Study of PSO-RBF Neural Network in Power System Load Prediction

DOI

10.2991/icecee-15.2015.299How to use a DOI?

Keywords

RBF; PSO; Power system; Load prediction.

Abstract

Short-term load prediction of power system has great significance for safety and economy of power system operation as basic content of power system operation management and real-time control. In the paper, power system short-term load predicting model based on RBF neural network was established. Influences of temperature, holidays and other factors on power system load were mainly considered in the model. PSO optimization algorithm was adopted for optimizing initial weights and base width of RBF neural network aiming at random settings of initial weights and base width of RBF neural network. History real load data was verified, and the verified results were compared with traditional RBF neural network model, the results showed that the prediction precision of RBF neural network model optimized by PSO algorithm was obviously improved, thereby providing an effective method for short-term load prediction of power system.

Copyright

© 2015, 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/).

Download article (PDF)

TY  - CONF
AU  - Ai-hua Jiang
AU  - Yan Li
AU  - Chen Xue
PY  - 2015/06
DA  - 2015/06
TI  - Study of PSO-RBF Neural Network in Power System Load Prediction
BT  - Proceedings of the 2015 International Conference on Electrical, Computer Engineering and Electronics
PB  - Atlantis Press
SP  - 1588
EP  - 1593
SN  - 2352-538X
UR  - https://doi.org/10.2991/icecee-15.2015.299
DO  - 10.2991/icecee-15.2015.299
ID  - Jiang2015/06
ER  -