NDLI: Short-term wind generation forecasting and confidence interval estimation based on neural networks trained by extended Kalman particle filter

Content Provider	IEEE Xplore Digital Library
Author	Che Guan Luh, P.B. Wen Cao
Copyright Year	2011
Description	Author affiliation: Department of Electrical and Computer Engineering, University of Connecticut, Storrs, 06269, USA (Che Guan; Luh, P.B.) \|\| Stern Business School, New York University, 10012, USA (Wen Cao)
Abstract	Short-term wind generation forecasting predicts wind power 24-hours into the future in hourly steps. Effective forecasting is important for reliability, electricity markets and transmission grids. It is however difficult in view of the intermittent nature of the wind generation. We previously presented a method of neural networks trained by extended Kalman filter. In this paper, the method of the neural networks trained by extended Kalman particle filter is developed to capture the stochastic feature of the wind generation. The key idea is to take differencing transformation on the wind generation since differenced data has the student t-distribution based on the data analysis. The method of neural network trained by extended Kalman particle filter is then developed. It runs several neural networks trained by extended Kalman filter in parallel, and dynamically weights them based on the particle filter algorithm. Because the forecasting accuracies can be volatile, the method of generalized autoregressive conditional heteroscedastic is used to model the changes in residuals to improve predictions. Individual predictions from each neural network are then statistically combined to form the final forecasting. To accurately estimate the confidence interval, the dynamic covariance matrices are derived based on the differencing transformation. The overall dynamic covariance matrix is calculated by statistically combining all the covariance matrices. Numerical testing based on EIRGRID and ISO New England data demonstrates the significant values of our methods.
Starting Page	1173
Ending Page	1179
File Size	1602247
Page Count	7
File Format	PDF
ISBN	9781612846989
e-ISBN	9781612847009
DOI	10.1109/WCICA.2011.5970701
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-21
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wind forecasting Artificial neural networks Forecasting Covariance matrix Kalman filters Wind power generation Prediction algorithms short-term wind generation forecasting Confidence interval estimation generalized autoregressive conditional heteroskedastic neural network trained by extended Kalman particle filter
Content Type	Text
Resource Type	Article

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