Use of turbine-level data for improved wind power forecasting

Browell, Jethro and Gilbert, Ciaran and McMillan, David; (2017) Use of turbine-level data for improved wind power forecasting. In: 12th IEEE PES PowerTech Conference. IEEE, GBR. (In Press)

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Abstract

Short-term wind power forecasting is based on modelling the complex relationship between the weather forecasts and wind farm power production. To date, efforts to improve wind power forecasts have focused on improving Numerical Weather Prediction and wind farm power curve models. However, utility-scale wind farms cover large areas meaning that a single power curve model may struggle to represent the collective behaviour of large numbers of wind turbines. Contemporary statistical techniques are capable of processing large volumes of data, enabling the assimilation of measurements from individual wind turbines to construct a more detailed representation of wind farm power generation. Here, three state-of-the-art techniques are applied to forecast wind farm power production 1) directly from numerical weather predictions, and 2) by aggregating forecasts for individual wind turbines. Furthermore, it is observed that some wind turbines are better predictors than others and an aggregation process based on conditional weighting is proposed. In case studies of two large wind farms in the UK, it is shown that wind farm power forecasts comprising a conditional weighted sum of turbine-level predictions are superior to a direct wind farm forecast for horizons up to 48 hours ahead. Specifically, performance of the best-performing benchmark, the gradient boosting machine, is improved by 12% at Clyde South wind farm and by 6% at Gordonbush.

ORCID iDs

Browell, Jethro ORCID logoORCID: https://orcid.org/0000-0002-5960-666X, Gilbert, Ciaran ORCID logoORCID: https://orcid.org/0000-0001-6114-7880 and McMillan, David ORCID logoORCID: https://orcid.org/0000-0003-3030-4702;
  • Item type:Book Section
    ID code:60591
    Dates:
    Date
    Event
    7 April 2017
    Published
    7 April 2017
    Accepted
    Notes:© 2017 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:03 May 2017 11:43
    Last modified:19 Nov 2024 01:22
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/60591
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