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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, Piscataway. 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.