A hierarchical approach to probabilistic wind power forecasting

Gilbert, Ciaran and Browell, Jethro and McMillan, David; (2018) A hierarchical approach to probabilistic wind power forecasting. In: 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). IEEE, USA. ISBN 9781538635964 (https://doi.org/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.


Gilbert, Ciaran ORCID logoORCID: https://orcid.org/0000-0001-6114-7880, Browell, Jethro ORCID logoORCID: https://orcid.org/0000-0002-5960-666X and McMillan, David ORCID logoORCID: https://orcid.org/0000-0003-3030-4702;