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The ASHRAE Great Energy Predictor III (GEPIII) competition was held in late2019 as one of the largest machine learning competitions ever held focused onbuilding performance. It was hosted on the Kaggle platform and resulted in39,402 prediction submissions, with the top five teams splitting $25,000 inprize money. This paper outlines lessons learned from participants, mainly fromteams who scored in the top 5% of the competition. Various insights were gainedfrom their experience through an online survey, analysis of publicly sharedsubmissions and notebooks, and the documentation of the winning teams. Thetop-performing solutions mostly used ensembles of Gradient Boosting Machine(GBM) tree-based models, with the LightGBM package being the most popular. Thesurvey participants indicated that the preprocessing and feature extractionphases were the most important aspects of creating the best modeling approach.All the survey respondents used Python as their primary modeling tool, and itwas common to use Jupyter-style Notebooks as development environments. Theseconclusions are essential to help steer the research and practicalimplementation of building energy meter prediction in the future.

Gradient boosting machines and careful pre-processing work best: ASHRAE Great Energy Predictor III lessons learned