TO-22-C046 - Gradient Boosting Machines and Careful Pre-processing Work Best: ASHRAE Great Energy Predictor III Lessons Learned

The ASHRAE Great Energy Predictor III (GEPIII) competition was held in late 2019 as one of the largest machine learning competitions ever heldfocused on building performance. It was hosted on the Kaggle platform and resulted in 39,402 prediction submissions, with the top five teams splitting$25,000 in prize money. This paper outlines lessons learned from participants, mainly from teams who scored in the top 5% of the competition. Variousinsights were gained from their experience through an online survey, analysis of publicly shared submissions and notebooks, and the documentation of thewinning teams. The top-performing solutions mostly used ensembles of Gradient Boosting Machine (GBM) tree-based models, with the LightGBM packagebeing the most popular. The survey participants indicated that the preprocessing and feature extraction phases were the most important aspects of creatingthe best modeling approach. All the survey respondents used Python as their primary modeling tool, and it was common to use Jupyter-style Notebooks asdevelopment environments. These conclusions are essential to help steer the research and practical implementation of building energy meter prediction in thefuture.