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Xin Wang
North China Electric Power University

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Industrial Electrical Energy Efficiency Research Based on DEA Optimization Model Jianna Zhao; Xin Wang
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 3: March 2014
Publisher : Institute of Advanced Engineering and Science

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Abstract

Traditional DEA does not take into account the environmental variables when analysis the electrical energy efficiency. In order to weigh the objective and subjective factors’ impact on industrial electrical energy efficiency , this paper attempts to analyze the China's  2011 industrial electrical energy efficiency step by step using DEA optimization model. The results showed that, electrical energy efficiency value in considering the case of environmental factors was increased , effect of the external environment on electrical energy efficiency indeed have a significant impact, On these analysis, the paper proposed some feasible recommendations like increasing the scale of industrial energy utilization, eliminating monopolies as soon as possible, allocating the science and technology funding actively and feasibly DOI : http://dx.doi.org/10.11591/telkomnika.v12i3.4454
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

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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 Jianna Zhao Mei Liu Zhengren Wu