scholarly journals Wind Speed Prediction Model Using LSTM and 1D-CNN

2018 ◽  
Vol 22 (4) ◽  
pp. 207-210 ◽  
Author(s):  
Rui Fukuoka ◽  
Hiroshi Suzuki ◽  
Takahiro Kitajima ◽  
Akinobu Kuwahara ◽  
Takashi Yasuno
Keyword(s):  
Wind Speed ◽  
Prediction Model ◽  
Wind Speed Prediction ◽  
Speed Prediction

An application of backtracking search optimization–based least squares support vector machine for prediction of short-term wind speed

2019 ◽  
Vol 44 (3) ◽  
pp. 266-281 ◽  
Author(s):  
Zhongda Tian ◽  
Yi Ren ◽  
Gang Wang
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Prediction Model ◽  
Least Squares ◽  
Support Vector ◽  
Short Term ◽  
Backtracking Search ◽  
Wind Speed Prediction ◽  
Search Optimization ◽  
Speed Prediction

Wind speed prediction is an important technology in the wind power field; however, because of their chaotic nature, predicting wind speed accurately is difficult. Aims at this challenge, a backtracking search optimization–based least squares support vector machine model is proposed for short-term wind speed prediction. In this article, the least squares support vector machine is chosen as the short-term wind speed prediction model and backtracking search optimization algorithm is used to optimize the important parameters which influence the least squares support vector machine regression model. Furthermore, the optimal parameters of the model are obtained, and the short-term wind speed prediction model of least squares support vector machine is established through parameter optimization. For time-varying systems similar to short-term wind speed time series, a model updating method based on prediction error accuracy combined with sliding window strategy is proposed. When the prediction model does not match the actual short-term wind model, least squares support vector machine trains and re-establishes. This model updating method avoids the mismatch problem between prediction model and actual wind speed data. The actual collected short-term wind speed time series is used as the research object. Multi-step prediction simulation of short-term wind speed is carried out. The simulation results show that backtracking search optimization algorithm–based least squares support vector machine model has higher prediction accuracy and reliability for the short-term wind speed. At the same time, the prediction performance indicators are also improved. The prediction result is that root mean square error is 0.1248, mean absolute error is 0.1374, mean absolute percentile error is 0.1589% and R2 is 0.9648. When the short-term wind speed varies from 0 to 4 m/s, the average value of absolute prediction error is 0.1113 m/s, and average value of absolute relative prediction error is 8.7111%. The proposed prediction model in this article has high engineering application value.

scholarly journals A Novel Self-Adaptive Wind Speed Prediction Model Considering Atmospheric Motion and Fractal Feature

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 215892-215903
Author(s):  
Ji Jin ◽  
Bin Wang ◽  
Min Yu ◽  
Jiang Liu ◽  
Wenbo Wang
Keyword(s):  
Wind Speed ◽  
Prediction Model ◽  
Wind Speed Prediction ◽  
Speed Prediction ◽  
Atmospheric Motion ◽  
Self Adaptive

Short-term wind speed prediction model based on GA-ANN improved by VMD

2020 ◽  
Vol 156 ◽  
pp. 1373-1388 ◽  
Author(s):  
Yagang Zhang ◽  
Guifang Pan ◽  
Bing Chen ◽  
Jingyi Han ◽  
Yuan Zhao ◽  
...  
Keyword(s):  
Wind Speed ◽  
Prediction Model ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Model Based ◽  
Speed Prediction

Wind Speed Prediction Based on Improved Grey Neural Network Model

2014 ◽  
Vol 548-549 ◽  
pp. 1235-1240
Author(s):  
Bin Zeng ◽  
Jian Xiao Zou ◽  
Kai Li ◽  
Xiao Shuai Xin
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Prediction Model ◽  
Data Storage ◽  
Wind Farm ◽  
Grey System Theory ◽  
Grey System ◽  
Wind Speed Prediction ◽  
Neural Network Prediction ◽  
Speed Prediction

Wind speed forecasting is an effective method to improve power stability of wind farm. Grey system theory have certain advantages in the study of poor information and uncertainty problems, it is suitable for the system with limited computing power and data storage capacity, such as wind turbine control system. In order to further improve the prediction accuracy of grey model, we combined GM (1, 1) model and BP neural network prediction model in this paper, and improved the combined model by background value optimizing and introducing genetic algorithm. Through analyzing the simulation results and comparing the forecasting results with the actual wind speed, it is clear that the improved combined prediction model is superior to pure grey forecasting model and it meets the needs of the wind power control.

Sign in / Sign up

Export Citation Format

Share Document