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

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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.
LanguageEnglish
Title of host publication2017 14th International Conference on the European Electricity Market Conference (EEM)
Place of PublicationPiscataway
PublisherIEEE
ISBN (Print)9781509054992
Publication statusAccepted/In press - 19 May 2017
Event14th International Conference on the European Energy Market - Technische Universität Dresden, Dresden, Germany
Duration: 6 Jun 20179 Jun 2017

Conference

Conference14th International Conference on the European Energy Market
Abbreviated titleEEM 17
CountryGermany
CityDresden
Period6/06/179/06/17

Fingerprint

Wind power
Clustering algorithms
Farms

Keywords

  • wind power
  • forecasting
  • time series
  • clustering
  • autoregression
  • regime switching

Cite this

Browell, J., & Gilbert, C. P. (Accepted/In press). 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) Piscataway: IEEE.
Browell, Jethro ; Gilbert, Ciaran P. / Cluster-based regime-switching AR for the EEM 2017 Wind Power Forecasting Competition. 2017 14th International Conference on the European Electricity Market Conference (EEM). Piscataway : IEEE, 2017.
@inproceedings{9937e1a136234d51b61b792d7f4c70b4,
title = "Cluster-based regime-switching AR for the EEM 2017 Wind Power Forecasting Competition",
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.",
keywords = "wind power, forecasting, time series, clustering, autoregression, regime switching",
author = "Jethro Browell and Gilbert, {Ciaran P}",
note = "{\circledC} 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.",
year = "2017",
month = "5",
day = "19",
language = "English",
isbn = "9781509054992",
booktitle = "2017 14th International Conference on the European Electricity Market Conference (EEM)",
publisher = "IEEE",

}

Browell, J & Gilbert, CP 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, Piscataway, 14th International Conference on the European Energy Market, Dresden, Germany, 6/06/17.

Cluster-based regime-switching AR for the EEM 2017 Wind Power Forecasting Competition. / Browell, Jethro; Gilbert, Ciaran P.

2017 14th International Conference on the European Electricity Market Conference (EEM). Piscataway : IEEE, 2017.

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

TY - GEN

T1 - Cluster-based regime-switching AR for the EEM 2017 Wind Power Forecasting Competition

AU - Browell, Jethro

AU - Gilbert, Ciaran P

N1 - © 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.

PY - 2017/5/19

Y1 - 2017/5/19

N2 - 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.

AB - 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.

KW - wind power

KW - forecasting

KW - time series

KW - clustering

KW - autoregression

KW - regime switching

UR - http://eem2017.com/

UR - http://ieeexplore.ieee.org/servlet/opac?punumber=1002121

M3 - Conference contribution book

SN - 9781509054992

BT - 2017 14th International Conference on the European Electricity Market Conference (EEM)

PB - IEEE

CY - Piscataway

ER -

Browell J, Gilbert CP. 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). Piscataway: IEEE. 2017