Pandarasamy Arjunan (Samy) | ASHRAE - Great Energy Predictor III

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

Publications:

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

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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. This paper gives a high-level overview of the competition preparation and dataset, competitors and their discussions, machine learning workflows and models generated, winners and their submissions, discussion of lessons learned, and competition outputs and next steps. 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.

Scientific Data

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

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This paper describes an open data set of 3,053 energy meters from 1,636 non-residential buildings with a range of two full years (2016 and 2017) at an hourly frequency (17,544 measurements per meter resulting in approximately 53.6 million measurements). These meters were collected from 19 sites across North America and Europe, with one or more meters per building measuring whole building electrical, heating and cooling water, steam, and solar energy as well as water and irrigation meters. Part of these data were used in the Great Energy Predictor III (GEPIII) competition hosted by the ASHRAE organization in October-December 2019. GEPIII was a machine learning competition for long-term prediction with an application to measurement and verification. This paper describes the process of data collection, cleaning, and convergence of time-series meter data, the meta-data about the buildings, and complementary weather data. This data set can be used for further prediction benchmarking and prototyping as well as anomaly detection, energy analysis, and building type classification.