Proceedings of the 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016)

The Short Term Load Forecasting of RBF Neural Network Power System Based on Fuzzy Control

Authors
Jiangnan Ni, Guo Jin
Corresponding Author
Jiangnan Ni
Available Online September 2016.
DOI
https://doi.org/10.2991/icence-16.2016.28How to use a DOI?
Keywords
Power system, Load forecasting, RBF neural network, Fuzzy control.
Abstract

This paper presents a kind of power system short-term load prediction algorithm based on fuzzy control and RBF neural network, to solve the problems of th traditional RBF neural network in electric power system short-term load forecast errors. Through the example verification, this method can improve the prediction accuracy compared with the traditional RBF load forecasting method, which has a good application prospect.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016)
Series
Advances in Computer Science Research
Publication Date
September 2016
ISBN
978-94-6252-229-9
ISSN
2352-538X
DOI
https://doi.org/10.2991/icence-16.2016.28How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Jiangnan Ni
AU  - Guo Jin
PY  - 2016/09
DA  - 2016/09
TI  - The Short Term Load Forecasting of RBF Neural Network Power System Based on Fuzzy Control
BT  - Proceedings of the 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016)
PB  - Atlantis Press
SP  - 133
EP  - 137
SN  - 2352-538X
UR  - https://doi.org/10.2991/icence-16.2016.28
DO  - https://doi.org/10.2991/icence-16.2016.28
ID  - Ni2016/09
ER  -