Abstract
With the raise of “The Green Industrial Revolution” and the growing demand for low-carbon energy, determining the method for assessing energy usage has become an important problem. In this paper, an assessment method of energy is proposed based on the combination of several models. The study of energy efficiency in China is conducted. First, the data related to the energy efficiency in 24 provinces for the last 9 years is gathered. Meanwhile the key factors for ensuring effective energy utilization in two provinces is determined through feature recognition. Then, the comparative analysis on the categories-fusion model’s goodness of fit is performed, which was also used to predict the energy efficiency. Subsequently, the provinces with high and low energy efficiency based on the clustering strategies with multiple models merged are differentiated. Finally, corresponding recommendations for the development problem in China are presented based on the summary of the experimental results. The experimental results show that in comparison with the single-model approach, the merged multiple models has better performance than other methods. Therefore, this approach has a good engineering value.
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References
Rosen, M.A.: Assessing global resource utilization efficiency in the industrial sector. Sci. Total Environ. 461, 804–807 (2013)
Lee, Y.C., Zomaya, A.Y.: Energy efficient utilization of resources in cloud computing systems. J. Supercomput. 60(2), 268–280 (2012)
Subirats, J., Guitart, J.: Assessing and forecasting energy efficiency on Cloud computing platforms. Future Gener. Comput. Syst. 45, 70–94 (2015)
Shen, N., Zhou, J., Zou, W.: Energy efficiency measures and convergence in China, taking into account the effects of environmental and random factors. Polish J. Environ. Stud. 24(1), 257–267 (2015)
Guan, W., Xu, S.: Study on spatial pattern and spatial effect of energy eco-efficiency in China. Acta Geogr. Sinica 70(6), 980–992 (2015)
Jiankun, H.E., Xiliang, Z.: Analysis declining tendency in China’s energy consumption intensity during the 11th Five - Year - Plan period. China Soft Sci. 4, 33–38 (2006)
Hu, J.-L., Wang, S.-C.: Total-factor energy efficiency of regions in China. Energy Policy 34(17), 3206–3217 (2006)
Wang, Q., et al.: Energy efficiency and production technology heterogeneity in China: a meta-frontier DEA approach. Econ. Modelli. 35, 283–289 (2013)
Yang, M., Yang, F., Chen, X.: On influencing factors affecting China’s energy efficiency: an empirical test based on the VEC model. Resour. Sci. 33(1), 163–168 (2011)
Chu, W.: Research on Energy Efficiency of China[M]. China Environmental Science Press, Beijing (2011)
Chu, W., Shen, M.: Empirical analysis of energy and influences factors based on DEA. Manag. World 8, 66–76 (2007)
Yingrui, C.: The Third Industrial Energy Effiency Evaluation Research Based on DEA Models [D]. Beijing University of Technology, Beijing (2013)
Li, L.: Appraisal of input- output effect in every industry within tertiary industry [J]. J. Beijing Technol. Bus Univ 6, 32–36 (2007)
Li, M.: Research of the Efficiency of Oil Companies Based on Enhanced DEA Model[D]. Tianjin University, Tianjin (2010)
Tang, K.: Improvement of DEA Model and its Application on Efficiency Evaluation of Listed Companies[D]. Harbin Institute of Technology, Harbin (2010)
Lee, W.S.: Benchmarking the energy efficiency of government buildings with data envelopment analysis[J]. Energy Build. 40(5), 891–895 (2008)
Quinlan, J.R.: Decision trees and decision-making. IEEE Trans. Syst. Man Cybern. 20(2), 339–346 (1990)
Shen, Y., Lei, L., Zhang, X.: Evaluation of energy transfer and utilization efficiency of azo dye removal by different pulsed electrical discharge modes. Chin. Sci. Bull. 53(12), 1824–1834 (2008)
Liu, L.: How large is Chinas regional disparity of energy efficiency in industrial sector? from the perspective of substitution effect analysis. Math. Pract. Theory 42(12), 48–54 (2012)
Zou, G., et al.: Measurement and evaluation of Chinese regional energy efficiency based on provincial panel data. Math. Computer Modelling 58(5–6), 1000–1009 (2013)
Shang, C., et al.: Feature selection via maximizing global information gain for text classification. Knowl. Based Syst. 54, 298–309 (2013)
Zhao, C., Wang, F., Zhang, Y.: Nonlinear process monitoring based on kernel dissimilarity analysis[J]. Control Eng. Pract. 17(1), 221–230 (2009)
Polat, K., Guenes, S.: A novel hybrid intelligent method based on C4.5 decision tree classifier and one-against-all approach for multi-class classification problems. Expert Syst. with Appl. 36(2), 1587–1592 (2009)
Quinlan, J.R.: Induction of decision trees. Mach. Learning 1(1), 81–106 (1986)
Hühn, J., Hüllermeier, E.: FURIA: an algorithm for unordered fuzzy rule induction. Data Min. Knowl. Discov. 19(3), 293–319 (2009)
Yang, L., Wang, K.-L.: Regional differences of environmental efficiency of China’s energy utilization and environmental regulation cost based on provincial panel data and DEA method. Math. Computer Modelling 58(5–6), 1074–1083 (2013)
Acknowledgments
This work was supported by the National Natural Science Foundation of China under Grant No. 71501040 and 2014 Nanjing “321 Introduction Plan” Candidate Project.
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Fansheng, M., Bin, L., Donghui, Y. et al. Energy efficiency evaluation method based on multi-model fusion strategy. Cluster Comput 19, 1937–1949 (2016). https://doi.org/10.1007/s10586-016-0673-7
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DOI: https://doi.org/10.1007/s10586-016-0673-7