Wind Speed Prediction and Visualization Using Long Short-Term Memory Networks (LSTM)
Wind Speed Prediction and Visualization Using Long Short-Term Memory Networks (LSTM)
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DOI：
10.1109/icist49303.2020.9202300
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发表时间：
2020-05
期刊：
影响因子：
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通讯作者：
Md. Amimul Ehsan;A. Shahirinia;N. Zhang;T. Oladunni
中科院分区：
文献类型：
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作者：
Md. Amimul Ehsan;A. Shahirinia;N. Zhang;T. Oladunni
气候变化是本世纪最受关注的问题之一。发电的排放是一个关键因素，将人们的关注推向了下一个层次。可再生能源在全球范围内广泛可用，然而，主要挑战之一是以更翔实的方式了解其特征。本文提出了简化风电场规划和可行性研究的风速预测方法。利用12种人工智能算法对收集到的气象参数进行风速预测。比较了模型的性能，确定了风速预测的精度。结果表明，采用深度学习方法，长短期记忆（LSTM）以97.8%的最高准确率优于其他模型。
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%.