Wind Speed Prediction and Visualization Using Long Short-Term Memory Networks (LSTM)

被引：0

作者：

Ehsan, Amimul ^{[1
]}

Shahirinia, Amir ^{[1
]}

Zhang, Nian ^{[1
]}

Oladunni, Timothy ^{[2
]}

机构：

[1] Univ Dist Columbia, Dept Elect & Comp Engn, Washington, DC 20008 USA

[2] Univ Dist Columbia, Dept Comp Sci & Informat Technol, Washington, DC 20008 USA

来源：

2020 10TH INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE AND TECHNOLOGY (ICIST) | 2020年

基金：

美国国家科学基金会;

关键词：

wind speed prediction; deep learning; convolutional neural networks; long short-term memory (LSTM); LEARNING-MODEL; DECOMPOSITION;

D O I：

10.1109/icist49303.2020.9202300

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Climate change is one of the most concerning issues of this century. Emission from electric power generation is a crucial factor that drives the concern to the next level. Renewable energy sources are widespread and available globally, however, one of the major challenges is to understand their characteristics in a more informative way. This paper proposes the prediction of wind speed that simplifies wind farm planning and feasibility study. Twelve artificial intelligence algorithms were used for wind speed prediction from collected meteorological parameters. The model performances were compared to determine the wind speed prediction accuracy. The results show a deep learning approach, long short-term memory (LSTM) outperforms other models with the highest accuracy of 97.8%.

引用

页码：234 / 240

页数：7

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