Abstract

Environmental conditions of an indoor space have impacts on the mental and physical well-being of its occupants and subsequently influence their productivity. Occupants in a shared space may have varied thermal and visual preferences for the indoor environmental conditions. Moreover, their perceptions of the indoor environment, such as their thermal and visual sensations, depend on their positions inside the space. For energy management systems of office buildings, inability to acknowledge occupants’ preferences may cause productivity losses. Salaries of office workers are many times higher than the costs of energy consumption in providing comfort in the working space, hence, improving the productivity of occupants in office buildings can offer significant economic benefits. While optimizing energy consumption costs, the energy management system of an office building can provide occupants with preferred indoor environmental conditions by making timely energy-related decisions for the indoor environment. Several continuously changing inputs including indoor and outdoor environmental parameters, energy exchange processes across the building, energy prices, occupants’ presence, activities, and preferences, are required to make timely decisions.
The main objective of this research is to propose a method for personalized energy and comfort management in office buildings to simultaneously optimize energy consumption costs and the productivity of office workers. A simplified RC-network thermal model of a multi-zone office building, located in Montreal, Canada is developed and its annual energy performance simulation is studied. The method presents Pareto optimal solutions for the automated control of the indoor environment, by managing the level of indoor temperature, ventilation rate, natural illumination, and artificial lighting, in different zones of the office. Within a multi-objective optimization framework, several parameters are considered by the method, including (1) energy exchange processes across the zones, (2) sets of indoor and outdoor environmental parameters, (3) energy prices, (4) indoor air quality of the zones, and (5) occupants’ positions, activities, personalized thermal and visual preferences, and adaptive behavior. Under different scenarios, occupants are considered to have distinct thermal and visual preferences and behavior. The flexibility of the method to perform personalized energy and comfort management, by managing the indoor environmental conditions according to occupants’ personalized thermal and visual preferences, thermal and visual behavior, and positions are determined. Based on the provided results, the proposed method is capable of improving the productivity of occupants, by up to $1000 per year per person (assuming fixed productivity rate of 20 $/h), while simultaneously optimizing the energy consumption costs.