Cluster-based regime-switching AR for the EEM 2017 Wind Power Forecasting Competition

Browell, Jethro and Gilbert, Ciaran P; (2017) Cluster-based regime-switching AR for the EEM 2017 Wind Power Forecasting Competition. In: 2017 14th International Conference on the European Electricity Market Conference (EEM). IEEE, DEU. ISBN 9781509054992 (In Press)

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Abstract

This paper describes the regime-switching autoregressive models used to win the EEM 2017 Wind Power Forecasting Competition. The competition required participants to produce daily forecast wind power production for a portfolio of wind farms from 2 to 38 hours-ahead based on historic generation and numerical weather prediction analysis data only. The regimes used in the methodology presented are defined on the previous day’s weather conditions using the k-medians clustering algorithm. Cross-validation is used to identify models with the best predictive power from a pool of candidate models. The final methodology produced a final weighted mean absolute error 4.5% lower than the second place team during the two-week competition period.

ORCID iDs

Browell, Jethro ORCID logoORCID: https://orcid.org/0000-0002-5960-666X and Gilbert, Ciaran P ORCID logoORCID: https://orcid.org/0000-0001-6114-7880;
  • Item type:Book Section
    ID code:60735
    Dates:
    Date
    Event
    19 May 2017
    Published
    19 May 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
    Depositing user: Pure Administrator
    Date deposited:22 May 2017 09:42
    Last modified:11 Nov 2024 15:10
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/60735
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