Abstract
As urban areas continue to grow and evolve in a world of increasing
environmental awareness, the need for detailed information regarding residential
energy consumption patterns has become increasingly important. Though current
modeling efforts mark significant progress in the effort to better understand the
spatial distribution of energy consumption, the majority of techniques are highly
dependent on region-specific data sources and often require building- or
dwelling-level details that are not publicly available for many regions in the United
States. Furthermore, many existing methods do not account for errors in input data
sources and may not accurately reflect inherent uncertainties in model outputs. We
propose an alternative and more general hybrid approach to high-resolution residential
electricity consumption modeling by merging a dasymetric model with a
complementary machine learning algorithm. The method’s flexible data requirement
and statistical framework ensure that the model both is applicable to a wide
range of regions and considers errors in input data sources.
