Open AccessA Wind Speed Prediction Model Based on ARIMA and Improved Kalman Filter Algorithm
Author(s) -
Yuan Sheng-yuan,
Yanxia Shen
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1650/3/032095
Subject(s) - kalman filter , autoregressive integrated moving average , autoregressive model , computer science , weighting , wind speed , algorithm , series (stratigraphy) , time series , control theory (sociology) , extended kalman filter , mathematics , artificial intelligence , statistics , machine learning , meteorology , medicine , paleontology , physics , control (management) , biology , radiology
This article is dedicated to solving the problem of wind speed prediction. A time series analysis method based on the establishment of a differential autoregressive sliding model for simple training and a prediction model to obtain the state is proposed. The Kalman filter method with adaptive weighting coefficients is used to predict the equation, and the experimental results show that the composite algorithm can effectively reduce the prediction error.