NDLI: GARCH in mean type models for wind power forecasting

Content Provider	IEEE Xplore Digital Library
Author	Hao Chen Qiulan Wan Fangxing Li Yurong Wang
Copyright Year	2013
Description	Author affiliation: Jiangsu Electr. Power Co., Nanjing, China (Hao Chen) \|\| Southeast Univ., Nanjing, China (Qiulan Wan; Yurong Wang) \|\| Univ. of Tennessee, Knoxville, TN, USA (Fangxing Li)
Abstract	Wind power penetration to power system is increasing with a very fast speed. Study on wind power forecasting is beneficial to the stable operation and economic dispatch of power systems. To improve the forecasting performance of wind power, it is necessary to investigate on the intrinsic characteristics of wind power. As the most recognized characteristics of wind power, volatility and intermittency are widely concerned. In this paper, GARCH in mean (Generalized Autoregressive Conditional Heteroskedasticity in mean) type models are presented for wind power forecasting, and the impacts of volatility and intermittency to wind power time series is modeled in the mean equation of the forecasting model. With the help of GARCH in mean effect curve, the negative impact of volatility to wind power is highlighted. By means of the Conditional Maximum Likelihood Estimation (CMLE) method, the parameters are estimated for all the proposed models. In case study, wind power forecasting based on the two types of proposed models are carried out using the historical coastal wind power data of East China. Compared with the time persistence model, Auto-regressive Moving Average (ARMA) model and GARCH model, the proposed GARCH in mean type models are validated to be effective and outperform the classical wind power forecasting models.
Sponsorship	IEEE Power Energy Soc.
Starting Page	1
Ending Page	5
File Size	604155
Page Count	5
File Format	PDF
ISBN	9781479913039
ISSN	19449925
DOI	10.1109/PESMG.2013.6672237
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-21
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Biological system modeling Predictive models Forecasting Silicon carbide Wind power generation Sea measurements Indexes Term ARMA GARCH PARCH GARCH-M PARCH-M Wind Power Forecasting Volatility Compensation
Content Type	Text
Resource Type	Article

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