scholarly journals Ultra-short-term wind speed forecasting method based on spatial and temporal correlation models

2017 ◽  
Vol 2017 (13) ◽  
pp. 1071-1075 ◽  
Author(s):  
Zhou Haiqiang ◽  
Xue Yusheng ◽  
Guo Jizhu ◽  
Chen Jiehui
Keyword(s):  
Wind Speed ◽  
Temporal Correlation ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Correlation Models ◽  
Forecasting Method

scholarly journals Wind Speed Forecasting Method Using EEMD and the Combination Forecasting Method Based on GPR and LSTM

2018 ◽  
Vol 10 (10) ◽  
pp. 3693 ◽  
Author(s):  
Yuansheng Huang ◽  
Shijian Liu ◽  
Lei Yang
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Wind Speed Prediction ◽  
Forecasting Methods ◽  
Combination Forecasting ◽  
Forecasting Method ◽  
Speed Prediction ◽  
Eemd Method

Short-term wind speed prediction is of cardinal significance for maximization of wind power utilization. However, the strong intermittency and volatility of wind speed pose a challenge to the wind speed prediction model. To improve the accuracy of wind speed prediction, a novel model using the ensemble empirical mode decomposition (EEMD) method and the combination forecasting method for Gaussian process regression (GPR) and the long short-term memory (LSTM) neural network based on the variance-covariance method is proposed. In the proposed model, the EEMD method is employed to decompose the original data of wind speed series into several intrinsic mode functions (IMFs). Then, the LSTM neural network and the GPR method are utilized to predict the IMFs, respectively. Lastly, based on the IMFs’ prediction results with the two forecasting methods, the variance-covariance method can determine the weight of the two forecasting methods and offer a combination forecasting result. The experimental results from two forecasting cases in Zhangjiakou, China, indicate that the proposed approach outperforms other compared wind speed forecasting methods.

scholarly journals Short-Term Wind Speed Hybrid Forecasting Model Based on Bias Correcting Study and Its Application

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Mingfei Niu ◽  
Shaolong Sun ◽  
Jie Wu ◽  
Yuanlei Zhang
Keyword(s):  
Wind Speed ◽  
Wind Farm ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Time Optimization ◽  
Combination Forecasting ◽  
Dynamic Uncertainty ◽  
Forecasting Method ◽  
Hybrid Forecasting ◽  
Real Time Optimization

The accuracy of wind speed forecasting is becoming increasingly important to improve and optimize renewable wind power generation. In particular, reliable short-term wind speed forecasting can enable model predictive control of wind turbines and real-time optimization of wind farm operation. However, due to the strong stochastic nature and dynamic uncertainty of wind speed, the forecasting of wind speed data using different patterns is difficult. This paper proposes a novel combination bias correcting forecasting method, which includes the combination forecasting method and forecasting bias correcting model. The forecasting result shows that the combination bias correcting forecasting method can more accurately forecast the trend of wind speed and has a good robustness.

scholarly journals Meta Learning-Based Hybrid Ensemble Approach for Short-Term Wind Speed Forecasting

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 172859-172868
Author(s):  
Zhengwei Ma ◽  
Sensen Guo ◽  
Gang Xu ◽  
Saddam Aziz
Keyword(s):  
Wind Speed ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Ensemble Approach ◽  
Meta Learning

Very short-term wind speed forecasting with Bayesian structural break model

2013 ◽  
Vol 50 ◽  
pp. 637-647 ◽  
Author(s):  
Yu Jiang ◽  
Zhe Song ◽  
Andrew Kusiak
Keyword(s):  
Wind Speed ◽  
Structural Break ◽  
Short Term ◽  
Wind Speed Forecasting
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