Staff profile

• Machine Learning
• Uncertainty Quantification
• Forecast evaluation
• Nonlinear time series analysis
• Data Assimilation
• Weather and Climate modelling
• Energy System Optimisation

Conference Paper

• Liu, J., Kazemtabrizi, B., Du, H., Matthews, P., & Sun, H. (2024, November). An Integrated Stacked Sparse Autoencoder and CNN-BLSTM Model for Ultra-Short-Term Wind Power Forecasting with Advanced Feature Learning. Presented at 50th Annual Conference of the IEEE Industrial Electronics Society, Chicago, USA

Journal Article

• Domingo, D., Royapoor, M., Du, H., Boranian, A., Walker, S., & Goldstein, M. (2024). Calibration under Uncertainty Using Bayesian Emulation and History Matching: Methods and Illustration on a Building Energy Model. Energies, 17(16), Article 4014. https://doi.org/10.3390/en17164014
• Du, H. (2021). Beyond Strictly Proper Scoring Rules: The Importance of Being Local. Weather and Forecasting, 36(2), 457-468. https://doi.org/10.1175/waf-d-19-0205.1
• Du, H., Sun, W., Goldstein, M., & Harrison, G. (2021). Optimization via Statistical Emulation and Uncertainty Quantification: Hosting Capacity Analysis of Distribution Networks. IEEE Access, 9, 118472-118483. https://doi.org/10.1109/access.2021.3105935
• Smith, L. A., Du, H., & Higgins, S. (2020). Designing Multimodel Applications with Surrogate Forecast Systems. Monthly Weather Review, 148(6), 2233-2249. https://doi.org/10.1175/mwr-d-19-0061.1
• M, R., Du, H., N, W., M, G., T, R., P, T., & S, W. (2019). Carbon mitigation unit costs of building retrofits and the scope for carbon tax, a case study. Energy and Buildings, 203, https://doi.org/10.1016/j.enbuild.2019.109415
• Du, H., & Smith, L. A. (2017). Multi-model cross-pollination in time. Physica D: Nonlinear Phenomena, 353-354, 31-38. https://doi.org/10.1016/j.physd.2017.06.001
• Du, H., & Smith, L. A. (2017). Rising Above Chaotic Likelihoods. SIAM/ASA Journal on Uncertainty Quantification, 5(1), 246-258. https://doi.org/10.1137/140988784
• Smith, L. A., Suckling, E. B., Thompson, E. L., Maynard, T., & Du, H. (2015). Towards improving the framework for probabilistic forecast evaluation. Climatic Change, 132(1), 31-45. https://doi.org/10.1007/s10584-015-1430-2
• Smith, L. A., Du, H., Suckling, E. B., & Niehörster, F. (2015). Probabilistic skill in ensemble seasonal forecasts. Quarterly Journal of the Royal Meteorological Society, 141(689), 1085-1100. https://doi.org/10.1002/qj.2403
• Du, H., & Smith, L. A. (2014). Pseudo-Orbit Data Assimilation. Part II: Assimilation with Imperfect Models. Journal of the Atmospheric Sciences, 71(2), 483-495. https://doi.org/10.1175/jas-d-13-033.1
• Du, H., & Smith, L. A. (2014). Pseudo-Orbit Data Assimilation. Part I: The Perfect Model Scenario. Journal of the Atmospheric Sciences, 71(2), 469-482. https://doi.org/10.1175/jas-d-13-032.1
• Frigg, R., Bradley, S., Du, H., & Smith, L. A. (2014). Laplace’s Demon and the Adventures of His Apprentices. Philosophy of Science, 81(1), 31-59. https://doi.org/10.1086/674416
• Du, H., & Smith, L. A. (2012). Parameter estimation through ignorance. Physical review E: Statistical, nonlinear, and soft matter physics, 86(1), Article 016213. https://doi.org/10.1103/physreve.86.016213
• Smith, L. A., Cuéllar, M. C., Du, H., & Judd, K. (2010). Exploiting dynamical coherence: A geometric approach to parameter estimation in nonlinear models. Physics Letters A, 374(26), 2618-2623. https://doi.org/10.1016/j.physleta.2010.04.032