An integrated model for position-based productivity and energy costs optimization in offices

In shared spaces, occupants may have varied thermal and visual preferences for the indoor environmental conditions. Moreover, an occupant's perception of the indoor environment, such as her thermal and visual sensations, depends on her position inside an enclosed space. There is a strong relationship between occupants' comfort conditions and their level of productivity, hence, improving the productivity of occupants in offices offers significant economic benefits. The main interest of this research is to propose a Multi-Objective Optimization (MOOP) method for position-based energy and comfort management in offices. The proposed method accounts for personalized thermal and visual preferences of occupants and their positions within an office space, and simultaneously optimizes energy consumption costs and collective productivity of office workers, by proposing Pareto optimal solutions for the automated control of the indoor environment. Occupants' thermal and visual preferences and positions, their productivity rates, thermal and visual behavior, Indoor Air Quality (IAQ) of the space, energy exchanges processes across the building, indoor and outdoor environmental parameters, and energy prices, are considered in this optimization. Application of the proposed method under varied occupancy scenarios is analyzed by energy performance simulation of a multi-zone office building, located in Montreal, Canada. The proposed method (1) has the flexibility to account for the diversity among occupants' environmental preferences, (2) manages the indoor environmental conditions based on office workers' positions and preferences, and (3) simultaneously optimizes energy costs and office workers' productivity. Crown Copyright (C) 2018 Published by Elsevier B.V. All rights reserved.