107 citations · 200 across the 6 of their papers we have counts for
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cs.LG2022★ 4 cited
Gradient boosting machines and careful pre-processing work best: ASHRAE Great Energy Predictor III lessons learned
Clayton Miller, Liu Hao, Chun Fu
The ASHRAE Great Energy Predictor III (GEPIII) competition was held in late 2019 as one of the largest machine learning competitions ever held focused on building performance. It w…
cs.LG2021★ 43 cited
Using Google Trends as a proxy for occupant behavior to predict building energy consumption
Chun Fu, Clayton Miller
In recent years, the availability of larger amounts of energy data and advanced machine learning algorithms has created a surge in building energy prediction research. However, one…
cs.LG2021★ 42 cited
Limitations of machine learning for building energy prediction: ASHRAE Great Energy Predictor III Kaggle competition error analysis
Clayton Miller, Bianca Picchetti, Chun Fu +1
Research is needed to explore the limitations and potential for improvement of machine learning for building energy prediction. With this aim, the ASHRAE Great Energy Predictor III…