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Modeling and Analyzing Occupant Behaviors in Building Energy Analysis Using a State Space Approach and Non-Invasive Sensing

Triana Carmenate, Md Mahbubur Rahman, Diana Leante, Leonardo Bobadilla, Ali Mostafavi


Buildings represent one of the major sources of energy consumption in the United States. One of the most
important factors that affect the energy performance of buildings is the behavior of their occupants. Monitoring,
understanding, and decoding occupant’s behaviors are key to identifying energy waste and for proposing strategies
to curtail excessive energy consumption in buildings. In this paper, we propose a sensor-centric approach for
automated detection and proactive monitoring of energy waste due to occupant behaviors. We first propose a
methodology to mathematically model states and trajectories that arise in buildings in the context of energy
consumption. We then present a set of simple filtering algorithms to capture non-invasively information necessary
to detect wasteful states and trajectories. We also describe and implement a prototype of a sensor network
consisting of inexpensive distance, light, temperature sensors and electricity consumption monitors used in order
capture data related to occupancy behaviors. By maintaining a count of the number occupants and energy
expenditures in different regions of a building, we can estimate how occupancy behavior is affecting energy use
in a non-invasive way. Furthermore, we present initial ideas to pro-actively eliminate energy expenditure by
calculating a score associated with occupants in different regions. This score can be used to suggest policies
to users or facility managers to help reduce energy costs related to occupancy behaviors. Our ideas are tested
experimentally in a study case in a residential building.

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