深度学习
计算机科学
人工智能
风速
机器学习
风力发电
光学(聚焦)
财产(哲学)
数据建模
加速
工程类
气象学
操作系统
电气工程
认识论
光学
物理
哲学
数据库
作者
Vikram Bali,Ajay Kumar,Satyam Gangwar
标识
DOI:10.1109/confluence.2019.8776923
摘要
Wind speed forecasting is the term used for predicting speed of wind to generate wind power. Deep learning, which is the subfield of machine learning and is used to implement on a large data sets and predictions made using deep learning with LSTM can increases the accuracy rate to the great extent. The combination of deep learning with LSTM can enhances the prediction rate as due to the property of LSTM of pattern remembrance for longer duration of time. This survey discusses the existing functionality measures of different approaches by partitioning them into various methodologies: models of very small time gap, small time gap, and longtime gap. All these approaches include certain models with various parameters, advantages and disadvantages are discussed. The focus of this survey is to present a better and efficient evaluation of various approaches to help the researcher to select best model out of all present models.
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