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.

Combined Prediction of Wind Power Using Multi-Dimension Embedding Phase Space

2013 ◽  
Vol 321-324 ◽  
pp. 838-841
Author(s):  
Qiang Wang ◽  
Yang Yang
Keyword(s):  
Neural Network ◽  
Phase Space ◽  
Wind Power ◽  
Prediction Error ◽  
Wind Farm ◽  
Forecast Accuracy ◽  
Power Prediction ◽  
Combined Model ◽  
Wind Power Prediction ◽  
Network Method

In order to diminish the effect of reconstructed parameters to prediction of chaotic, a combined model for wind power prediction based on multi-dimension embedding is proposed. The combined model makes use of neural network method to achieve combination of several neural networks models based on phase space reconstruction, which can synthesize information and fuse prediction deviation in different embedding dimension, resulting in forecast accuracy improved. Simulation is performed to the real power time series Fujin wind farm. The results show that the combined prediction model is effective, and the prediction error of neural network combination is less than 7%.

Research of Aggregated Wind Power Prediction Method for a Region

2013 ◽  
Vol 860-863 ◽  
pp. 262-266
Author(s):  
Jin Yao Zhu ◽  
Jing Ru Yan ◽  
Xue Shen ◽  
Ran Li
Keyword(s):  
Power Systems ◽  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Prediction Method ◽  
Support Vector ◽  
Power Prediction ◽  
Power Characteristics ◽  
Wind Power Prediction ◽  
Grey Correlation Theory

Wind power is intermittent and volatility. Some new problems would arise to power system operation when Large-scale wind farm is connected with power systems. One of the most important effect is the influence on the grid dispatch. An aggregated wind power prediction method for a region is presented. By means of analyzing power characteristics and correlation, then the greater correlation is selected as model input. Based on grey correlation theory, a least squares support vector machine prediction model is established. Finally, this method is executed on a real case and integrated wind power prediction method can effectively improve the prediction accuracy and simplify the prediction step are proved.

Short-Term Wind Power Forecasting Based on SVM with Backstepping Wind Speed of Power Curve

2012 ◽  
Vol 224 ◽  
pp. 401-405
Author(s):  
Xi Yun Yang ◽  
Peng Wei ◽  
Huan Liu ◽  
Bao Jun Sun
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Power Plants ◽  
Wind Farm ◽  
Prediction Method ◽  
Power Curve ◽  
Power Prediction ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Wind Power Prediction

Accurate wind farm power prediction can relieve the disadvantageous impact of wind power plants on power systems and reduce the difficulty of the scheduling of power dispatching department. Improving accuracy of short-term wind speed prediction is the key of wind power prediction. The authors have studied the short-term wind power forecasting of power plants and proposed a model prediction method based on SVM with backstepping wind speed of power curve. In this method, the sequence of wind speed that is calculated according to the average power of the wind farm operating units and the scene of the power curve is the input of the SVM model. The results show that this method can meet the real-time needs of the prediction system, but also has better prediction accuracy, is a very valuable short-term wind power prediction method.

A new wind power prediction method based on chaotic theory and Bernstein Neural Network

Energy ◽  
2016 ◽  
Vol 117 ◽  
pp. 259-271 ◽  
Author(s):  
Cong Wang ◽  
Hongli Zhang ◽  
Wenhui Fan ◽  
Xiaochao Fan
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Prediction Method ◽  
Power Prediction ◽  
Wind Power Prediction ◽  
Chaotic Theory

Application and Contrast Analysis of BP and RBF Neural Network in Short-Term Wind Power Prediction

2014 ◽  
Vol 492 ◽  
pp. 544-549 ◽  
Author(s):  
Wen Hua Li ◽  
Qian Xiao ◽  
Jin Long Liu ◽  
Hui Qiao Liu
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Bp Neural Network ◽  
Wind Farm ◽  
Rbf Neural Network ◽  
Power Prediction ◽  
Wind Power Prediction ◽  
Contrast Analysis ◽  
Bp Model ◽  
Better Than

Wind power prediction is very important to maintain the power balance and economic operation of power system. The BP and RBF neural network were respectively used to predict one wind turbines’ output power, in 4 hours, on a wind farm in Shandong Province. The results show that the BP model, with 6-13-1 net structure and considering the meteorological factors, exhibits the best prediction accuracy (MAPE is 3.59%, NRMSE is 1.58%). The most important factor in the meteorological information for power prediction is temperature, followed by air pressure, relative humidity finally. BP model is slightly better than RBF model, but the latter is much better in the learning speed and stability. Dynamic-BP neural network, combined with the dynamical weight adjustment method, is better than BP neural network in solving the weight problem. These methods are feasible to the wind power prediction.

A Wind Power Prediction Method Based on RBF Neural Network

2015 ◽  
Vol 713-715 ◽  
pp. 1107-1110 ◽  
Author(s):  
Yue Ren Wang
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Large Scale ◽  
Historical Data ◽  
Rbf Neural Network ◽  
Prediction Method ◽  
Power Prediction ◽  
Output Data ◽  
Wind Power Prediction ◽  
System Input

With the interconnection of the large-scale wind power, wind power forecasting is particularly important to the dispatcher of power grid. Based on the historical data, this paper proposes a prediction method based on RBF (radial basis function) neural network. This method is based on the model taking the influence of the system input (wind speed, wind direction, historical power output data) on the predicting error into consideration to get the optimal input values. Examples with field data obtained from Northwest of China show the effectiveness and higher precisionof the proposed method.

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