Template-Type: ReDIF-Paper 1.0
Author-Name: Shiwei Yu
Author-Name-First: Shiwei
Author-Name-Last: Yu
Author-Name: Yi-Ming Wei
Author-Name-First: Yi-Ming
Author-Name-Last: Wei
Author-Person: pwe328
Author-Email: ymwei@263.net
Author-Workplace-Name: Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology
Author-Name: Ke Wang
Author-Name-First: Ke
Author-Name-Last: Wang
Title: China's primary energy demands in 2020: Predictions from an MPSO-RBF estimation model
Abstract: In the present study, a Mix-encoding Particle Swarm Optimization and Radial Basis Function (MPSO-RBF) network-based energy demand forecasting model is proposed and appliedto forecast China's energy consumption until 2020. The energy demand isanalyzed for the period from 1980 to 2009 based on GDP, population, proportion of industry in GDP, urbanization rate, and share of coal energy. The results reveal that the proposed MPSO-RBF based model has fewer hidden nodes andsmaller estimated errors compared with other ANN-based estimation models. The average annual growth of China's energy demand will be 6.70%, 2.81%, and 5.08% for the period between 2010 and 2020 in three scenarios and could reach 6.25 billion, 4.16 billion, and 5.29 billion tons coal equivalentin 2020.Regardless of future scenarios, China's energy efficiency in 2020 will increase by more than 30% compared with 2009.
Length: 21 pages
Creation-Date: 2011-03
Publication-Status: Published in Energy Conversion and Management, 2012, 61:59-66.
File-URL: https://ceep.bit.edu.cn/docs/2026-07/eff1b915997d44d8b792b94dd1dddd0f.pdf
File-Format: Application/pdf
Number: 15
Classification-JEL: Q41, Q47
Keywords: China's energy demand, forecasting, Radial Basis Function (RBF) neural network, energy intensity
Handle: RePEc:biw:wpaper:15