Adaptive modelling and forecasting of offshore wind power fluctuations with Markov-switching autoregressive models

Publication: Research - peer-reviewJournal article – Annual report year: 2011

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Adaptive modelling and forecasting of offshore wind power fluctuations with Markov-switching autoregressive models. / Pinson, Pierre; Madsen, Henrik.

In: Journal of Forecasting, Vol. 31, No. 4, 2012, p. 281–313.

Publication: Research - peer-reviewJournal article – Annual report year: 2011

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Author

Pinson, Pierre; Madsen, Henrik / Adaptive modelling and forecasting of offshore wind power fluctuations with Markov-switching autoregressive models.

In: Journal of Forecasting, Vol. 31, No. 4, 2012, p. 281–313.

Publication: Research - peer-reviewJournal article – Annual report year: 2011

Bibtex

@article{f1f34ff17f77421c860e17f07186f3df,
title = "Adaptive modelling and forecasting of offshore wind power fluctuations with Markov-switching autoregressive models",
keywords = ", Wind power forecasting, Regime switching, Adaptive estimation, Point forecasting, Interval forecasting",
publisher = "John/Wiley & Sons Ltd.",
author = "Pierre Pinson and Henrik Madsen",
year = "2012",
doi = "10.1002/for.1194",
volume = "31",
number = "4",
pages = "281–313",
journal = "Journal of Forecasting",
issn = "0277-6693",

}

RIS

TY - JOUR

T1 - Adaptive modelling and forecasting of offshore wind power fluctuations with Markov-switching autoregressive models

A1 - Pinson,Pierre

A1 - Madsen,Henrik

AU - Pinson,Pierre

AU - Madsen,Henrik

PB - John/Wiley & Sons Ltd.

PY - 2012

Y1 - 2012

N2 - Wind power production data at temporal resolutions of a few minutes exhibit successive periods with fluctuations of various dynamic nature and magnitude, which cannot be explained (so far) by the evolution of some explanatory variable. Our proposal is to capture this regime-switching behaviour with an approach relying on Markov-switching autoregressive (MSAR) models. An appropriate parameterization of the model coefficients is introduced, along with an adaptive estimation method allowing accommodation of long-term variations in the process characteristics. The objective criterion to be recursively optimized is based on penalized maximum likelihood, with exponential forgetting of past observations. MSAR models are then employed for one-step-ahead point forecasting of 10 min resolution time series of wind power at two large offshore wind farms. They are favourably compared against persistence and autoregressive models. It is finally shown that the main interest of MSAR models lies in their ability to generate interval/density forecasts of significantly higher skill.

AB - Wind power production data at temporal resolutions of a few minutes exhibit successive periods with fluctuations of various dynamic nature and magnitude, which cannot be explained (so far) by the evolution of some explanatory variable. Our proposal is to capture this regime-switching behaviour with an approach relying on Markov-switching autoregressive (MSAR) models. An appropriate parameterization of the model coefficients is introduced, along with an adaptive estimation method allowing accommodation of long-term variations in the process characteristics. The objective criterion to be recursively optimized is based on penalized maximum likelihood, with exponential forgetting of past observations. MSAR models are then employed for one-step-ahead point forecasting of 10 min resolution time series of wind power at two large offshore wind farms. They are favourably compared against persistence and autoregressive models. It is finally shown that the main interest of MSAR models lies in their ability to generate interval/density forecasts of significantly higher skill.

KW - Wind power forecasting

KW - Regime switching

KW - Adaptive estimation

KW - Point forecasting

KW - Interval forecasting

U2 - 10.1002/for.1194

DO - 10.1002/for.1194

JO - Journal of Forecasting

JF - Journal of Forecasting

SN - 0277-6693

IS - 4

VL - 31

SP - 281

EP - 313

ER -