An Improved RBF Neural Network for Short Term Load Forecasting

Chuang Li; Zhi Qiang Liang; Min You Chen

doi:10.4028/www.scientific.net/AMR.1008-1009.709

Paper Titles

Research on Access Network Communication Technology for Condition Monitoring System of Power Transmission Line
p.684

Research on Tuning Protection with Distributed Power Distribution Grid
p.690

A Closed-Loop Test System for Network Performance of Smart Substation
p.694

A New Kind of Smart Dual DSP2812 Power Quality Monitor Based on Dedicated Users
p.701

An Improved RBF Neural Network for Short Term Load Forecasting
p.709

Analysis on Capacity Evaluation Model and Optimization Algorithm for Meshed Power Collection Grid
p.714

Research on Scheduling Strategy for Distribution Network with the Access of Multi-Microgrids Including PV/WT/ST
p.718

A Review of Distribution Network Planning with Large-Scale Intermittent Renewable Energy Access to Distribution Network
p.723

Study on the Operation Electricity Price of Active Distribution Network
p.729

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 1008-1009An Improved RBF Neural Network for Short Term Load...

An Improved RBF Neural Network for Short Term Load Forecasting

Abstract:

Neural network is widely used in the load forecasting area,but the traditional methods of load forecasting usually base on static model,which cannot change as time goes on. And the accuracy is worse and worse. To solve the problem, a dynamic neural network model for load forecasting is proposed .By way of introduce Error discriminant function, to control the error of load forecasting and dynamically modify the predicting model. Through the contrast of the short-term load forecasting result based on static neural network model and dynamic neural network model proposed, the error of load forecasting is decrease effectively.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 1008-1009)

Pages:

709-713

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.1008-1009.709

Citation:

Cite this paper

Online since:

August 2014

Authors:

Chuang Li, Zhi Qiang Liang, Min You Chen

Keywords:

Dynamic Model, Error Discriminant Function, Load Forecasting, RBF Neural Network

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Yingjin Li, Dongxiao Niu. Studyon graycombined forecasting ofnonlinear seasonal power load. Power System Technology, Vol. 27(2003), p.26.

Google Scholar

[2] Junhua Xu, Qitian Liu. An approach to short-term load forecaststing based on wavelet transform and artificial neural network. Power System Technology, Vol. 28(2004), p.30.

Google Scholar

[3] Wenxiao Ma, Xiaomin Bai, Lianshun Mu. Short term load forecasting using artificial neural network and fuzzy inference. Power System Technology, Vol. 27(2003), p.29.

DOI: 10.1109/icpst.2002.1047571

Google Scholar