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Dr Zi Lin

Senior Lecturer

Department: Mechanical and Construction Engineering

Dr Zi Lin is a Senior Lecturer at Northumbria University. She holds a PhD degree in Engineering from the University of Strathclyde (UK); an MSc in Civil Engineering and a BEng (Hons) in Mechanical Engineering, both from Dalian University of Technology (China). Prior to Northumbria, she was a Research Fellow at the University of Strathclyde and Cranfield University.

Her main area of expertise is regarding fluid mechanics and offshore renewable energy systems (wind, wave and solar energy), focusing on the dynamics of renewable energy system modelling through the advanced numerical method and artificial intelligence algorithm. She has published more than 30 peer-reviewed international journals and conference papers and serves as a reviewer for top-rated journals.

To be involved in the energy sector outside of academia, she is a recognized professional in Project Management, who is licensed as a PRINCE2 Practitioner and have full membership at AXELOS. Apart from it, she is a frequent presenter and session-chair (35th, 37th and 38th; Ocean Renewable Energy) at the International Conference on Ocean, Offshore and Arctic Engineering (OMAE). Besides, she owns the Fellowship of the Higher Education Academy (FHEA), Full Membership of the Women's Engineering Society (WES) and Membership of the Institution of Mechanical Engineers (IMechE) & Chartered Engineer (CEng).

Zi Lin

  • Wind turbine aero-hydro-servo-elastic (AHSE) and drivetrain modelling
  • Method and application of machine learning
  • Offshore renewable energy (wind, wave and PV)
  • Hydrodynamics of offshore renewable energies and Boundary Element Method
  • Multibody Dynamics

  • Please visit the Pure Research Information Portal for further information
  • Anomaly detection in wind turbine SCADA data for power curve cleaning, Morrison, R., Liu, X., Lin, Z. 1 Jan 2022, In: Renewable Energy
  • Ensemble offshore Wind Turbine Power Curve modelling – An integration of Isolation Forest, fast Radial Basis Function Neural Network, and metaheuristic algorithm, Li, T., Liu, X., Lin, Z., Morrison, R. 15 Jan 2022, In: Energy
  • Short-term offshore wind power forecasting - A hybrid model based on Discrete Wavelet Transform (DWT), Seasonal Autoregressive Integrated Moving Average (SARIMA), and deep-learning-based Long Short-Term Memory (LSTM), Zhang, W., Lin, Z., Liu, X. 1 Feb 2022, In: Renewable Energy
  • Fault detection by an ensemble framework of Extreme Gradient Boosting (XGBoost) in the operation of offshore wind turbines, Trizoglou, P., Liu, X., Lin, Z. 1 Dec 2021, In: Renewable Energy
  • Impact of Covid-19 pandemic on electricity demand in the UK based on multivariate time series forecasting with Bidirectional Long Short Term Memory, Liu, X., Lin, Z. 15 Jul 2021, In: Energy
  • Impacts of water depth increase on offshore floating wind turbine dynamics, Lin, Z., Liu, X., Lotfian, S. 15 Mar 2021, In: Ocean Engineering
  • Lagrangian actuator model for wind turbine wake aerodynamics, Liu, W., Shi, J., Chen, H., Liu, H., Lin, Z., Wang, L. 1 Oct 2021, In: Energy
  • Short-term offshore wind speed forecast by seasonal ARIMA - A comparison against GRU and LSTM, Liu, X., Lin, Z., Feng, Z. 15 Jul 2021, In: Energy
  • Wind power forecasting – A data-driven method along with gated recurrent neural network, Kisvari, A., Lin, Z., Liu, X. Jan 2021, In: Renewable Energy
  • A Critical Review of Wind Power Forecasting Methods—Past, Present and Future, Hanifi, S., Liu, X., Lin, Z., Lotfian, S. 22 Jul 2020, In: Energies

  • Engineering PhD
  • Chartered Engineer CEng
  • Fellow of the Higher Education Academy FHEA


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