The Short-Term Wind Power Prediction Based on the Neural Network of Logistic Mapping Phase Space Reconstruction

2015 ◽  
Author(s):  
Han Yajun ◽  
Yang Xiaoqiang
Keyword(s):  
Neural Network ◽  
Phase Space ◽  
Wind Power ◽  
Phase Space Reconstruction ◽  
Power Prediction ◽  
Short Term ◽  
Logistic Mapping ◽  
The Neural Network ◽  
Wind Power Prediction

Ultra-short-term Wind Power Prediction based on Chaos Phase Space Reconstruction and NWP

2015 ◽  
Vol 8 (5) ◽  
pp. 325-336 ◽  
Author(s):  
Yang Gao ◽  
Aoran Xu ◽  
Yan Zhao ◽  
Baogui Liu ◽  
Liu Zhang ◽  
...  
Keyword(s):  
Phase Space ◽  
Wind Power ◽  
Phase Space Reconstruction ◽  
Power Prediction ◽  
Short Term ◽  
Wind Power Prediction

Short-term wind power prediction based on improved small-world neural network

2018 ◽  
Vol 31 (7) ◽  
pp. 3173-3185 ◽  
Author(s):  
Shuang-Xin Wang ◽  
Meng Li ◽  
Long Zhao ◽  
Chen Jin
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Small World ◽  
Power Prediction ◽  
Short Term ◽  
Wind Power Prediction

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.

Wavelet Neural Network Model for Short-Term Wind Power Forecasting Based on Particle Swarm Optimization Algorithm

2014 ◽  
Vol 543-547 ◽  
pp. 806-812 ◽  
Author(s):  
Ye Chen
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Wind Power ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Wavelet Neural Network ◽  
Power Prediction ◽  
Short Term ◽  
Swarm Optimization ◽  
Wind Power Prediction

The accuracy of short-term wind power forecast is important to the power system operation. Based on the real-time wind power data, a wind power prediction model using wavelet neural network is proposed. At the same time in order to overcome the disadvantages of the wavelet neural network for only use error reverse transmission as a fixed rule, this paper puts forward using Particle Swarm Optimization algorithm to replace the traditional gradient descent method training wavelet neural network. Through the analysis of the measured data of a wind farm, Shows that the forecasting method can improve the accuracy of the wind power prediction, so it has great practical value.

scholarly journals Short-Term Wind Power Prediction Using GA-BP Neural Network Based on DBSCAN Algorithm Outlier Identification

Processes ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 157 ◽  
Author(s):  
Pei Zhang ◽  
Yanling Wang ◽  
Likai Liang ◽  
Xing Li ◽  
Qingtian Duan
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Wind Power ◽  
Bp Neural Network ◽  
Linear Regression Method ◽  
Power Prediction ◽  
Short Term ◽  
Outlier Identification ◽  
Dbscan Algorithm ◽  
Wind Power Prediction

Accurately predicting wind power plays a vital part in site selection, large-scale grid connection, and the safe and efficient operation of wind power generation equipment. In the stage of data pre-processing, density-based spatial clustering of applications with noise (DBSCAN) algorithm is used to identify the outliers in the wind power data and the collected wind speed data of a wind power plant in Shandong Province, and the linear regression method is used to correct the outliers to improve the prediction accuracy. Considering the important impact of wind speed on power, the average value, the maximum difference and the average change rate of daily wind speed of each historical day are used as the selection criteria to select similar days by using DBSCAN algorithm and Euclidean distance. The short-term wind power prediction is carried out by using the similar day data pre-processed and unprocessed, respectively, as the input of back propagation neural network optimized by genetic algorithm (GA-BP neural network). Analysis of the results proves the practicability and efficiency of the prediction model and the important role of outlier identification and correction in improving the accuracy of wind power prediction.

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%.

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