Physical Model Induced Neural Network for Wind Speed Prediction for Wind Turbines under Wake Effects

2021 ◽  
Author(s):  
J. Liu ◽  
Y. Qiu ◽  
Y. Feng ◽  
X. Li
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Physical Model ◽  
Wind Turbines ◽  
Wind Speed Prediction ◽  
Wake Effects ◽  
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.

scholarly journals Wind speed prediction with RBF neural network based on PCA and ICA

2018 ◽  
Vol 69 (2) ◽  
pp. 148-155 ◽  
Author(s):  
Yagang Zhang ◽  
Chenhong Zhang ◽  
Yuan Zhao ◽  
Shuang Gao
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Wind Farm ◽  
Component Analysis ◽  
Independent Component ◽  
Learning Ability ◽  
Random Fluctuation ◽  
Forecasting Accuracy ◽  
Wind Speed Prediction ◽  
Speed Prediction

Abstract Thanks to non-pollution and sustainability of wind energy, it has become the main source of power generation in the new era worldwide. However, the inherent random fluctuation and intermittency of wind power have negative effects on the safe and stable operation of power system and the quality of power. The key solving this problem is to improve the accuracy of wind speed prediction. In the paper, considering the forecasting accuracy is affected by many factors, we propose that, Principal Component Analysis (PCA) is combined with Independent Component Analysis (ICA) to process the sample, which can weaken the mutual interference between the various factors, extract accurately independent component reflected the characteristics of wind farm and achieve the purpose of improving the accuracy of wind speed prediction. At the same time, the adaptive and self-learning ability of neural network is more suitable for wind speed sequence prediction. The prediction results demonstrate that compared with the traditional neural network predicting model (RBF, BP, Elman), this model makes full use of the information provided by varieties of relevant factors, weakens the volatility of wind speed sequence and significantly enhances the short-term wind speed forecasting accuracy. The research work in the paper can help wind farm reasonably arrange the power dispatching plan, reduce the power operation cost and effectively boost the large-scale development and utilization of renewable energy.

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