Time Series Methods for the Simulation of Wind Speed Fields Across Great Britain

  • Gruffudd Edwards

Student thesis: Doctoral ThesisPhD


This thesis presents the development of a time series model and associated algorithms capable of generating synthetic time-series datasets representing the hourly-averaged wind-speed field across the Country – as represented by a set of 20 points. This field is of interest as the energy resource available to wind generators connected to the Great Britain (GB) electricity networks. A wind power output dataset was also generated for an example distribution of wind generation capacities.The datasets generated are suitable for use in sequential Monte Carlo simulations of the GB electricity system – either the present system or future scenarios, potentially with full consideration of network constraints. Accurate representation of the spatio-temporal behaviours of renewable resources are an essential aspect of such simulations, along with their relationship to demand, with rarely occurring extreme events of particular interest. Therefore, variability in the resource occurring on all timescales – from turbulence to climatic shifts between decades must be represented. The synthetic data are time-stamped with time of the day and day of the year, so care was taken to ensure that all relevant deterministic and stochastic patterns are accurately reproduced.A major component of the research project was identification of the optimum level of complexity for various aspects of the model structure, and the associated computational expense of generating the series, particularly given the high dimensionality of the problem. The final choice of wind speed model was 2-factor-VGARMA-APARCH, along with several deterministic transformations.
Date of Award13 Nov 2014
Original languageEnglish
Awarding Institution
  • University of Bath
SupervisorRoderick Dunn (Supervisor)


  • Wind energy
  • Wind modelling
  • Time series analysis
  • Simulation and Modeling
  • Capacity Credit
  • power system planning
  • multivariate analysis

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