Journal ArticleDOI
Locally recurrent neural networks for long-term wind speed and power prediction
TLDR
Experimental results on the wind prediction problem demonstrate that the proposed algorithms exhibit enhanced performance, in terms of convergence speed and the accuracy of the attained solutions, compared to conventional gradient-based methods.About:
This article is published in Neurocomputing.The article was published on 2006-01-01. It has received 128 citations till now. The article focuses on the topics: Recurrent neural network & Wind speed.read more
Citations
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Journal ArticleDOI
A review on the forecasting of wind speed and generated power
TL;DR: A bibliographical survey on the general background of research and developments in the fields of wind speed and wind power forecasting and further direction for additional research and application is proposed.
Journal ArticleDOI
Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model
TL;DR: The developed model shows the best accuracy comparing with basic FNN and unmodified EMD-based FNN through multi-step forecasting the mean monthly and daily wind speed in Zhangye of China.
Journal ArticleDOI
Optimal parameters selection for BP neural network based on particle swarm optimization: A case study of wind speed forecasting
TL;DR: A Back Propagation neural network based on Particle Swam Optimization that combines PSO-BP with comprehensive parameter selection is introduced that achieves much better forecast performance than the basic back propagation neural network and ARIMA model.
Journal ArticleDOI
Transfer learning for short-term wind speed prediction with deep neural networks
TL;DR: This paper introduces deep neural networks, trained by data from data-rich farms, to extract wind speed patterns, and finely tune the mapping with data coming from newly-built farms, and shows that prediction errors are significantly reduced using the proposed technique.
References
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Journal ArticleDOI
A learning algorithm for continually running fully recurrent neural networks
Ronald J. Williams,David Zipser +1 more
TL;DR: The exact form of a gradient-following learning algorithm for completely recurrent networks running in continually sampled time is derived and used as the basis for practical algorithms for temporal supervised learning tasks.
Book
Theory and Practice of Recursive Identification
Lennart Ljung,Torsten Söderström +1 more
TL;DR: Methods of recursive identification deal with the problem of building mathematical models of signals and systems on-line, at the same time as data is being collected.