Leveraging turbine-level data for improved probabilistic wind power forecasting
Gilbert, Ciaran and Browell, Jethro and McMillan, David (2020) Leveraging turbine-level data for improved probabilistic wind power forecasting. IEEE Transactions on Sustainable Energy, 11 (3). pp. 1152-1160. ISSN 1949-3037 (https://doi.org/10.1109/TSTE.2019.2920085)
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Abstract
This paper describes two methods for creating improved probabilistic wind power forecasts through the use of turbine-level data. The first is a feature engineering approach whereby deterministic power forecasts from the turbine level are used as explanatory variables in a wind farm level forecasting model. The second is a novel bottom-up hierarchical approach where the wind farm forecast is inferred from the joint predictive distribution of the power output from individual turbines. Notably, the latter produces probabilistic forecasts that are coherent across both turbine and farm levels, which the former does not. The methods are tested at two utility scale wind farms and are shown to provide consistent improvements of up to 5%, in terms of continuous ranked probability score compared to the best performing state-of-the-art benchmark model. The bottom-up hierarchical approach provides greater improvement at the site characterized by a complex layout and terrain, while both approaches perform similarly at the second location. We show that there is a clear benefit in leveraging readily available turbine-level information for wind power forecasting.
ORCID iDs
Gilbert, Ciaran ORCID: https://orcid.org/0000-0001-6114-7880, Browell, Jethro ORCID: https://orcid.org/0000-0002-5960-666X and McMillan, David ORCID: https://orcid.org/0000-0003-3030-4702;-
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Item type: Article ID code: 68158 Dates: DateEvent31 July 2020Published6 June 2019Published Online18 May 2019AcceptedSubjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering
Strategic Research Themes > EnergyDepositing user: Pure Administrator Date deposited: 30 May 2019 15:50 Last modified: 11 Nov 2024 12:03 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/68158