Prescriptive tool for zero-emissions building fenestration design using hybrid metaheuristic algorithms

2025-11-26

AI Summary

Addressing the challenge of multi-objective (heating/cooling load, thermal discomfort) co-optimization across 19 facade design parameters for zero-emission buildings (ZEBs), this paper proposes an automated optimization framework integrating building energy simulation with a hybrid metaheuristic algorithm. Innovatively, it couples a parametric, updatable component library with dynamic design rules to jointly optimize façade opening configurations, glazing optical properties, and the geometry and control strategies of operable shading systems. Validated across three distinct climate zones in Spain, the method significantly reduces total energy demand and thermal discomfort compared to conventional genetic algorithms, while yielding a more diverse and robust Pareto-optimal solution set. Notably, it identifies critical bottlenecks in cooling-load minimization under warm-humid conditions—providing a directly implementable, high-fidelity technical pathway for ZEB façade design.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Designing Zero-Emissions Buildings (ZEBs) involves balancing numerous complex objectives that traditional methods struggle to address. Fenestration, encompassing façade openings and shading systems, plays a critical role in ZEB performance due to its high thermal transmittance and solar radiation admission. This paper presents a novel simulation-based optimization method for fenestration designed for practical application. It uses a hybrid metaheuristic algorithm and relies on rules and an updatable catalog, to fully automate the design process, create a highly diverse search space, minimize biases, and generate detailed solutions ready for architectural prescription. Nineteen fenestration variables, over which architects have design flexibility, were optimized to reduce heating, cooling demand, and thermal discomfort in residential buildings. The method was tested across three Spanish climate zones. Results demonstrate that the considered optimization algorithm significantly outperforms the baseline Genetic Algorithm in both quality and robustness, with these differences proven to be statistically significant. Furthermore, the findings offer valuable insights for ZEB design, highlighting challenges in reducing cooling demand in warm climates, and showcasing the superior efficiency of automated movable shading systems compared to fixed solutions.

Problem

• Optimizes fenestration design for zero-emissions buildings
• Automates design to reduce heating, cooling, and discomfort
• Tests method across climates to improve algorithm performance

Innovation

• Hybrid metaheuristic algorithm optimizes fenestration variables automatically
• Simulation-based method integrates updatable catalog and design rules
• Automated movable shading systems outperform fixed solutions in warm climates