A Large-Scale Study on Predicting and Contextualizing Building Energy Usage

Authors

  • J. Kolter Massachusetts Institute of Technology
  • Joseph Ferreira Massachusetts Institute of Technology

Abstract

In this paper we present a data-driven approach to modeling end user energy consumption in residential and commercial buildings. Our model is based upon a data set of monthly electricity and gas bills, collected by a utility over the course of several years, for approximately 6,500 buildings in Cambridge, MA. In addition, we use publicly available tax assessor records and geographical survey information to determine corresponding features for the buildings. Using both parametric and non-parametric learning methods, we learn models that predict distributions over energy usage based upon these features, and use these models to develop two end-user systems. For utilities or authorized institutions (those who may obtain access to the full data) we provide a system that visualizes energy consumption for each building in the city; this allows companies to quickly identify outliers (buildings which use much more energy than expected even after conditioning on the relevant predictors), for instance allowing them to target homes for potential retrofits or tiered pricing schemes. For other end users, we provide an interface for entering their own electricity and gas usage, along with basic information about their home, to determine how their consumption compares to that of similar buildings as predicted by our model. Merely allowing users to contextualize their consumption in this way, relating it to the consumption in similar buildings, can itself produce behavior changes to significantly reduce consumption.

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Published

2011-08-04

How to Cite

Kolter, J., & Ferreira, J. (2011). A Large-Scale Study on Predicting and Contextualizing Building Energy Usage. Proceedings of the AAAI Conference on Artificial Intelligence, 25(1), 1349-1356. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/7806

Issue

Section

Special Track on Computational Sustainability and AI