MURAL - Maynooth University Research Archive Library

    Considerations in developing a scalable wind power forecasting solution

    Butler, Shane and Farren, Des and Ringwood, John (2016) Considerations in developing a scalable wind power forecasting solution. In: IEEE Congress on Evolutionary Computation (CEC), 2016. IEEE, pp. 1541-1546. ISBN 9781509006236

    Download (632kB) | Preview

    Share your research

    Twitter Facebook LinkedIn GooglePlus Email more...

    Add this article to your Mendeley library


    This paper addresses the wind power forecasting problem from the standpoint of performance and solution requirements. These requirements stem from the need to trade wind power on real-time energy markets, the need for reliable and accurate forecasts, the ability to learn from new data as it becomes available, the ability of the solution to generalise and scale to a large number of wind farms, and the need to compute the solutions in real time. An analogs-based solution methodology is implemented and demonstrated to show potential in fulfilling the solution requirements. Analogs-based modelling represent a non-parametric modelling solution which exploit similarities in historical data with the current forecasting window. The methodology is applied to both local wind speed forecast correction and, subsequently, to wind farm power forecasting. Results for two wind farms in Europe are presented, confirming analogs as a viable and attractive solution methodology.

    Item Type: Book Section
    Keywords: wind farms; wind power plants; nonparametric statistics; power markets; wind forecasting;
    Academic Unit: Faculty of Science and Engineering > Electronic Engineering
    Faculty of Science and Engineering > Research Institutes > Centre for Ocean Energy Research
    Item ID: 9410
    Identification Number:
    Depositing User: Professor John Ringwood
    Date Deposited: 25 Apr 2018 13:56
    Journal or Publication Title: IEEE Congress on Evolutionary Computation (CEC), 2016
    Publisher: IEEE
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

    Repository Staff Only(login required)

    View Item Item control page


    Downloads per month over past year

    Origin of downloads