NDLI: A combination prediction model for wind farm output power

Content Provider	IEEE Xplore Digital Library
Author	Feng Jiangxia Liang Jun Wang Chengfu
Copyright Year	2011
Description	Author affiliation: School of Electrical Engineering of Shandong University, Jinan, China (Feng Jiangxia; Liang Jun; Wang Chengfu)
Abstract	Wind power's volatility and intermittence have a profound impact on power system's security and economic operation. However, high-precision power prediction is the important prerequisite to reduce the influence of wind power on the power system. This paper illustrates a wind power prediction model based on time-series and back propagation artificial neural network (BP-ANN), considering wind speed, temperature, humidity, geographical conditions and other factors. Taking account of approximate linear relationship between wind speeds, the prediction model of wind speed was built based on time-series, and the model of wind speed-to-power was set up in the way of the nonlinear mapping relationship based on the method of BP-ANN. The paper predicts wind power based on the measured data of 24h ahead. By analyzing predicted data, it shows that the combined prediction model based on time-series and BP-ANN is effective.
Starting Page	1290
Ending Page	1294
File Size	1024874
Page Count	5
File Format	PDF
ISBN	9781457703645
e-ISBN	9781457703652
DOI	10.1109/DRPT.2011.5994094
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Predictive models Wind power generation Wind speed Data models Autoregressive processes Mathematical model Artificial neural networks BP-ANN wind speed wind power prediction time-series
Content Type	Text
Resource Type	Article

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