Data-Driven Energy and Population Estimation for Real-Time City-Wide Energy Footprinting

Abstract

Energy footprinting has the potential to raise awareness of energy consumption and lead to energy saving behavior. However, current methods are largely restricted to single buildings; these methods require energy and occupancy monitoring sensor deployments, which can be expensive and difficult to deploy at scale. Further, current methods for estimating energy consumption and population cannot provide fine enough temporal or spatial granularity for a reasonable personal energy footprint estimate. In this work, we present CityEnergy, a data-driven system for city-wide estimation of personal energy footprints. CityEnergy takes advantage of existing sensing infrastructure and data sources in urban cities to provide energy and population estimates at the building level, even in built environments that do not have existing or accessible energy or population data.