Title :
Application of Dynamic Recurrent Neural Network in Power System Short-Term Load Forecasting
Author :
Ge Chao ; Zhang Jing-chun ; Sun Yan-bin ; Sun Li-ying
Author_Institution :
Coll. of Inf., Hebei Polytech. Univ., Tangshan, China
Abstract :
Convergence speed of the traditional BP neural network is slow, and it is easy to fall into local minimum. A novel dynamic recurrent fuzzy neural network model is proposed, which is used to resolve the power system short-term load forecasting. The fuzzy inference function is realized easily by using a product operation in the network. The simulation results indicate that the proposed network can overcome the limit of back-propagation-based static network methods and accurately forecast the short-term load.
Keywords :
backpropagation; load forecasting; power engineering computing; power systems; recurrent neural nets; back-propagation-based static network methods; dynamic recurrent neural network; fuzzy inference function; power system short-term load forecasting; Convergence; Fuzzy neural networks; Load forecasting; Neural networks; Power system dynamics; Power system modeling; Power system simulation; Power systems; Predictive models; Recurrent neural networks; dynamic recurrent; forecasting model; fuzzy neural network; load forecasting;
Conference_Titel :
Computing, Control and Industrial Engineering (CCIE), 2010 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-0-7695-4026-9
DOI :
10.1109/CCIE.2010.101