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This study conducts a detailed analysis to improve to enhance the energy performance of residential buildings in UAE through various retrofit measures. The applied methodology involved developing a calibrated building energy model for a two-story residential building, followed by a parametric analysis of six design variables, including wall and roof insulation, glazing, infiltration rate, window shading, and setpoint and setback temperatures to evaluate their impact on annual energy consumption. Additionally, a sensitivity analysis was conducted to assess the importance of the investigated design variables on building energy use. An optimization approach using the non-dominated sorting genetic algorithm (NSGA-II) was then implemented to optimize energy consumption while minimizing discomfort conditions. The key findings from the parametric simulations show significant energy savings: a 38.8 % reduction from improved wall insulation (achieving a U-value of 0.14 W/m2K), a 2.3 % decrease with better roof insulation, a 9.8 % saving from using triple clear glass glazing, a 9.6 % reduction by lowering the infiltration rate to 2.5 m³/h.m2, 7.5 % savings from window shading, and a 25.7 % decrease by optimizing cooling setpoints. A sensitivity analysis highlighted the dominant impact of wall insulation and cooling setpoint temperatures on energy usage. Followed by the cooling setpoint temperature. The subsequent NSGA-II optimization yielded 106 Pareto optimal solutions from 1897 iterations, offering a balance between reducing energy consumption (10,942 to 20,250 kWh/year, averaging 60 % savings) and minimizing discomfort hours (296–1230 h). These results provide actionable insights for stakeholders in the retrofitting process, emphasizing the significant energy-saving potential of specific retrofit measures.
Abdeen et al. (Thu,) studied this question.