ASHRAE - Great Energy Predictor III

A Kaggle competition to generate crowd-sourced energy prediction models.

In late 2019, ASHRAE hosted the Great Energy Predictor III (GEPIII) machine learning competition on the Kaggle platform. This launch marked the third energy prediction competition from ASHRAE and the first since the mid-1990s. In this updated version, the competitors were provided with over 20 million points of training data from 2,380 energy meters collected for 1,448 buildings from 16 sources. This competition’s overall objective was to find the most accurate modeling solutions for the prediction of over 41 million private and public test data points. The competition had 4,370 participants, split across 3,614 teams from 94 countries who submitted 39,403 predictions. In addition to the top five winning workflows, the competitors publicly shared 415 reproducible online machine learning workflow examples (notebooks), including over 40 additional, full solutions. The most popular and accurate machine learning workflows used large ensembles of mostly gradient boosting tree models, such as LightGBM. Similar to the first predictor competition, preprocessing of the data sets emerged as a key differentiator.

Competition website: Kaggle
Code: Overview of analysis of data about the competition and Top 5 winning solutions
Dataset: The Building Data Genome 2 (BDG2) - extended version
Video: An overview of the competition from the ASHRAE 2020 Online Conference


  1. ASHRAE STBE IF: 1.356
    The ASHRAE Great Energy Predictor III competition: Overview and results
    Miller, Clayton, Arjunan, Pandarasamy, Kathirgamanathan, Anjukan, Fu, Chun, Roth, Jonathan, Park, June Young, Balbach, Chris, Gowri, Krishnan, Nagy, Zoltan, Fontanini, Anthony D, and others,
    Science and Technology for the Built Environment 2020
  1. The Building Data Genome Project 2: Hourly energy meter data from the ASHRAE Great Energy Predictor III competition
    Miller, Clayton, Kathirgamanathan, Anjukan, Picchetti, Bianca, Arjunan, Pandarasamy, Park, June Young, Nagy, Zoltan, Raftery, Paul, Hobson, Brodie W, Shi, Zixiao, and Meggers, Forrest
    Scientific data 2020