 Complex-valued forecasting of wind profileS.L. Goh, M. Chen, D.H. Popović, K. Aihara, D. Obradovic and D.P. Mandic Renewable Energy, 2006, vol. 31, issue 11, 1733-1750 Abstract: This paper presents a novel approach for the simultaneous modelling and forecasting of wind signal components. This is achieved in the complex domain by using novel neural network algorithms and architectures. We first perform a signal nonlinearity and component-dependent analyses, which suggest the use of modular complex-valued recurrent neural networks (RNNs). This RNN-based modelling rests upon a combination of nonlinearity, complexity and internal memory and allows for the multiple step ahead forecasting of the wind signal in its complex form (speed and direction). The approach is first verified on benchmark Data Set A (NH3 laser data) of the Santa Fe Time Series Prediction Competition together with artificial data generated by chaotic Mackey–Glass equations, and then applied to the real-world wind measurements. Simulations support the proposed architecture and algorithms. Keywords: Wind forecasting; Complex-valued representation; Recurrent neural networks; Pipelined recurrent neural network; Delay vector variance (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:31:y:2006:i:11:p:1733-1750 DOI: 10.1016/j.renene.2005.07.006 Access Statistics for this article Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides More articles in Renewable Energy from Elsevier |
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