Multi-Objective Optimization of Building Envelopes in Hot Arid Climates: A Pareto Front Analysis Approach | ||||
| Aswan University Journal of Sciences and Technology | ||||
| Volume 5, Issue 2, June 2025, Page 61-80 PDF (1.51 MB) | ||||
| Document Type: Original papers | ||||
| DOI: 10.21608/aujst.2025.356231.1171 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Nada Tarek1; Asmaa Omar2; Ayman Ragab   3
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| 1Aswan-Faculty of Engineering | ||||
| 2Department of Architectural Engineering, Faculty of Engineering, Aswan University, Aswan, Egypt | ||||
| 3Department of Architecture Engineering, Faculty of Engineering, Aswan University, Aswan, Egypt | ||||
| Abstract | ||||
| This study presents a methodology for optimizing the envelope of a multi-story residential building in hot arid regions, focusing on multi-objective optimization of cooling energy consumption, CO2 emissions, and operative temperature. The optimization variables include building proportions, window-to-wall ratio (WWR), glazing types, and roof surface albedo. Cooling energy requirements were calculated using Design Builder, based on key assumptions such as local climatic conditions, building orientation, material properties, occupancy patterns, and HVAC system specifications. The optimization process employed the Non-dominated Sorting Algorithm (NDS) to identify optimal solutions. The multi-objective optimization aimed to minimize cooling energy consumption, and CO2 emissions, and maintain operative temperature in an acceptable range simultaneously. The results indicate that no solutions achieve lower cooling energy consumption without compromising other objectives. However, prioritizing the minimization of cooling energy suggests optimal values of building proportion at 0.5, WWR at 10%, single clear glass with a thickness of 0.3, and roof surface albedo at 0.1. This method helps decision-makers choose optimal solutions based on their priorities. This study not only provides theoretical insights but also delivers actionable guidelines for architects and engineers. By leveraging the Pareto front methodology, decision-makers can address the specific challenges of designing energy-efficient buildings in hot arid climates, where cooling energy demands are exceptionally high. This work underscores the applicability of advanced optimization techniques in achieving sustainability goals in rapidly urbanizing regions. | ||||
| Keywords | ||||
| Pareto front; Non-dominated Sorting Algorithm; Cooling energy; Operative temperature; CO2 emissions | ||||
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