Air quality and thermal comfort management for energy-efficient large public buildings

Abstract

Maximizing indoor comfort while minimizing energy costs has been a challenging problem for building management systems. This problem is significantly exacerbated for large public buildings with varying occupancy levels. A practical approach to measure occupants’ comfort has been to evaluate the indoor thermal comfort using Fanger’s Predictive Mean Vote (PMV) model, parameterized by the ambient temperature and Relative Humidity (RH). Such an approach is, however, one dimensional and does not consider other possible sources of discomfort like indoor air quality. Interestingly, the ambient temperature and RH, in addition to thermal comfort, also influence the amount of emissions from indoor furnishings, which is a prime source of indoor air quality degradation. Taking this into account, in this paper, we adapt the definition of comfort to include indoor air quality as well. Since occupancy levels, occupants’ activities and outdoor temperature vary with time, one way to achieve desired comfort goals is to continuously adapt the settings of Heating, Ventilation, and Air Conditioning (HVAC) units in buildings. Such a continuous adaptation results in significant energy costs, especially in geographical locations where outdoor temperatures can be significantly higher/lower than desired indoor temperatures. In this context, we propose a location-aware multi-objective optimization model for indoor comfort and energy cost management. We combine conflicting objectives—improving air quality and thermal comforts, and minimizing energy cost—to determine cost-driven, comfort-driven and Pareto optimal solutions using Multi-Objective Genetic Algorithm (MOGA). The proposed model is envisioned to enable building operators to determine suitable temperature and RH as per occupants’ requirement. The solution can be personalized based on the building structure and macro- and micro-location parameters. To ease configuration and customization of our model based on building- and geography-specific settings, we also present a MATLAB-based GUI that operators can leverage to understand the comfort-cost trade-off for buildings.