Wind Power Prediction Model Based on Wavelet Neural Network under Missing Data

2015 ◽  
Vol 737 ◽  
pp. 76-80
Author(s):  
Jing Lu ◽  
Yan Qing Zhao ◽  
Yu Hong Zhao ◽  
Jun Yi Zhao ◽  
Chao Ying Yang
Keyword(s):  
Neural Network ◽  
Missing Data ◽  
Prediction Model ◽  
Wind Power ◽  
Wind Farm ◽  
Wavelet Neural Network ◽  
Power Prediction ◽  
Model Based ◽  
Wind Power Prediction ◽  
Mean Square Errors

Wind power prediction is a key problem in optimizing power dispatching. This paper builds a wind power prediction model based on wavelet neural network which substitutes wavelet basis function for the transfer function of hidden layer. A missing data interpolation strategy is also given to improve the applicability of the model. With the wind farm data from southeast coast, the model works and the wind power in the next 30 hours is predicted. In the sense of the mean square errors this paper compared the prediction results of the model and BP neural network model, the results shows the models have a better accuracy.

Algorithm and Realization of Wind Power Prediction System

2013 ◽  
Vol 748 ◽  
pp. 439-443
Author(s):  
L. Zhou ◽  
E.W. He ◽  
J.C. Wang ◽  
D.H. Chen ◽  
Q.Z. Chen
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Wind Power ◽  
Wind Farm ◽  
Stable Operation ◽  
Prediction System ◽  
Power Prediction ◽  
Model Based ◽  
Term Prediction ◽  
Wind Power Prediction

The application of wind power prediction system (WPPS) contributes to security economic dispatching of power grid and stable operation of wind farm. This paper established short-term prediction model based on BP neural network and ultrashort-term prediction model based on improved time-series algorithm according to Xichang Wind Farm Phase I Project. A new probability model using two consecutive power points before prediction time was built to improve the traditional time-series algorithm. The system framework was designed. C# Language and SQL Server 2008 were taken to develop the system on the Microsoft .net platform. The WPPS uses distributed architecture, realizing seamless connection with the energy management system (EMS) of Xichang dispatching department.

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.

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.

Wind Power Forecasting Based on Improved Biased Wavelet Neural Network

2014 ◽  
Vol 915-916 ◽  
pp. 1532-1535
Author(s):  
Yu Han Mao
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Prediction Accuracy ◽  
Forecast Model ◽  
Wavelet Neural Network ◽  
Strong Regularity ◽  
Power Prediction ◽  
Wind Power Prediction ◽  
Wind Power System ◽  
Power Sequence

Wind power prediction is the key to grid-connected wind power system. In this paper, first of all, we decompose and reconstruct the power sequence by wavelet analysis, and reduce the noise of the detail signal, to obtain the strong-regularity subsequence. We adapt the biased wavelet neural network rolling forecast model for the processed sequence to obtain seven days of rolling forecast results through several amendments. For the sequence of 5 minutes interval the prediction accuracy is 98.63%, for the sequence of 15 minutes interval the prediction accuracy is 99.88%.

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