There is growing interest in reducing building energy consumption through increased sensor data and increased computational support for building controls. The goal of reduced building energy is often coupled with the desire for improved occupant comfort. Current building systems are inefficient in their energy usage for maintaining occupant comfort as they operate according to fixed schedules and maximum design occupancy assumptions, and they rely on code defined occupant comfort ranges. This paper presents and implements a multi-agent comfort and energy system (MACES) to model alternative management and control of building systems and occupants. MACES specifically improves upon previous multi-agent systems as it coordinates both building system devices and building occupants through direct changes to occupant meeting schedules using multi-objective Markov Decision Problems (MDP). MACES is implemented and tested with input from a real-world building including actual thermal zones, temperatures, occupant preferences, and occupant schedules. The operations of this building are then simulated according to three distinct control strategies involving varying levels of intelligent coordination of devices and occupants. Finally, the energy and comfort results of these three strategies are compared to the baseline and opportunities for further energy savings are assessed. A 12% reduction in energy consumption and a 5% improvement in occupant comfort are realized as compared to the baseline control. Specifically, by employing MDP meeting relocating, an additional 5% improvement in energy consumption is realized over other control strategies.
Intelligent systems, Decision analytics, Energy consumption, Markov decision problems, Multi-agent systems
Artificial Intelligence and Robotics | Computer Sciences
Intelligent Systems and Decision Analytics
Automation in Construction
KLEIN, Laura; Kwak, Jun Young; Kavulya, Geoffrey; Jazizadeh, Farrokh; Becerik-Gerber, Burcin; VARAKANTHAM, Pradeep; and Tambe, Milind.
Coordinating occupant behavior for building energy and comfort management using multi-agent systems. (2012). Automation in Construction. 22, 525-536. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/1455
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