|Table of Contents|

Short-term wind power forecasting model based on spatial correlation and wavelet-neural network

《南京理工大学学报》（自然科学版）[ISSN:1005-9830/CN:32-1397/N]

Issue:
2016年03期
Page:
360-
Research Field:
Publishing date:

Info

Title:
Short-term wind power forecasting model based on spatial correlation and wavelet-neural network
Author(s):
Xu Meimei1; Ren Zuyi2; Chen Jianguo1; Ni Jianjun2; Zhang Junfang3;  Ning Nan4; Zhao Jiwei4
1.Electric Power Research Institute of Guizhou Power Grid Co.,Ltd.,Guiyang 550002,China; 2.NR Electric Co.,Ltd.,Nanjing 211102,China; 3.School of Automation,Nanjing University of Science and Technology,Nanjing 210094,China; 4.Liupanshui Power Supply Bu
Keywords:
spatial correlation;  wavelet-neural network;  wind power forecasting;  wavelet basis function;  back propagation neural network;  wind energy utilization
PACS:
TM614
DOI:
10.14177/j.cnki.32-1397n.2016.40.03.019
Abstract:
In order to predict wind power accurately,a prediction model is proposed here.The inherent law of wind speed time series is extracted by the wind speed spatial correlation.The wavelet basis function is transferred into the neutron nodes of the neural network as the transfer function,and the wind power is predicted.The short-term wind power forecasting examples of two adjacent wind farms are simulated and analyzed.The simulation results show that compared with the back propagation neural network(BPNN)and wavelet-neural network(WNN)prediction models,the average percentage error of SC-WNN prediction model is reduced by 0.164 3.

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Last Update: 2016-06-30