The role of regimes in short-term wind speed forecasting at multiple wind farms

Stat ◽  
2015 ◽  
Vol 4 (1) ◽  
pp. 271-290 ◽  
Author(s):  
Karen Kazor ◽  
Amanda S. Hering
Keyword(s):  
Wind Speed ◽  
Wind Farms ◽  
Short Term ◽  
Wind Speed Forecasting

scholarly journals Short-Term Wind Speed Forecasting Based on Information of Neighboring Wind Farms

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 16760-16770 ◽  
Author(s):  
Zhongju Wang ◽  
Jing Zhang ◽  
Yu Zhang ◽  
Chao Huang ◽  
Long Wang
Keyword(s):  
Wind Speed ◽  
Wind Farms ◽  
Short Term ◽  
Wind Speed Forecasting

scholarly journals Short-Term Wind Speed Forecasting Using Decomposition-Based Neural Networks Combining Abnormal Detection Method

2014 ◽  
Vol 2014 ◽  
pp. 1-21
Author(s):  
Xuejun Chen ◽  
Jing Zhao ◽  
Wenchao Hu ◽  
Yufeng Yang
Keyword(s):  
Neural Networks ◽  
Wind Speed ◽  
Weighted Average ◽  
Wind Farms ◽  
Ensemble Empirical Mode Decomposition ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Hybrid Approaches ◽  
Mode Decomposition ◽  
Nonlinear Autoregressive

As one of the most promising renewable resources in electricity generation, wind energy is acknowledged for its significant environmental contributions and economic competitiveness. Because wind fluctuates with strong variation, it is quite difficult to describe the characteristics of wind or to estimate the power output that will be injected into the grid. In particular, short-term wind speed forecasting, an essential support for the regulatory actions and short-term load dispatching planning during the operation of wind farms, is currently regarded as one of the most difficult problems to be solved. This paper contributes to short-term wind speed forecasting by developing two three-stage hybrid approaches; both are combinations of the five-three-Hanning (53H) weighted average smoothing method, ensemble empirical mode decomposition (EEMD) algorithm, and nonlinear autoregressive (NAR) neural networks. The chosen datasets are ten-minute wind speed observations, including twelve samples, and our simulation indicates that the proposed methods perform much better than the traditional ones when addressing short-term wind speed forecasting problems.

Evolutionary product unit neural networks for short-term wind speed forecasting in wind farms

2011 ◽  
Vol 21 (5) ◽  
pp. 993-1005 ◽  
Author(s):  
C. Hervás-Martínez ◽  
S. Salcedo-Sanz ◽  
P. A. Gutiérrez ◽  
E. G. Ortiz-García ◽  
L. Prieto
Keyword(s):  
Neural Networks ◽  
Wind Speed ◽  
Wind Farms ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Evolutionary Product

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

Bayesian Optimization Based ANN Model for Short Term Wind Speed Forecasting in Newfoundland, Canada

2020 ◽  
Author(s):  
Habibur Rahaman ◽  
T. M. Rubaith Bashar ◽  
Mohammad Munem ◽  
Md. Hasibul Hasan Hasib ◽  
Hasan Mahmud ◽  
...  
Keyword(s):  
Wind Speed ◽  
Bayesian Optimization ◽  
Ann Model ◽  
Short Term ◽  
Wind Speed Forecasting
Sign in / Sign up

Export Citation Format

Share Document