High resolution wind speed forecasting based on wavelet decomposed phase space reconstruction and self-organizing map

Rui Hu, Weihao Hu, Nuri Gökmen, Pengfei Li, Huang Qi and Zhe Chen

Renewable Energy, 2019, vol. 140, issue C, 17-31

Abstract: Wind power has already brought lots of benefits for energy market and environment. To promote the utilization of wind energy further and achieve the ambitious goals of applying sustainable energy, the improvement of the performance of wind speed forecasting will play a significant role. The whole system will benefit from an accurate wind forecasting system with high resolution. This paper focused on short-term wind speed forecasting and proposed a method providing the wind speed forecasting with short-term horizon and high resolution. In this paper, a novel method based on the wavelet decomposition, the phase space reconstruction and the self-organizing map (SOM) artificial neural network was proposed. Study case was provided to demonstrate the performance of the proposed forecasting method. Comparative results from several classic forecasting methods were provided as well. Based on the outcomes of study cases and comparison results, it can be concluded that the proposed method performed well in short term forecasting horizon.

Keywords: Wind speed forecasting; Short-term; High-resolution; Phase space reconstruction; Paralleling computing; Self-organizing map (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (15)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:140:y:2019:i:c:p:17-31

DOI: 10.1016/j.renene.2019.03.041

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