|Srinivasan Iyengar||University of Massachusetts Amherst|
|Stephen Lee||University of Massachusetts Amherst|
|David Irwin||University of Massachusetts Amherst|
|Prashant Shenoy||University of Massachusetts Amherst|
|Benjamin Weil||University of Massachusetts Amherst|
The authors present present WattHome, a data-driven approach to identify the least energy efficient buildings from a large population of buildings in a city or a region.
Buildings consume over 40% of the total energy in modern societies and improving their energy efficiency can significantly reduce our energy footprint. In this paper, we present WattHome, a data-driven approach to identify the least energy efficient buildings from a large population of buildings in a city or a region. Unlike previous approaches such as least squares that use point estimates, WattHome uses Bayesian inference to capture the stochasticity in the daily energy usage by estimating the parameter distribution of a building. Further, it compares them with similar homes in a given population using widely available datasets. WattHome also incorporates a fault detection algorithm to identify the underlying causes of energy inefficiency. We validate our approach using ground truth data from different geographical locations, which showcases its applicability in different settings. Moreover, we present results from a case study from a city containing >10,000 buildings and show that more than half of the buildings are inefficient in one way or another indicating a significant potential from energy improvement measures. Additionally, we provide probable cause of inefficiency and find that 41%, 23.73%, and 0.51% homes have poor building envelope, heating, and cooling system faults respectively.