A Gaussian process regression based hybrid approach for short-term wind speed prediction

2016 ◽  
Vol 126 ◽  
pp. 1084-1092 ◽  
Author(s):  
Chi Zhang ◽  
Haikun Wei ◽  
Xin Zhao ◽  
Tianhong Liu ◽  
Kanjian Zhang
Keyword(s):  
Wind Speed ◽  
Gaussian Process ◽  
Hybrid Approach ◽  
Gaussian Process Regression ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Speed Prediction

A novel hybrid approach based on dynamic adaptive variable-weight optimization for short-term wind speed prediction

2020 ◽  
Vol 12 (1) ◽  
pp. 016101
Author(s):  
Yuansheng Huang ◽  
Lei Yang ◽  
Yingqi Yang ◽  
Yulin Dong ◽  
Chong Gao
Keyword(s):  
Wind Speed ◽  
Hybrid Approach ◽  
Weight Optimization ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Variable Weight ◽  
Speed Prediction

Gaussian Process Regression for numerical wind speed prediction enhancement

2020 ◽  
Vol 146 ◽  
pp. 2112-2123 ◽  
Author(s):  
Haoshu Cai ◽  
Xiaodong Jia ◽  
Jianshe Feng ◽  
Wenzhe Li ◽  
Yuan-Ming Hsu ◽  
...  
Keyword(s):  
Wind Speed ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Wind Speed Prediction ◽  
Speed Prediction

scholarly journals A Hybrid Approach for Short-term Wind Speed Prediction in Huan County of China

2019 ◽  
Vol 295 ◽  
pp. 012030 ◽  
Author(s):  
Tonglin Fu ◽  
Mingxia Yang ◽  
Pengyue Ju ◽  
Kun Liu ◽  
Huani Zhao
Keyword(s):  
Wind Speed ◽  
Hybrid Approach ◽  
Short Term ◽  
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.

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