NDLI: Short-term prediction of wind farm output using the recurrent quadratic volterra model

Content Provider	IEEE Xplore Digital Library
Author	Duehee Lee
Copyright Year	2011
Description	Author affiliation: Department of Electrical and Computer Engineering, the University of Texas at Austin, 2501 Speedway, Austin, TX, 78712, USA (Duehee Lee)
Abstract	This paper presents a way to use the recurrent quadratic Volterra system to forecast the wind power output. The recurrent quadratic Volterra system is a second-order polynomial equation that uses the output data as feedback recursively. The Volterra system is extracted from the weights of the Recurrent Neural Network. During this process, three innovative techniques are used. In order to make Volterra kernels from the combination of weights, the activation function is approximated to the high-order polynomial function by using the Lagrangian interpolation. Furthermore, the memory of the Volterra system is also identified using the Partial Autocorrelation Function. After building the Volterra system, the 15 and 30-minutes ahead of wind power output is forecasted with confidence intervals at the 95% confidence level. The confidence interval is calculated using the multi-linear regression techniques. The stability of the recurrent Volterra system is also considered by the heuristic method.
Starting Page	1
Ending Page	8
File Size	596145
Page Count	8
File Format	PDF
ISBN	9781457710001
ISSN	19449925
e-ISBN	9781457710025
e-ISBN	9781457710018
DOI	10.1109/PES.2011.6039128
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-24
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Wind power generation Polynomials Wind forecasting Predictive models Forecasting Neurons Short-Term Forecasting Volterra system Recurrent Neural Network Wind Power System
Content Type	Text
Resource Type	Article

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