OESPG: Computational Framework for Multidimensional Analysis of Occupant Energy Use Data in Commercial Buildings

Commercial buildings account for much of the energy use both in the United States and globally. The role of occupant behavior within the physical building has been found to be an important factor in the overall energy use profile of commercial buildings. Recent research has noted the potential energy savings that can be achieved when occupant behavior is beneficially modified. However, frameworks for analyzing occupant behavior are limited in their ability to simultaneously consider three key dimensions of occupant-driven energy use in buildings: spatial, temporal, and social. This paper presents the occupant energy signal processing on graphs (OESPG) framework, which is able to address the three key dimensions of occupant energy use in commercial buildings through an inherently scalable mathematical structure. The mechanics, applicability, and merits of the OESPG framework are demonstrated by applying it to occupant energy use data through both a simulated example and real test-bed data from a commercial office building. The OESPG framework able to identify situations in which occupant energy use through plug loads is out of sync with what would be expected based on nuanced spatial and organizational aspects. Additionally, the feasibility of using this framework to make recommendations for temporal and spatial occupancy shifts that would have a positive impact on occupant energy use is noted.