article · Results in Engineering
Energy consumption in residential buildings can be substantially lowered through targeted retrofit interventions. Using a calibrated energy model of a two-storey residential building in the United Arab Emirates, the research evaluated six retrofit variables: wall insulation, roof insulation, glazing, air infiltration rates, window shading, and indoor cooling temperature setpoints. Parametric and sensitivity assessments established that wall insulation and cooling setpoints exert the greatest influence on overall building energy demand. Improved wall insulation achieved a 38.8 percent reduction, while cooling setpoint adjustments yielded a 25.7 percent decrease. By applying a multi-objective genetic algorithm (NSGA-II), the evaluation generated 106 Pareto optimal configurations. These solutions achieved average annual energy savings of 60 percent, corresponding to between 10,942 and 20,250 kilowatt-hours per year, whilst maintaining low annual thermal discomfort durations between 296 and 1,230 hours.
Cooling buildings in hot climates requires immense amounts of electrical power, driving up utility bills and carbon emissions. This research illustrates how existing residential properties can be retrofitted effectively without compromising occupant comfort. By identifying which physical upgrades and operational settings deliver the highest energy reductions, decision-makers can prioritise investments that yield the greatest efficiency gains and lower running costs.
The findings can guide retrofit contractors, building energy auditors, and municipal planners seeking to prioritise renovation measures for residential housing stock in arid regions. Because the results derive from simulation-based modelling on a single calibrated structure, the work sits at an applied, pre-commercial research stage. Transitioning to broader deployment requires validating the optimised retrofit packages across physical field trials and developing standardised decision-support toolkits for real estate developers.
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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.
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DOI: 10.1016/j.rineng.2024.101815
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