论文标题
风速预测和可视化使用长期短期存储网络（LSTM）
Wind Speed Prediction and Visualization Using Long Short-Term Memory Networks (LSTM)
论文作者
论文摘要
气候变化是本世纪最令人关注的问题之一。发电是至关重要的因素，它将关注点推向下一个水平。可再生能源是广泛的，在全球范围内可用，但是，主要挑战之一是以更有信息的方式了解其特征。本文提出了简化风电场计划和可行性研究的风速的预测。十二种人工智能算法用于收集的气象参数的风速预测。比较模型性能以确定风速预测精度。结果表明，深度学习方法，长期记忆（LSTM）的表现优于其他模型，其精度最高为97.8％。
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%.
