Short-term wind speed forecasting in wind farm based on C-C and ELM

2017 ◽  
Author(s):  
Dongshuai Kang ◽  
Yingying Su ◽  
Xinghua Liu ◽  
Huabin Wang ◽  
Cuiying Li ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Farm ◽  
Short Term ◽  
Wind Speed Forecasting

A deep learning-based evolutionary model for short-term wind speed forecasting: A case study of the Lillgrund offshore wind farm

2021 ◽  
Vol 236 ◽  
pp. 114002
Author(s):  
Mehdi Neshat ◽  
Meysam Majidi Nezhad ◽  
Ehsan Abbasnejad ◽  
Seyedali Mirjalili ◽  
Lina Bertling Tjernberg ◽  
...  
Keyword(s):  
Deep Learning ◽  
Wind Speed ◽  
Wind Farm ◽  
Evolutionary Model ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Short Term ◽  
Wind Speed Forecasting

scholarly journals Multistep Wind Speed Forecasting Based on Wavelet and Gaussian Processes

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Niya Chen ◽  
Zheng Qian ◽  
Xiaofeng Meng
Keyword(s):  
Wind Speed ◽  
Wavelet Decomposition ◽  
Wind Farm ◽  
Southern China ◽  
Energy Utilization ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Hourly Data ◽  
Short Term Forecasting ◽  
Gp Model

Accurate wind speed forecasts are necessary for the safety and economy of the renewable energy utilization. The wind speed forecasts can be obtained by statistical model based on historical data. In this paper, a novel W-GP model (wavelet decomposition based Gaussian process learning paradigm) is proposed for short-term wind speed forecasting. The nonstationary and nonlinear original wind speed series is first decomposed into a set of better-behaved constitutive subseries by wavelet decomposition. Then these sub-series are forecasted respectively by GP method, and the forecast results are summed to formulate an ensemble forecast for original wind speed series. Therefore, the previous process which obtains wind speed forecast result is named W-GP model. Finally, the proposed model is applied to short-term forecasting of the mean hourly and daily wind speed for a wind farm located in southern China. The prediction results indicate that the proposed W-GP model, which achieves a mean 13.34% improvement in RMSE (Root Mean Square Error) compared to persistence method for mean hourly data and a mean 7.71% improvement for mean daily wind speed data, shows the best forecasting accuracy among several forecasting models.

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
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