Electricity Demand Projection Using a Path-Coefficient Analysis and BAG-SA Approach: A Case Study of ChinaReportar como inadecuado

Electricity Demand Projection Using a Path-Coefficient Analysis and BAG-SA Approach: A Case Study of China - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.

Discrete Dynamics in Nature and Society - Volume 2017 2017, Article ID 2379381, 9 pages - https:-doi.org-10.1155-2017-2379381

Research ArticleDepartment of Economics and Management, North China Electric Power University, Baoding 071003, China

Correspondence should be addressed to Chenyang Peng

Received 7 January 2017; Revised 18 March 2017; Accepted 27 March 2017; Published 9 April 2017

Academic Editor: Gabriella Bretti

Copyright © 2017 Qunli Wu and Chenyang Peng. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Path-coefficient analysis is utilized to investigate the direct and indirect effects of economic growth, population growth, urbanization rate, industrialization level, and carbon intensity on electricity demand of China. To improve the projection accuracy of electricity demand, this study proposes a hybrid bat algorithm, Gaussian perturbations, and simulated annealing BAG-SA optimization method. The proposed BAG-SA algorithm not only inherits the simplicity and efficiency of the standard BA with a capability of searching for global optimality but also enhances local search ability and speeds up the global convergence rate. The BAG-SA algorithm is employed to optimize the coefficients of the multiple linear and quadratic forms of electricity demand estimation model. Results indicate that the proposed algorithm has higher precision and reliability than the coefficients optimized by other single-optimization methods, such as genetic algorithm, particle swarm optimization algorithm, or bat algorithm. And the quadratic form of BAG-SA electricity demand estimation model has better fitting ability compared with the multiple linear form of the model. Therefore, the quadratic form of the model is applied to estimate electricity demand of China from 2016 to 2030. The findings of this study demonstrate that China’s electricity demand will reach 14925200 million KWh in 2030.

Autor: Qunli Wu and Chenyang Peng

Fuente: https://www.hindawi.com/


Documentos relacionados