Short-term wind speed forecasting using neural network models for Taiwan Strait. Issue 6 (December 2022)

• Record Type: Journal Article
• Authors: Soon, Yee-Sheng; Lu, Chao-hong; Tu, Qian-cheng; Lin, Ta-Hui
• Is Part Of: Wind engineering. Volume 46:Issue 6(2022)
• Page Start: 1792
• Page End: 1805
• Publication Date: 2022-12
• Subjects: Offshore wind power -- wind forecasting -- long short-term memory (LSTM); Wind-pressure -- Periodicals; Winds -- Periodicals; Wind power -- Periodicals; Engineering meteorology -- Periodicals; Pression du vent; Vents; Énergie éolienne; Météorologie appliquée; Engineering meteorology; Wind power; Wind-pressure; Winds; Periodicals; 621.4505
• DOI: 10.1177/0309524X221108423
• Languages: English
• ISSNs: 0309-524X

Abstract

In recent years, renewable energy has received rapidly growing attention due to its eco-friendly and sustainable properties. Taiwan as an island nation that is planning to develop offshore wind power to reduce the dependence on imported energy. Due to the intermittent and variable nature of wind, a study on wind characteristics and forecasting will make it possible to obtain valuable information on local wind conditions and enhance local forecasting abilities. In this study, wind data from 2017 to 2019, obtained from the Taipower Meteorological Mast in the Taiwan Strait, was used to develop a short-term multistep wind forecasting model. This model was based on a combination of an artificial neural network and a Long Short-Term Memory (LSTM) models. The results revealed that the northeast winds in winter and autumn were steadier, in terms of both speed and direction, than those in spring and summer. The prediction accuracies of this three-step forecasting model reached 0.991, 0.981, and 0.970, respectively. These findings will greatly improve our ability to forecast this important Taiwan Strait wind resource.