Modeling and analyzing occupant behaviors in building energy analysis using an information space approach

Buildings account for a majority of energy consumption in the United States. One of the major factors affecting the energy performance of buildings is occupant behaviors. Decoding occupant behaviors is a key to identifying energy waste and to discovering strategies to curtail energy consumption in buildings. We propose an information space approach for automated detection and proactive monitoring of energy waste due to occupant behaviors. In this paper we present a set of filtering algorithms to capture the minimum amount of information necessary to detect wasteful states and trajectories that occupants may have, in order to pro-actively modify occupant behaviors. We also describe and implement a sensor network consisting of inexpensive distance, light, temperature sensors and electricity consumption monitors utilized in order capture data related to occupancy behaviors. By keeping count of the number occupants and energy expenditures in different regions of a building, we accurately estimate how occupancy behavior is affecting energy use, in a non-invasive way. Furthermore, we present a methodology to pro-actively eliminate energy expenditure by calculating a score associated with occupants in different regions. This score will be used to suggest policies to users or facility managers to help reduce energy costs related to occupancy behaviors.