Research on Wind Power Prediction Method Based on Convolutional Neural Network and Genetic Algorithm

2019 ◽  
Author(s):  
Gang Chen ◽  
Jingning Shan ◽  
Dan Yang Li ◽  
Chenqi Wang ◽  
Chengwei Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Convolutional Neural Network ◽  
Wind Power ◽  
Prediction Method ◽  
Power Prediction ◽  
Wind Power Prediction

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.

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

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.

scholarly journals Deterministic and Probabilistic Wind Power Forecasting Based on Bi-Level Convolutional Neural Network and Particle Swarm Optimization

2019 ◽  
Vol 9 (9) ◽  
pp. 1794 ◽  
Author(s):  
Yang ◽  
Zhang ◽  
Yang ◽  
Lv
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Convolutional Neural Network ◽  
Wind Power ◽  
Particle Swarm ◽  
Power Prediction ◽  
Swarm Optimization ◽  
Wind Power Forecasting ◽  
Interval Prediction ◽  
Wind Power Prediction

The intermittency and uncertainty of wind power result in challenges for large-scale wind power integration. Accurate wind power prediction is becoming increasingly important for power system planning and operation. In this paper, a probabilistic interval prediction method for wind power based on deep learning and particle swarm optimization (PSO) is proposed. Variational mode decomposition (VMD) and phase space reconstruction are used to pre-process the original wind power data to obtain additional details and uncover hidden information in the data. Subsequently, a bi-level convolutional neural network is used to learn nonlinear features in the pre-processed wind power data for wind power forecasting. PSO is used to determine the uncertainty of the point-based wind power prediction and to obtain the probabilistic prediction interval of the wind power. Wind power data from a Chinese wind farm and modeled wind power data provided by the United States Renewable Energy Laboratory are used to conduct extensive tests of the proposed method. The results show that the proposed method has competitive advantages for the point-based and probabilistic interval prediction of wind power.

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