Title :
Electricity Consumption Forecasting Based on Improved BP Neural Network
Author :
Zhang Xing-ping ; Yuan Jia-hai
Author_Institution :
Sch. of Bus. Adm., North China Power Electr. Univ., Beijing
Abstract :
An improved BP Neural Network with additional momentum and adaptive learning is proposed in the paper to predict the growth rate of electricity consumption in China. Matlab7 is used as modeling tool to design the model. Current year GDP growth, electric power consumption growth and growth rate of secondary industry are taken as input variables while next year electric power consumption growth is predicted. The simulation results are compared with that of traditional BP Neural Network model, which show the feasibility of the model proposed in the paper.
Keywords :
backpropagation; economic indicators; energy consumption; load forecasting; neural nets; power engineering computing; power generation economics; BP neural network; China; GDP growth; adaptive learning; electricity consumption forecasting; momentum learning; Artificial neural networks; Computer languages; Economic forecasting; Economic indicators; Energy consumption; Mathematical model; Neural networks; Neurons; Research and development management; Risk management; Adaptive learning; Electricity demand; Learning algorithm; Neural network;
Conference_Titel :
Risk Management & Engineering Management, 2008. ICRMEM '08. International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-0-7695-3402-2
DOI :
10.1109/ICRMEM.2008.104
