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.

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.

scholarly journals Short-term Wind Power Prediction Model Based on Encoder-Decoder LSTM

2018 ◽  
Vol 186 ◽  
pp. 012020
Author(s):  
Kuan Lu ◽  
Wen Xue Sun ◽  
Xin Wang ◽  
Xiang Rong Meng ◽  
Yong Zhai ◽  
...  
Keyword(s):  
Prediction Model ◽  
Wind Power ◽  
Power Prediction ◽  
Short Term ◽  
Model Based ◽  
Wind Power Prediction

scholarly journals Wind Power Prediction in View of Ramping Events Based on Classified Spatiotemporal Network

2022 ◽  
Vol 9 ◽  
Author(s):  
Bingbing Xia ◽  
Qiyue Huang ◽  
Hao Wang ◽  
Liheng Ying
Keyword(s):  
Power System ◽  
Prediction Model ◽  
Wind Power ◽  
Large Scale ◽  
Weather Prediction ◽  
Measurement Data ◽  
Stable Operation ◽  
Power Prediction ◽  
Actual Measurement ◽  
Wind Power Prediction

Wind energy has been connected to the power system on a large scale with the advantage of little pollution and large reserves. While ramping events under the influence of extreme weather will cause damage to the safe and stable operation of power system. It is significant to promote the consumption of renewable energy by improving the power prediction accuracy of ramping events. This paper presents a wind power prediction model of ramping events based on classified spatiotemporal network. Firstly, the spinning door algorithm builds parallelograms to identify ramping events from historical data. Due to the rarity of ramping events, the serious shortage of samples restricts the accuracy of the prediction model. By using generative adversarial network for training, simulated ramping data are generated to expand the database. After obtaining sufficient data, classification and type prediction of ramping events are carried out, and the type probability is calculated. Combined with the probability weight, the spatiotemporal neural network considering numerical weather prediction data is used to realize power prediction. Finally, the effectiveness of the model is verified by the actual measurement data of a wind farm in Northeast China.

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