Accurate Medium-Term Wind Power Forecasting in a Censored Classification Framework

Christian M. Dahl; Carsten Croonenbroeck

doi:10.1016/j.energy.2014.06.013

Accurate Medium-Term Wind Power Forecasting in a Censored Classification Framework

Christian M. Dahl, Carsten Croonenbroeck

Research output: Contribution to journal › Journal article › Research › peer-review

Abstract

We provide a wind power forecasting methodology that exploits many of the actual data's statistical features, in particular both-sided censoring. While other tools ignore many of the important “stylized facts” or provide forecasts for short-term horizons only, our approach focuses on medium-term forecasts, which are especially necessary for practitioners in the forward electricity markets of many power trading places; for example, NASDAQ OMX Commodities (formerly Nord Pool OMX Commodities) in northern Europe. We show that our model produces turbine-specific forecasts that are significantly more accurate in comparison to established benchmark models and present an application that illustrates the financial impact of more accurate forecasts obtained using our methodology.

Original language	English
Journal	Energy
Volume	73
Issue number	14
Pages (from-to)	221-232
Number of pages	12
ISSN	0360-5442
DOIs	https://doi.org/10.1016/j.energy.2014.06.013
Publication status	Published - 1. Aug 2014

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Wind power

Turbines

Power markets

Keywords

Censored regression
Wind energy
Forecasting

Cite this

Dahl, C. M., & Croonenbroeck, C. (2014). Accurate Medium-Term Wind Power Forecasting in a Censored Classification Framework. Energy, 73(14), 221-232. https://doi.org/10.1016/j.energy.2014.06.013

Dahl, Christian M. ; Croonenbroeck, Carsten. / Accurate Medium-Term Wind Power Forecasting in a Censored Classification Framework. In: Energy. 2014 ; Vol. 73, No. 14. pp. 221-232.

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abstract = "We provide a wind power forecasting methodology that exploits many of the actual data's statistical features, in particular both-sided censoring. While other tools ignore many of the important “stylized facts” or provide forecasts for short-term horizons only, our approach focuses on medium-term forecasts, which are especially necessary for practitioners in the forward electricity markets of many power trading places; for example, NASDAQ OMX Commodities (formerly Nord Pool OMX Commodities) in northern Europe. We show that our model produces turbine-specific forecasts that are significantly more accurate in comparison to established benchmark models and present an application that illustrates the financial impact of more accurate forecasts obtained using our methodology.",

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Dahl, CM & Croonenbroeck, C 2014, 'Accurate Medium-Term Wind Power Forecasting in a Censored Classification Framework', Energy, vol. 73, no. 14, pp. 221-232. https://doi.org/10.1016/j.energy.2014.06.013

Accurate Medium-Term Wind Power Forecasting in a Censored Classification Framework. / Dahl, Christian M.; Croonenbroeck, Carsten.

In: Energy, Vol. 73, No. 14, 01.08.2014, p. 221-232.

Research output: Contribution to journal › Journal article › Research › peer-review

TY - JOUR

T1 - Accurate Medium-Term Wind Power Forecasting in a Censored Classification Framework

AU - Dahl, Christian M.

AU - Croonenbroeck, Carsten

PY - 2014/8/1

Y1 - 2014/8/1

N2 - We provide a wind power forecasting methodology that exploits many of the actual data's statistical features, in particular both-sided censoring. While other tools ignore many of the important “stylized facts” or provide forecasts for short-term horizons only, our approach focuses on medium-term forecasts, which are especially necessary for practitioners in the forward electricity markets of many power trading places; for example, NASDAQ OMX Commodities (formerly Nord Pool OMX Commodities) in northern Europe. We show that our model produces turbine-specific forecasts that are significantly more accurate in comparison to established benchmark models and present an application that illustrates the financial impact of more accurate forecasts obtained using our methodology.

AB - We provide a wind power forecasting methodology that exploits many of the actual data's statistical features, in particular both-sided censoring. While other tools ignore many of the important “stylized facts” or provide forecasts for short-term horizons only, our approach focuses on medium-term forecasts, which are especially necessary for practitioners in the forward electricity markets of many power trading places; for example, NASDAQ OMX Commodities (formerly Nord Pool OMX Commodities) in northern Europe. We show that our model produces turbine-specific forecasts that are significantly more accurate in comparison to established benchmark models and present an application that illustrates the financial impact of more accurate forecasts obtained using our methodology.

KW - Censored regression

KW - Wind energy

KW - Forecasting

U2 - 10.1016/j.energy.2014.06.013

DO - 10.1016/j.energy.2014.06.013

M3 - Journal article

VL - 73

SP - 221

EP - 232

JO - Energy

JF - Energy

SN - 0360-5442

IS - 14

ER -

Dahl CM, Croonenbroeck C. Accurate Medium-Term Wind Power Forecasting in a Censored Classification Framework. Energy. 2014 Aug 1;73(14):221-232. https://doi.org/10.1016/j.energy.2014.06.013

Documents & Links

10.1016/j.energy.2014.06.013