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Commercial buildings account for approximately 36% of US electricity consumption, of which nearly two-thirds is met by fossil fuels [1] resulting in an adverse impact on the environment. Reducing this impact requires improving energy efficiency and lowering energy consumption. Most existing studies focus on designing methods to regulate and reduce HVAC and lighting energy consumption. However, few studies have focused on the control of occupant plugload energy consumption. In this study, we conducted multiple experiments to analyze changes in occupant plugload energy consumption due to monetary incentives and/or feedback. The experiments were performed in government office and university buildings at NASA Research Park located in Moffett Field, CA. Analysis of the data reveal significant plugload energy reduction can be achieved via feedback and/or incentive mechanisms. Autoregressive models are used to predict expected plugload savings in the presence of exogenous variables. The results of this study suggest that occupant-in-the-loop control architectures have the potential to reduce energy consumption and hence lower the carbon footprint of commercial buildings.
In metropolitan areas populated with commercial buildings, electric power supply is stringent especially during business hours. Demand side management using battery is a promising solution to mitigate peak demands, however long payback time creates b
Nowadays the emerging smart grid technology opens up the possibility of two-way communication between customers and energy utilities. Demand Response Management (DRM) offers the promise of saving money for commercial customers and households while he
This paper analyzes two modeling approaches for occupant behaviour in buildings. It compares a purely statistical approach with a multi-agent social simulation based approach. The study concerns the door openings in an office.
In commercial buildings, about 40%-50% of the total electricity consumption is attributed to Heating, Ventilation, and Air Conditioning (HVAC) systems, which places an economic burden on building operators. In this paper, we intend to minimize the en
This paper describes several applications in astronomy and cosmology that are addressed using probabilistic modelling and statistical inference.