107 citations · 200 across the 6 of their papers we have counts for
6 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…
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…
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…
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…
A Probability Distribution and Location-aware ResNet Approach for QoS Prediction
Wenyan Zhang, Ling Xu, Meng Yan +2
In recent years, the number of online services has grown rapidly, invoke the required services through the cloud platform has become the primary trend. How to help users choose and…
The ASHRAE Great Energy Predictor III competition: Overview and results
Clayton Miller, Pandarasamy Arjunan, Anjukan Kathirgamanathan +8
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…