[1]余聪聪,熊才权,徐仕强,等. 基于改进SARIMA-LSTM的海上风速预测方法[J].湖北工业大学学报,2022,(1):23-28+53.
　YU Congcong,XIONG Caiquan,XU Shiqiang,et al. Research on Forecasting Method of Sea Wind Speed Based on Improved SARIMA-LSTM[J].,2022,(1):23-28+53.
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 基于改进SARIMA-LSTM的海上风速预测方法()
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《湖北工业大学学报》[ISSN:1003-4684/CN:42-1752/Z]

卷:
期数:
2022年第1期
页码:
23-28+53
栏目:
湖北工业大学学报
出版日期:
2022-02-28

文章信息/Info

Title:
 Research on Forecasting Method of Sea Wind Speed Based on Improved SARIMA-LSTM
文章编号:
1003-4684(2022)01-0024-05
作者:
 余聪聪;  熊才权;  徐仕强;  古小惠
 湖北工业大学计算机学院， 湖北 武汉 430068
Author(s):
 YU Congcong; XIONG Caiquan;  XU Shiqiang;  GU Xiaohui
 School of Computer Science, Hubei Univ. of Tech., Wuhan 430068, China
关键词:
 ; 海上风速预测;  差分步长; 季节性差分自回归移动平均
Keywords:
 ; maritime wind speed;  predictive analysis;  LSTM;  SARIMA
分类号:
TP18
文献标志码:
A
摘要:
 为了提高海上风速预测的精度，提出了一种基于局部加权回归的周期趋势分解（STL）改进的季节性差分自回归移动平均模型（SARIMA）和长短时记忆（LSTM）神经网络的海上风速预测方法。首先通过STL分解原始风速时间序列，提高SARIMA模型季节性差分步长的准确性，再使用SARIMA模型对观测的风速序列数据进行预测，得到预测值以及预测值与观测值之间的残差；然后用残差样本集训练长短时记忆神经网络并对残差进行预测；最后将两部分得到的预测值求和得到风速序列的预测值。选定3个不同地点分别进行仿真实验并与改进前方法进行比较，结果表明改进后模型的预测精度更高，误差更小。
Abstract:
 In order to improve the accuracy of offshore wind speed prediction, a model of sea wind speed prediction based on STL improved Seasonal Autoregressive Integrated Moving Average Model (SARIMA) and Long Shortterm Memory (LSTM) neural network is proposed in this paper. First, the accuracy of seasonal difference step size based on SARIMA model through STL decomposition is improved and the SARIMA model is used to predict the observed wind speed series data to obtain the predicted value and the residual between the predicted value and the observed value; the residual sample is then used set to train the LSTM neural network and predict the residual; finally, the two predicted values are summed to obtain the predicted value of the wind speed series. Three different locations are selected for experiments and comparison with the method before the improvement. The experimental results show that the prediction accuracy of the improved method is higher, and the error is smaller.

参考文献/References:

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备注/Memo

备注/Memo:
［收稿日期］ 2021-07-03
［基金项目］ 国家重点研发计划（2017YFC1405403）;国家自然科学基金(61075059); 湖北工业大学绿色工业科技引领计划（CPYF2017008）
［第一作者］ 余聪聪（1995-），男，湖北黄冈人，湖北工业大学硕士研究生，研究方向为人工智能，计算机仿真
［通信作者］ 熊才权（1966-），男，湖北鄂州人，工学博士，湖北工业大学教授，研究方向为人工智能，辩论模型
更新日期/Last Update: 2022-02-25