  Building energy performance benchmarking has been adopted widely in the USA
and Canada through the Energy Star Portfolio Manager platform. Building
operations and energy management professionals have long used a simple 1-100
score to understand how their building compares to its peers. This single
number is easy to use, but is created by inaccurate linear regression (MLR)
models. This paper proposes a methodology that enhances the existing Energy
Star calculation method by increasing accuracy and providing additional model
output processing to help explain why a building is achieving a certain score.
We propose and test two new prediction models: multiple linear regression with
feature interactions (MLRi) and gradient boosted trees (GBT). Both models have
better average accuracy than the baseline Energy Star models. The third order
MLRi and GBT models achieve 4.9% and 24.9% increase in adjusted R2,
respectively, and 7.0% and 13.7% decrease in normalized root mean squared error
(NRMSE), respectively, on average than MLR models for six building types. Even
more importantly, a set of techniques is developed to help determine which
factors most influence the score using SHAP values. The SHAP force
visualization in particular offers an accessible overview of the aspects of the
building that influence the score that non-technical users can readily
interpret. This methodology is tested on the 2012 Commercial Building Energy
Consumption Survey (CBECS)(1,812 buildings) and public data sets from the
energy disclosure programs of New York City (11,131 buildings) and Seattle
(2,073 buildings).
