Methodology for Preliminary Design of Buildings Using Multi-Objective Optimization Based on Performance Simulation.

Area of Science:

• Building Science and Architecture
• Computational Engineering
• Sustainable Design

Background:

• Buildings significantly impact global energy consumption and environmental footprint.
• Architectural design, particularly the building envelope, is crucial for energy efficiency but involves complex multi-objective challenges.
• Optimization approaches are lacking for the critical schematic design stage.

Purpose of the Study:

• To present a multi-objective optimization model for schematic building design.
• To assist designers in achieving optimal solutions balancing cost and energy efficiency.
• To integrate evolutionary algorithms with building energy simulation for design support.

Main Methods:

• Utilized Pareto Archived Evolutionary Strategies (PAES) algorithm coupled with the EnergyPlus simulator.
• Employed a binary array-based search process for evolving building design components.
• Evaluated solutions based on constructive cost and energy efficiency.

Main Results:

• Optimal solutions achieved an average of 50% energy savings compared to typical consumption.
• Demonstrated a 50% reduction in operating CO2 emissions.
• Showcased an investment return of less than 3 years across four different weather conditions.

Conclusions:

• The developed optimization model effectively supports schematic building design.
• The methodology proves valuable in identifying cost-effective, energy-efficient building designs.
• The tool aids in achieving substantial energy savings and environmental benefits in buildings.