Occupancy-Driven Optimization of Building Operations: Balancing Energy, Thermal Comfort, and Indoor Air Quality

Abstract

Energy efficiency, thermal comfort, and indoor air quality act synergistically as fundamental determinants of sustainability in the built environment. Conventional approaches often address these objectives in isolation, limiting opportunities for balanced performance. Advances in optimization techniques enable systematic trade-offs across competing objectives; however, in multi-occupancy spaces, where challenges of energy use, thermal comfort, and indoor air quality are most pronounced, the influence of occupant-related factors remains underexplored, with research largely focused on system-related variables. This study addresses this gap by systematically investigating the efficacy of multi-objective optimization strategies in enhancing building performance, while incorporating occupancy and operational variables. The research evaluates how variations in occupancy density influence energy consumption, thermal comfort, and indoor air quality in multi-occupancy shared working environments.

Building on a rigorous systematic literature review, a scenario-based framework was developed that integrates global sensitivity analysis with multi-objective optimization. Multi-method sensitivity analysis, using standardized regression coefficients, partial correlation coefficients, and Morris elementary effects, identified the occupancy and operational design variables with the greatest influence on building performance. The findings of sensitivity analysis guided a non-dominated sorting genetic algorithm to generate Pareto-optimal trade-offs across performance objectives and under varying occupancy and seasonal conditions. To translate the Pareto fronts into implementable settings, three multi-criteria decision-making methods were comparatively evaluated, providing decision-ready solutions tailored to different occupancy scenarios.

The results demonstrate that occupancy density, ventilation and heating setpoints are critical drivers of building performance in multi-occupancy settings. The optimization produced distinct, scenario-specific Pareto fronts rather than a single global optimum, with each front representing a spectrum of trade-offs among performance objectives, indicating that operating points are contingent on both occupancy and season. High-occupancy scenarios amplified the three-way trade-offs among objectives and widened the set of non-dominated solutions, whereas low-occupancy scenarios enabled superior energy efficiency and indoor air quality without compromising comfort. Seasonal variations further complicated performance optimization, emphasizing the need for adaptive, context-sensitive control strategies in shared working environments. Across Pareto-optimal solutions, thermal comfort was consistently improved and energy consumption reduced relative to the base case, with energy reductions reaching approximately 8% in summer and 22% in winter. In winter high-density scenarios where CO₂ exceeded 1000 ppm, optimization was able to reduce concentrations up to 11%. The TOPSIS-selected solutions delivered balanced improvements, reducing energy use by up to 6.8% in summer and 13.7% in winter, improving thermal comfort across seasons, and achieving modest winter IAQ improvements of up to 9.8% at higher occupancy densities.

By understanding the interdependencies between occupancy and performance metrics across seasons, this research establishes a robust foundation for designing and managing energy-efficient, healthy, and comfortable shared working environments. The proposed optimization framework is applicable to both new and retrofit projects, equipping designers, facility managers, and policy makers with evidence-based strategies to anticipate occupancy- and season-driven variability and implement adaptive, occupancy-tailored operating strategies. More broadly, it advances methodological frontiers for future research by demonstrating how multi-objective optimization can integrate occupancy dynamics, seasonal conditions, system performance, and operational management to accelerate progress toward high-performance sustainable buildings.