水准点(测量)
均方误差
超参数
卷积神经网络
计算机科学
人工神经网络
风速
超参数优化
循环神经网络
人工智能
相关系数
模式识别(心理学)
机器学习
支持向量机
数学
统计
气象学
物理
大地测量学
地理
作者
Zhipeng Shen,Xuechun Fan,Liangyu Zhang,Haomiao Yu
标识
DOI:10.1016/j.oceaneng.2022.111352
摘要
Wind speed is a key factor for unmanned sailboats, and accurate prediction of wind speed is of great significance to the safety and performance of unmanned sailboats. In this study, a novel hybrid neural network scheme based on convolutional neural network (CNN) and long short-term memory (LSTM) is proposed for multi-step wind speed prediction. The scheme consists of two parts: a data processing module and a model module. We improved the grid search method to determine the selection of learning rate and input length hyperparameters. Simulations were performed on three different data sets and four types of other benchmark models were developed for comparison with the CNN-LSTM, such as recurrent neural network (RNN) and LSTM model, etc. The forecasts are evaluated by looking at the mean absolute error (MAE), root mean squared error (RMSE), correlation coefficient (CC) and R squared (R2). The evaluation metrics showed that the MAE and RMSE of CNN-LSTM are lower than the other benchmark models most of the time, while both CC and R2 are higher than the other models, which means the CNN-LSTM performs better accuracy and stability. It is accurate enough to provide a reliable wind input to the unmanned sailboat control system.
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