Article Per Year (5 Year)
p-Index From 2021 - 2026
0
P-Index
This Author published in this journals
All Journal Indonesian Journal of Electrical Engineering and Computer Science
Mei Liu
North China Electric Power University

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Research on Electrical Energy Consumption Efficiency Based on GM-DEA Mei Liu
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 8: August 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i8.pp5801-5806

Abstract

In today's environment which emphasis on energy efficiency, predicting the trend of electrical energy consumption efficiency, and researching the efficient operation model of power industry has practical significance. By establishing GM-DEA method system, we use GM model to predict four inputs and output indicators in 2011 and 2012 in Beijing first, and then use DEA to give predicting years a reasonable analysis for the efficiency of energy consumption. The result shows that efficiency of electrical energy consumption in Beijing is gradually increasing, GM-DEA model can analyze the trend of the efficiency effectively in advance, and it provides a scientific basis for the rapid development of power industry.
Prediction of Electric Power Consumption Based on the Improved GM(1, 1) Zhengren Wu; Mei Liu; Xin Wang
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 8: August 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Based on the electric power consumption data in 2001-2010, this paper discusses GM (1, 1) model and its improved model in the application of power consumption forecasting. Due to the traditional Grey Model itself has certain defects, we grouped the original sequence according to the degree of deviation first, and then combined with nonlinear GM (1, 1, α) to improve the traditional GM (1, 1) model. Through the relative error testing and the posterior testing, this paper made a comparative analysis to the traditional GM (1, 1) model and the improved GM. Example of Beijing shows that the improved model had good accuracy; it had a good application value in the actual prediction system. DOI: http://dx.doi.org/10.11591/telkomnika.v11i8.3104
Co-Authors Xin Wang Zhengren Wu