NDLI: Wind power plant prediction by using neural networks

Content Provider	IEEE Xplore Digital Library
Author	Ziqiao Liu Wenzhong Gao Yih-Huei Wan Muljadi, E.
Copyright Year	2012
Description	Author affiliation: Department of Electrical and Computer Engineering, University of Denver, CO, 80210 USA (Ziqiao Liu; Wenzhong Gao) \|\| National Renewable Energy Laboratory, Golden, CO 80401, USA (Yih-Huei Wan; Muljadi, E.)
Abstract	This paper introduces a method of short term wind power prediction for a wind power plant by training neural networks based on historical data of wind speed and wind direction. There are two steps in the process of wind power prediction. In the first step, raw data collected by plant information system is filtered by probabilistic neural network. This step prepares valid data to be used for building a prediction model. In the second step, a complex-valued recurrent neural network is applied to build a model to predict wind power. The test results of the prediction model are presented and analyzed at the end of the paper. The model proposed is shown to achieve a high accuracy with respect to the measured data.
Starting Page	3154
Ending Page	3160
File Size	1031137
Page Count	7
File Format	PDF
ISBN	9781467308021
e-ISBN	9781467308038
e-ISBN	9781467308014
DOI	10.1109/ECCE.2012.6342351
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-09-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Probabilistic neural network Wind power plant Wind power prediction Wind speed Neural networks Wind power generation Predictive models Data models Wind turbines Complex-valued recurrent neural network
Content Type	Text
Resource Type	Article

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