Title :
Power Consumption Forecast by Combining Neural Networks with Immune Algorithm
Author_Institution :
North China Electr. Power Univ., Beijing
fDate :
July 30 2007-Aug. 1 2007
Abstract :
Considering the impact of multi-factors, established a forecasting model, and then designed the structure of BP neural network while applying immune algorithm to optimize its network structure and weights, a nonlinear network model between power consumption and the affected factors was obtained through training the relative data of power consumption from 1980 to 2005 in China, and power consumption was forecasted. The result shows that the forecast by this model is more accurate.
Keywords :
load forecasting; neural nets; power consumption; power engineering computing; BP neural network; immune algorithm; nonlinear network model; power consumption forecast; Algorithm design and analysis; Artificial intelligence; Artificial neural networks; Distributed computing; Economic forecasting; Energy consumption; Neural networks; Predictive models; Software algorithms; Software engineering;
Conference_Titel :
Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2007. SNPD 2007. Eighth ACIS International Conference on
Conference_Location :
Qingdao
Print_ISBN :
978-0-7695-2909-7
DOI :
10.1109/SNPD.2007.49