學術發表

- Fu, C., Quintana, M., Nagy, Z., & Miller, C. (2024). Filling time-series gaps using image techniques: Multidimensional context autoencoder approach for building energy data imputation. Applied Thermal Engineering, 236, 121545.

- Canaydin, A., Fu, C., Balint, A., Khalil, M., Miller, C., & Kazmi, H. (2024). Interpretable domain-informed and domain-agnostic features for supervised and unsupervised learning on building energy demand data. Applied Energy, 360, 122741.

- Fu, C., Kazmi, H., Quintana, M., & Miller, C. (2023, November). Enhancing Classification of Energy Meters with Limited Labels using a Semi-Supervised Generative Model. In Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (pp. 450-453).

- H. Kazmi, Fu, C., C. Miller, “Ten questions concerning data-driven modelling and forecasting of operational energy demand at building and urban scale,” Building and Environment, vol. 239, pp. 110407, 2023.

- Fu, C., Arjunan, P., & Miller, C. (2022, November). Trimming outliers using trees: winning solution of the large-scale energy anomaly detection (LEAD) competition. In Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (pp. 456-461).

- Miller, C., Picchetti, B., Fu, C., & Pantelic, J. (2022). Limitations of machine learning for building energy prediction: ASHRAE Great Energy Predictor III Kaggle competition error analysis. Science and Technology for the Built Environment, 1-18.

- Fu, C., & Miller, C. (2022). Using Google Trends as a proxy for occupant behavior to predict building energy consumption. Applied Energy, 310, 118343.

- Miller, C., Hao, L., Fu, C. (2022). Gradient boosting machines and careful pre-processing work best: ASHRAE Great Energy Predictor III lessons learned. arXiv preprint arXiv:2202.02898.

- Miller, C., Arjunan, P., Kathirgamanathan, A., Fu, C., Roth, J., Park, J. Y., ... & Haberl, J. (2020). The ASHRAE great energy predictor III competition: Overview and results. Science and Technology for the Built Environment, 26(10), 1427-1447.

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