Staff profile

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

Conference Paper

• Bi-Level Low-Carbon Scheduling of Active Distribution Networks with Multiple Technical Virtual Power Plants in the Integrated Electricity and Carbon Markets
  
  Liu, J., Ruan, J., Tang, H., Kazemtabrizi, B., Du, H., Matthews, P. C., & Sun, H. (in press). Bi-Level Low-Carbon Scheduling of Active Distribution Networks with Multiple Technical Virtual Power Plants in the Integrated Electricity and Carbon Markets. Presented at IEEE ISGT (Innovative Smart Grid Technologies), Valleta, Malta.
• An Integrated Stacked Sparse Autoencoder and CNN-BLSTM Model for Ultra-Short-Term Wind Power Forecasting with Advanced Feature Learning
  
  Liu, J., Kazemtabrizi, B., Du, H., Matthews, P., & Sun, H. (2025). An Integrated Stacked Sparse Autoencoder and CNN-BLSTM Model for Ultra-Short-Term Wind Power Forecasting with Advanced Feature Learning. In IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society. IEEE. https://doi.org/10.1109/IECON55916.2024.10905784

Journal Article

• Double weighted k nearest neighbours for binary classification of high dimensional genomic data
  
  Ali, A., Khan, Z., Du, H., & Aldahmani, S. (2025). Double weighted k nearest neighbours for binary classification of high dimensional genomic data. Scientific Reports, 15(1), Article 12681. https://doi.org/10.1038/s41598-025-97505-2
• Calibration under Uncertainty Using Bayesian Emulation and History Matching: Methods and Illustration on a Building Energy Model
  
  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
• Beyond Strictly Proper Scoring Rules: The Importance of Being Local
  
  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
• Optimization via Statistical Emulation and Uncertainty Quantification: Hosting Capacity Analysis of Distribution Networks
  
  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
• Designing Multimodel Applications with Surrogate Forecast Systems
  
  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
• Carbon mitigation unit costs of building retrofits and the scope for carbon tax, a case study
  
  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
• Multi-model cross-pollination in time
  
  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
• Rising Above Chaotic Likelihoods
  
  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
• Towards improving the framework for probabilistic forecast evaluation
  
  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
• Probabilistic skill in ensemble seasonal forecasts
  
  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
• Pseudo-Orbit Data Assimilation. Part I: The Perfect Model Scenario
  
  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
• Pseudo-Orbit Data Assimilation. Part II: Assimilation with Imperfect Models
  
  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
• Laplace’s Demon and the Adventures of His Apprentices
  
  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
• Parameter estimation through ignorance
  
  Du, H., & Smith, L. A. (2012). Parameter estimation through ignorance. Physical Review E, 86(1), Article 016213. https://doi.org/10.1103/physreve.86.016213
• Exploiting dynamical coherence: A geometric approach to parameter estimation in nonlinear models
  
  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