# The ASHRAE Great Energy Predictor III competition: Overview and results

arXiv: https://arxiv.org/abs/2007.06933 · HTML: https://mlsanity.com/paper/2007.06933/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2020.

## Built on by (most cited first, 15 of 15)

- [SaDI: A Self-adaptive Decomposed Interpretable Framework for Electric Load Forecasting under Extreme Events](https://mlsanity.com/paper/2306.08299/) (2023)
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- [A Systematic Review for Transformer-based Long-term Series Forecasting](https://mlsanity.com/paper/2310.20218/) (2023)
- [Limitations of machine learning for building energy prediction: ASHRAE Great Energy Predictor III Kaggle competition error analysis](https://mlsanity.com/paper/2106.13475/) (2021)
- [Creating synthetic energy meter data using conditional diffusion and building metadata](https://mlsanity.com/paper/2404.00525/) (2024)
- [Using Google Trends as a proxy for occupant behavior to predict building energy consumption](https://mlsanity.com/paper/2111.00426/) (2021)
- [Gradient boosting machines and careful pre-processing work best: ASHRAE Great Energy Predictor III lessons learned](https://mlsanity.com/paper/2202.02898/) (2022)
- [LEAD1.0: A Large-scale Annotated Dataset for Energy Anomaly Detection in Commercial Buildings](https://mlsanity.com/paper/2203.17256/) (2022)
- [Interpreting Machine Learning Models for Room Temperature Prediction in Non-domestic Buildings](https://mlsanity.com/paper/2111.13760/) (2021)
- [SolNet: Open-source deep learning models for photovoltaic power forecasting across the globe](https://mlsanity.com/paper/2405.14472/) (2024)
- [FETS Benchmark: Foundation Models Enable Scalable and Generalizable Energy Time Series Forecasting](https://mlsanity.com/paper/2604.22328/) (2026)
- [Frugal day-ahead forecasting of multiple local electricity loads by aggregating adaptive models](https://mlsanity.com/paper/2302.08192/) (2023)
- [ALDI++: Automatic and parameter-less discord and outlier detection for building energy load profiles](https://mlsanity.com/paper/2203.06618/) (2022)
- [X-ray transferable polyrepresentation learning](https://mlsanity.com/paper/2507.06264/) (2025)
- [Uncovering Memorization in Timeseries Imputation models: LBRM Membership Inference and its link to attribute Leakage](https://mlsanity.com/paper/2603.24213/) (2026)

## Abstract (arXiv)

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.