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Using Gradient Boosting Trees and Explainable ML for Commericial Building Benchmarking

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EnergyStar++: Towards more accurate and explanatory building energy benchmarking

The aim of this project is to develop the next generation building energy benchmarking system with following capabilities:

  • Fair: to ensure fair comparision of building's energy efficiency.
  • Explainable: to make the benchmarking process is understandable to the buliding owners.

Preprint found here

ENERGY STAR® is the most widely used energy benchmarking system for buildings and plants in the USA.

We reproduce the ENERGY STAR® models for the following building types:

Explainable Benchmarking system

In existing benchmarking systems, including ENERGY STAR®, it is difficult for the facility managers to understand how certain score/grade was assigned to their buildings. By leveraging Explainable Artificial Intelligence (XAI) methods, we unbox the complex models to make them exaplaintable to the facility managers. We have created an app using with one can clearly understand the level of influence of each building attribute and how it affects the final benchmarking score/grade.

Benchmarking using public datasets

One of challeneges of benchmarking is clearly define the peer groups. We analyze the feasability of using public datasets for developing a benchmarking system.

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Using Gradient Boosting Trees and Explainable ML for Commericial Building Benchmarking

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