Title :
Energy Demand Forecast in China Based on Wavelet Neural Network
Author :
Jiang, Li ; Wang, Jue
Author_Institution :
Sch. of Math. & Phys., Qingdao Univ. of Sci. & Technol., Qingdao, China
Abstract :
In this paper, a wavelet-neural-network-based forecast model is developed for energy demand in China. Combining qualitative with quantitative analysis, we analyze some main factors affecting energy demand in China. A first order wavelet-neural network forecasting model with time-delay is established, including population, GDP, variation of industrial structure and energy consumption. The simulation result shows that this nonlinear forecasting model is more reasonable and has higher precision than other multiple regression models.
Keywords :
delays; load forecasting; neural nets; power consumption; power engineering computing; regression analysis; wavelet transforms; GDP; energy consumption; energy demand forecast; first order wavelet-neural network forecasting model; forecast model; industrial structure; multiple regression models; nonlinear forecasting model; population; qualitative analysis; quantitative analysis; time-delay; wavelet neural network; Artificial neural networks; Demand forecasting; Energy consumption; Load forecasting; Mathematics; Neural networks; Petroleum; Predictive models; Statistical analysis; Wavelet analysis; Energy demand; Impact factor; Wavelet neural network;
Conference_Titel :
Computer Science and Engineering, 2009. WCSE '09. Second International Workshop on
Conference_Location :
Qingdao
Print_ISBN :
978-0-7695-3881-5
DOI :
10.1109/WCSE.2009.755
