ARIMA-Based Time Series Model of Stochastic Wind Power Generation

Peiyuan Chen; Troels Pedersen; Birgitte Bak-Jensen; Zhe Chen

doi:10.1109/TPWRS.2009.2033277

ARIMA-Based Time Series Model of Stochastic Wind Power Generation

Peiyuan Chen, Troels Pedersen, Birgitte Bak-Jensen, Zhe Chen

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review

333 Citationer (Scopus)

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Abstract

This paper proposes a stochastic wind power model based on an autoregressive integrated moving average (ARIMA)
process. The model takes into account the nonstationarity and physical limits of stochastic wind power generation. The model is constructed based on wind power measurement of one year from the Nysted offshore wind farm in Denmark. The proposed limited-ARIMA (LARIMA) model introduces a limiter and characterizes the stochastic wind power generation by mean level, temporal correlation and driving noise. The model is validated against the measurement in terms of temporal correlation and probability distribution. The LARIMA model outperforms a
first-order transition matrix based discrete Markov model in terms of temporal correlation, probability distribution and model parameter number. The proposed LARIMA model is further extended to include the monthly variation of the stochastic wind power generation.

Originalsprog	Engelsk
Tidsskrift	IEEE Transactions on Power Systems
Vol/bind	25
Udgave nummer	2
Sider (fra-til)	667-676
Antal sider	10
ISSN	0885-8950
DOI	https://doi.org/10.1109/TPWRS.2009.2033277
Status	Udgivet - 2010

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© 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, 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.

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10.1109/TPWRS.2009.2033277

Corrected Dec072009 ARIMA Based Time Series Model of Stochastic Wind Power GenerationForlagets udgivne version, 423 KB

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Citationsformater

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abstract = "This paper proposes a stochastic wind power model based on an autoregressive integrated moving average (ARIMA)process. The model takes into account the nonstationarity and physical limits of stochastic wind power generation. The model is constructed based on wind power measurement of one year from the Nysted offshore wind farm in Denmark. The proposed limited-ARIMA (LARIMA) model introduces a limiter and characterizes the stochastic wind power generation by mean level, temporal correlation and driving noise. The model is validated against the measurement in terms of temporal correlation and probability distribution. The LARIMA model outperforms afirst-order transition matrix based discrete Markov model in terms of temporal correlation, probability distribution and model parameter number. The proposed LARIMA model is further extended to include the monthly variation of the stochastic wind power generation.",

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AU - Chen, Peiyuan

AU - Pedersen, Troels

AU - Bak-Jensen, Birgitte

AU - Chen, Zhe

N1 - © 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, 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 - 2010

Y1 - 2010

N2 - This paper proposes a stochastic wind power model based on an autoregressive integrated moving average (ARIMA)process. The model takes into account the nonstationarity and physical limits of stochastic wind power generation. The model is constructed based on wind power measurement of one year from the Nysted offshore wind farm in Denmark. The proposed limited-ARIMA (LARIMA) model introduces a limiter and characterizes the stochastic wind power generation by mean level, temporal correlation and driving noise. The model is validated against the measurement in terms of temporal correlation and probability distribution. The LARIMA model outperforms afirst-order transition matrix based discrete Markov model in terms of temporal correlation, probability distribution and model parameter number. The proposed LARIMA model is further extended to include the monthly variation of the stochastic wind power generation.

AB - This paper proposes a stochastic wind power model based on an autoregressive integrated moving average (ARIMA)process. The model takes into account the nonstationarity and physical limits of stochastic wind power generation. The model is constructed based on wind power measurement of one year from the Nysted offshore wind farm in Denmark. The proposed limited-ARIMA (LARIMA) model introduces a limiter and characterizes the stochastic wind power generation by mean level, temporal correlation and driving noise. The model is validated against the measurement in terms of temporal correlation and probability distribution. The LARIMA model outperforms afirst-order transition matrix based discrete Markov model in terms of temporal correlation, probability distribution and model parameter number. The proposed LARIMA model is further extended to include the monthly variation of the stochastic wind power generation.

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DO - 10.1109/TPWRS.2009.2033277

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EP - 676

JO - IEEE Transactions on Power Systems

JF - IEEE Transactions on Power Systems

IS - 2

ER -

ARIMA-Based Time Series Model of Stochastic Wind Power Generation

Abstract

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