Analytical study of Wind power prediction system by using Feed Forward Neural Network

2016 ◽  
Author(s):  
Vicky Jain ◽  
Alok Singh ◽  
Vibhor Chauhan ◽  
Alok Pandey
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Analytical Study ◽  
Prediction System ◽  
Power Prediction ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Wind Power Prediction

Comparison of the Extreme Learning Machine with the BP Neural Network for Short-Term Prediction of Wind Power

2012 ◽  
Vol 608-609 ◽  
pp. 564-568 ◽  
Author(s):  
Yi Hui Zhang ◽  
He Wang ◽  
Zhi Jian Hu ◽  
Meng Lin Zhang ◽  
Xiao Lu Gong ◽  
...  
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Extreme Learning Machine ◽  
Bp Neural Network ◽  
Learning Algorithm ◽  
Power Prediction ◽  
Feed Forward Neural Network ◽  
Wind Power Prediction ◽  
Learning Machine ◽  
Hidden Layer

Extreme learning machine (ELM) is a new and effective single-hidden layer feed forward neural network learning algorithm. Extreme learning machine only needs to set the number of hidden layer nodes of the network, and there is no need to adjust the neural network input weights and the hidden units bias, and it generates the only optimum solution, so it has the advantage of fast learning and good generalization ability. And the back propagation (BP) neural network is the most maturely applied. This paper has introduced the extreme learning machine into the wind power prediction. By comparing the wind power prediction method using the BP neural network. Study shows that the extreme learning machine has better prediction accuracy and shorter model training time.

Long-Term Wind Power Prediction Based on Rough Set

2013 ◽  
Vol 329 ◽  
pp. 411-415 ◽  
Author(s):  
Shuang Gao ◽  
Lei Dong ◽  
Xiao Zhong Liao ◽  
Yang Gao
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Rough Set ◽  
Wind Farm ◽  
Rough Set Theory ◽  
Prediction Method ◽  
Key Factors ◽  
Power Prediction ◽  
Wind Power Prediction

In long-term wind power prediction, dealing with the relevant factors correctly is the key point to improve the prediction accuracy. This paper presents a prediction method with rough set analysis. The key factors that affect the wind power prediction are identified by rough set theory. The chaotic characteristics of wind speed time series are analyzed. The rough set neural network prediction model is built by adding the key factors as the additional inputs to the chaotic neural network model. Data of Fujin wind farm are used for this paper to verify the new method of long-term wind power prediction. The results show that rough set method is a useful tool in long-term prediction of wind power.

Short-term wind power prediction based on improved small-world neural network

2018 ◽  
Vol 31 (7) ◽  
pp. 3173-3185 ◽  
Author(s):  
Shuang-Xin Wang ◽  
Meng Li ◽  
Long Zhao ◽  
Chen Jin
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Small World ◽  
Power Prediction ◽  
Short Term ◽  
Wind Power Prediction

Forecast of Short-Term Wind Power Based on GA Optimized Elman Neural Network

2014 ◽  
Vol 536-537 ◽  
pp. 470-475
Author(s):  
Ye Chen
Keyword(s):  
Neural Network ◽  
Power Systems ◽  
Wind Power ◽  
Wind Farm ◽  
Wind Farms ◽  
Power Prediction ◽  
Elman Neural Network ◽  
Wind Power Prediction ◽  
Forecasting Method ◽  
Safety And Stability

Due to the features of being fluctuant, intermittent, and stochastic of wind power, interconnection of large capacity wind farms with the power grid will bring about impact on the safety and stability of power systems. Based on the real-time wind power data, wind power prediction model using Elman neural network is proposed. At the same time in order to overcome the disadvantages of the Elman neural network for easily fall into local minimum and slow convergence speed, this paper put forward using the GA algorithm to optimize the weight and threshold of Elman neural network. Through the analysis of the measured data of one wind farm, shows that the forecasting method can improve the accuracy of the wind power prediction, so it has great practical value.

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