|Table of Contents|

Short-term power load forecasting model based on QPSO-RBFNN

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

Issue:
2016年01期
Page:
97-
Research Field:
Publishing date:

Info

Title:
Short-term power load forecasting model based on QPSO-RBFNN
Author(s):
Zhu ZhenshuBo YumingWu PanlongZhao GaopengZhu Jianliang
School of Automation,Nanjing University of Science and Technology,Nanjing 210094,China
Keywords:
quantum-behaved particle swarm optimization power load load forecasting radical basis function neural network K-means clustering weights root mean square error
PACS:
TM715
DOI:
-
Abstract:
In order to improve the accuracy of short-term power load forecasting,a forecasting model is proposed by combining quantum-behaved particle swarm optimization(QPSO)algorithm with radical basis function neural network(RBFNN).The basis function center of the RBFNN is obtained by the K-means clustering algorithm.The neural network weights are optimized by the particle swarm optimization algorithm.The convergence rate of the RBFNN is fastened while the forecasting accuracy is raised.The short-term power load forecasting is verified by real load data,the root mean square error of daily load forecasting is less than 0.01,and the rationality and validity of the model are demonstrated.

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Last Update: 2016-02-29