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

^*此作品的通讯作者

自动化学院

Shenyang Agricultural University
Shenyang Institute of Engineering
Liaoning Power Survey and Design Institute
Beijing Institute of Technology

科研成果: 期刊稿件 › 文章 › 同行评审

13 引用（Scopus）

摘要

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.

源语言	英语
页（从-至）	164-167
页数	4
期刊	Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control
卷	38
期	20
出版状态	已出版 - 16 10月 2010

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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",

month = oct,

day = "16",

language = "English",

volume = "38",

pages = "164--167",

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

issn = "1674-3415",

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TY - JOUR

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

UR - https://www.scopus.com/pages/publications/78649379917

M3 - Article

AN - SCOPUS:78649379917

SN - 1674-3415

VL - 38

SP - 164

EP - 167

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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