Deep Learning for Wind Speed Forecasting Using Bi-LSTM with Selected Features  

作　　者：Siva Sankari Subbiah Senthil Kumar Paramasivan Karmel Arockiasamy Saminathan Senthivel Muthamilselvan Thangavel 

机构地区：[1]Department of Computer Science and Engineering,Sreenivasa Institute of Technology and Management Studies,Chittoor,517127,India [2]School of Information Technology&Engineering,Vellore Institute of Technology,Vellore,632014,India [3]School of Computer Science and Engineering,Vellore Institute of Technology,Chennai,600127,India [4]School of Computing,College of Engineering and Technology,SRM Institute of Science and Technology,Kattankulathur,603203,India

出　　处：《Intelligent Automation & Soft Computing》2023年第3期3829-3844,共16页智能自动化与软计算（英文）

摘　　要：Wind speed forecasting is important for wind energy forecasting.In the modern era,the increase in energy demand can be managed effectively by fore-casting the wind speed accurately.The main objective of this research is to improve the performance of wind speed forecasting by handling uncertainty,the curse of dimensionality,overfitting and non-linearity issues.The curse of dimensionality and overfitting issues are handled by using Boruta feature selec-tion.The uncertainty and the non-linearity issues are addressed by using the deep learning based Bi-directional Long Short Term Memory(Bi-LSTM).In this paper,Bi-LSTM with Boruta feature selection named BFS-Bi-LSTM is proposed to improve the performance of wind speed forecasting.The model identifies relevant features for wind speed forecasting from the meteorological features using Boruta wrapper feature selection(BFS).Followed by Bi-LSTM predicts the wind speed by considering the wind speed from the past and future time steps.The proposed BFS-Bi-LSTM model is compared against Multilayer perceptron(MLP),MLP with Boruta(BFS-MLP),Long Short Term Memory(LSTM),LSTM with Boruta(BFS-LSTM)and Bi-LSTM in terms of Root Mean Square Error(RMSE),Mean Absolute Error(MAE),Mean Square Error(MSE)and R2.The BFS-Bi-LSTM surpassed other models by producing RMSE of 0.784,MAE of 0.530,MSE of 0.615 and R2 of 0.8766.The experimental result shows that the BFS-Bi-LSTM produced better forecasting results compared to others.

关 键 词：Bi-directional long short term memory boruta feature selection deep learning machine learning wind speed forecasting 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]