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Comprehensive evaluation of regional clean energy development levels based on principal component analysis and rough set theory

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  • He, Yongxiu
  • Pang, Yuexia
  • Zhang, Qi
  • Jiao, Zhe
  • Chen, Qian

Abstract

After the strategic concept of global energy Internet constructed, it has produced a strong response in the world. In recent years, countries around the world are actively promoting energy transformation and have developed clean energy development goals and plans. This paper constructs a comprehensive evaluation index system for the level of clean energy development by considering policies and regulations, energy supply, environmental impact, energy consumption, technology, economy and so on. At the same time, China, Germany, the United States and other countries are selected as evaluation objects. On the basis of searching, processing and analyzing a large number of data, firstly, the research uses the principal component analysis method to carry out the correlation cluster analysis of the index and then uses the rough set method to assign the weight of the extracted principal components. Finally, the weight of each index in the index system is calculated. The results of the comprehensive evaluation of each country are compared and analyzed, which shows that the weight determination method based on rough set theory and principal component analysis is more reasonable and objective. At the end of the paper, some suggestions are proposed to promote the development of clean energy.

Suggested Citation

  • He, Yongxiu & Pang, Yuexia & Zhang, Qi & Jiao, Zhe & Chen, Qian, 2018. "Comprehensive evaluation of regional clean energy development levels based on principal component analysis and rough set theory," Renewable Energy, Elsevier, vol. 122(C), pages 643-653.
    • Handle: RePEc:eee:renene:v:122:y:2018:i:c:p:643-653
    DOI: 10.1016/j.renene.2018.02.028
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    References listed on IDEAS

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    1. Shen, Chenhua, 2017. "A comparison of principal components using TPCA and nonstationary principal component analysis on daily air-pollutant concentration series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 453-464.
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    Cited by:

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    10. Xiaowei Fu & Yanlin Liu & Xi Li, 2020. "Source Diagnosis of Solid Oxide Fuel Cell System Oscillation Based on Data Driven," Energies, MDPI, vol. 13(16), pages 1-13, August.
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