Study of PSO-RBF Neural Network in Power System Load Prediction
- https://doi.org/10.2991/icecee-15.2015.299How to use a DOI?
- RBF; PSO; Power system; Load prediction.
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.
- © 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/).
Cite this article
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 - https://doi.org/10.2991/icecee-15.2015.299 ID - Jiang2015/06 ER -