42 citations · 49 across the 4 of their papers we have counts for
4 papers
The Building Data Genome Directory -- An open, comprehensive data sharing platform for building performance research
Xiaoyu Jin, Chun Fu, Hussain Kazmi +6
The building sector plays a crucial role in the worldwide decarbonization effort, accounting for significant portions of energy consumption and environmental effects. However, the…
ALDI++: Automatic and parameter-less discord and outlier detection for building energy load profiles
Matias Quintana, Till Stoeckmann, June Young Park +3
Data-driven building energy prediction is an integral part of the process for measurement and verification, building benchmarking, and building-to-grid interaction. The ASHRAE Grea…
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…
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…