A Hierarchical Approach to Probabilistic Wind Power Forecasting

Gilbert, C., Browell, J. and McMillan, D. (2018) A Hierarchical Approach to Probabilistic Wind Power Forecasting. In: 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), Boise, ID, USA, 24-28 Jun 2018, ISBN 9781538635964 (doi: 10.1109/PMAPS.2018.8440571)

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This paper describes a method to generate improved probabilistic wind farm power forecasts in a hierarchical framework with the incorporation of production data from individual wind turbines. Wind power forms a natural hierarchy as generated electricity is aggregated from the individual turbine, to farm, to the regional level and so on. To forecast the wind farm power generation, a layered approach is proposed whereby deterministic forecasts from the lower layer (turbine level) are used as input features to an upper-level (wind farm) probabilistic model. In a case study at a utility scale wind farm it is shown that improvements in probabilistic forecast skill (CRPS) of 1.24% and 2.39% are obtainable when compared to two very competitive benchmarks based on direct forecasting of the wind farm power using Gradient Boosting Trees and an Analog Ensemble, respectively.

Item Type:Conference Proceedings
Additional Information:Ciaran Gilbert is supported by the University of Strathclyde's EPSRC Centre for Doctoral Training in Wind and Marine Energy Systems, grant number EP/L016680/1.
Glasgow Author(s) Enlighten ID:Browell, Dr Jethro
Authors: Gilbert, C., Browell, J., and McMillan, D.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics

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