Short-term wind speed forecasting using neural network models for Taiwan Strait

Yee Sheng Soon, Chao Hong Lu, Qian Cheng Tu, Ta Hui Lin

研究成果: Article同行評審

1 引文 斯高帕斯(Scopus)


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.

頁(從 - 到)1792-1805
期刊Wind Engineering
出版狀態Published - 2022 12月

All Science Journal Classification (ASJC) codes

  • 可再生能源、永續發展與環境
  • 能源工程與電力技術


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