenge lies in selecting the most effective energy retrofitting strategy for a specific building, given the numerous possible combinations of retrofit measures and conflicting goals. The unique weather conditions in Canada further complicate building energy retrofit challenges. In this study, the focus was on optimizing the thermal performance of the Montpetit Building on the University of Ottawa campus. This thesis aims to create a model that reduces energy usage and increases economic returns using a multi-objective optimization technique based on simulation. The simulation was first run using SketchUp software with the OpenStudio plugin to build a simulation-based multi-objective optimization framework. The results were then exported to EnergyPlus software. This framework combines the NSGA-II algorithm in MATLAB® as an optimization engine with EnergyPlus as a dynamic energy simulator, aiming to achieve optimal reductions in energy consumption and associated costs. The algorithm explores various solutions for the building envelope, including insulation and windows. The final solutions were chosen based on their respective Pareto fronts, considering cost-optimality and energy efficiency. Notably, the results show 50 fixed ideal answers by the outcome. By optimizing the building's envelope, a reduction of approximately 10% in total primary energy consumption is expected for one of the solutions with the least energy consumption. Furthermore, adjusting the cooling setpoint from 22°C to 25°C and modifying the heating setback by 2°C presents a potential reduction ranging from 10% to 30%. Specifically, during January, February, October, November, and December, optimization efforts may yield a reduction of up to 24% before adjusting the setpoint and setback and up to 48% following these adjustments. Additionally, implementing the aforementioned setpoint and setback changes on extreme winter days could result in a 20% decrease in total energy consumption; even on extreme summer days, a reduction of 27% is achievable. The second solution which is examined has the least cost. There is a notable reduction, especially in January, October, November, and December, with savings of up to 12% after optimization. with the changes in setpoints and setbacks to the mentioned values, the optimization increased to 42% savings in October. Also, the total energy efficiency increases by 90% after retrofitting and adjusting new setpoints and setbacks during extreme winter days and extreme summer days. Additionally, The sum weight method was used to select the third option. In this case, after optimization, we have a significant reductions of energy consumption, especially in January, October, November, and December which is up to 22%. By adjusting new setpoints and setbacks to the mentioned values, energy efficiency increased to 47% in October, 31% in September, and 43% in May, and after retrofitting and adjusting new setpoints and setbacks, total energy efficiency increased by 90% and 92% during extreme winter and summer days respectively. Moreover, the two-dimensional Pareto front demonstrates the inverse link between NPV and total energy usage.enAttribution-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nd/4.0/OptimizationThesis