scholarly journals Wind power short-term power forecasting based on Improved Grey Wolf algorithm and Optimized Generalized Regression Neural Network

2021 ◽  
Vol 2085 (1) ◽  
pp. 012020
Author(s):  
Yiwen Hu ◽  
Yang Gao ◽  
Shuai Yang
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Wind Power ◽  
Prediction Method ◽  
General Regression Neural Network ◽  
Generalized Regression Neural Network ◽  
Gray Wolf ◽  
Power Prediction ◽  
Bp Network ◽  
Generalized Regression

Abstract Aiming at the problem of wind turbine output prediction, a wind power prediction method based on Improved Gray Wolf algorithm and optimized generalized regression neural network is proposed in this paper. Firstly, according to the daily similarity of wind speed and wind power, cluster analysis is used to classify the data. Considering that the degree of each factor affecting wind power output changes, based on the selection of similar days, an improved gray wolf algorithm is introduced to optimize the weight of each influencing factor. The two models of the first mock exam are selected to input the radial single mode function RBF and the back propagation (BP) network to predict the output of the wind turbine separately. The prediction results of the two models are input to the generalized regression neural network optimized by the Wolf Wolf algorithm and the nonlinear combination forecasting is carried out. The basis models are used to predict the output of the wind turbine. The example analysis of an area shows that the model can be closer to the real value in the peak and valley of the prediction curve and has higher prediction accuracy than the combined prediction model of single BP, RBF and non optimized general regression neural network (GRNN).

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.

Application of the Combination Prediction Model in Forecasting the Short-Term Wind Power

2015 ◽  
Vol 733 ◽  
pp. 893-897
Author(s):  
Peng Yu Zhang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Wind Power ◽  
Pso Algorithm ◽  
Support Vector ◽  
Generalized Regression Neural Network ◽  
Power Prediction ◽  
Short Term ◽  
Combination Forecasting ◽  
Wind Power Prediction

The accuracy of short-term wind power forecast is important for the power system operation. Based on the real-time wind power data, a wind power prediction model using wavelet neural network (WNN) is proposed. In order to overcome such disadvantages of WNN as easily falling into local minimum, this paper put forward using Particle Swarm Optimization (PSO) algorithm to optimize the weight and threshold of WNN. It’s advisable to use Support Vector Machine (SVM) to comparatively do prediction and put two outcomes as input vector for Generalized Regression Neural Network (GRNN) to do nonlinear combination forecasting. Simulation shows that combination prediction model can improve the accuracy of the short-term wind power prediction.

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.

Prediction Method of Railway Freight Volume Based on Genetic Algorithm Improved General Regression Neural Network

2017 ◽  
Vol 28 (5) ◽  
pp. 835-848
Author(s):  
Zhi-da Guo ◽  
Jing-Yuan Fu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Supply And Demand ◽  
Prediction Method ◽  
General Regression Neural Network ◽  
Generalized Regression Neural Network ◽  
Improved Method ◽  
Railway Freight ◽  
Freight Volume ◽  
Construction Operation

Abstract Railway freight transportation is an important part of the national economy. The accurate forecast of railway freight volume is significant to the planning, construction, operation, and decision-making of railways. Railway freight volume forecasting methods are complex and nonlinear due to the imbalance of supply and demand in the railway freight market as well as the complicated and different influences of various factors on freight volume. The relation between some information is easily ignored when the traditional method of railway freight volume forecasting is used for prediction based on causality or time series. After analyzing the application status of the generalized regression neural network (GRNN) in the prediction method of railway freight volume, this paper improves the performance of this model using an improved neural network. In the improved method, genetic algorithm (GA) is adopted to search the optimal spread, which is the only factor of the GRNN, and then the optimal spread is used for forecasting in the GRNN. In the process of railway freight volume forecasting, through this method, the increments of data are taken in the calculation process and the goal values are obtained after calculation as the forecasted results. Compared to the results of the GRNN, higher prediction accuracy is obtained through the GA-improved GRNN. Finally, the railway freight volumes in the next 2 years are forecasted based on this method and this improved method can provide a new approach for predicting the railway freight volume.

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