Prediction of wind power generation based on ARMA with additive noise model

Yang Gao; Zai Lin Piao; Xu Peng Zhang; Lei Dong; Ying Hao

Prediction of wind power generation based on ARMA with additive noise model

Yang Gao^*, Zai Lin Piao, Xu Peng Zhang, Lei Dong, Ying Hao

^*Corresponding author for this work

School of Automation

Research output: Contribution to journal › Article › peer-review

11 Citations (Scopus)

Abstract

Because of a mass of factors, forecast quality of ordinary ARMA model will be greatly reduced. Aiming at the condition, the time series of wind power generation from the Fujin Wind Farm located in China and HOYW-based order dertermining method are used to build ARMA with additive noise model. Model applicability is verified by the analysis of the residual error. Applying this new model to predict, the Normalized Mean Absolute Error (NMAE) is 0.0658. Then, the superiority of the new ARMA model is verified during the comparison with the ordinary ARMA model in accuracy of wind power generation prediction.

Original language	English
Pages (from-to)	164-167
Number of pages	4
Journal	Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control
Volume	38
Issue number	20
Publication status	Published - 16 Oct 2010

Keywords

ARMA model
HOYW method
Model applicability
Noise
Normalized mean absolute error (NMAE)
Wind power generation prediction

Cite this

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title = "Prediction of wind power generation based on ARMA with additive noise model",

abstract = "Because of a mass of factors, forecast quality of ordinary ARMA model will be greatly reduced. Aiming at the condition, the time series of wind power generation from the Fujin Wind Farm located in China and HOYW-based order dertermining method are used to build ARMA with additive noise model. Model applicability is verified by the analysis of the residual error. Applying this new model to predict, the Normalized Mean Absolute Error (NMAE) is 0.0658. Then, the superiority of the new ARMA model is verified during the comparison with the ordinary ARMA model in accuracy of wind power generation prediction.",

keywords = "ARMA model, HOYW method, Model applicability, Noise, Normalized mean absolute error (NMAE), Wind power generation prediction",

author = "Yang Gao and Piao, {Zai Lin} and Zhang, {Xu Peng} and Lei Dong and Ying Hao",

year = "2010",

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journal = "Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control",

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T1 - Prediction of wind power generation based on ARMA with additive noise model

AU - Gao, Yang

AU - Piao, Zai Lin

AU - Zhang, Xu Peng

AU - Dong, Lei

AU - Hao, Ying

PY - 2010/10/16

Y1 - 2010/10/16

N2 - Because of a mass of factors, forecast quality of ordinary ARMA model will be greatly reduced. Aiming at the condition, the time series of wind power generation from the Fujin Wind Farm located in China and HOYW-based order dertermining method are used to build ARMA with additive noise model. Model applicability is verified by the analysis of the residual error. Applying this new model to predict, the Normalized Mean Absolute Error (NMAE) is 0.0658. Then, the superiority of the new ARMA model is verified during the comparison with the ordinary ARMA model in accuracy of wind power generation prediction.

AB - Because of a mass of factors, forecast quality of ordinary ARMA model will be greatly reduced. Aiming at the condition, the time series of wind power generation from the Fujin Wind Farm located in China and HOYW-based order dertermining method are used to build ARMA with additive noise model. Model applicability is verified by the analysis of the residual error. Applying this new model to predict, the Normalized Mean Absolute Error (NMAE) is 0.0658. Then, the superiority of the new ARMA model is verified during the comparison with the ordinary ARMA model in accuracy of wind power generation prediction.

KW - ARMA model

KW - HOYW method

KW - Model applicability

KW - Noise

KW - Normalized mean absolute error (NMAE)

KW - Wind power generation prediction

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SN - 1674-3415

VL - 38

SP - 164

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JO - Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control

JF - Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control

IS - 20

ER -

Prediction of wind power generation based on ARMA with additive noise model

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