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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Abstract

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:Book Section
    ID code:70980
    Dates:
    Date
    Event
    20 August 2018
    Published
    20 August 2018
    Published Online
    31 December 2017
    Accepted
    Notes:© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Strategic Research Themes > Energy
    Depositing user: Pure Administrator
    Date deposited:18 Dec 2019 12:25
    Last modified:08 Apr 2024 13:58
    URI:https://strathprints.strath.ac.uk/id/eprint/70980
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