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Comprehensive Potential Evaluation for the Rooftop PV Development Based on IPO

In: Introduction to Internet of Things in Management Science and Operations Research

Author

Listed:
  • Qiong Shen

    (College of Architecture and Urban-Rural Planning, Sichuan Agricultural University)

  • Xue Wan

    (College of Architecture and Urban-Rural Planning, Sichuan Agricultural University)

  • Wanyu Ni

    (College of Architecture and Urban-Rural Planning, Sichuan Agricultural University)

  • Benjamin Lev

    (LeBow College of Business, Drexel University)

  • Lu Gan

    (College of Architecture and Urban-Rural Planning, Sichuan Agricultural University
    LeBow College of Business, Drexel University
    Business School, Sichuan University)

Abstract

Recently, China’s Ministry of Finance continues to expand China’s photovoltaic (i.e., PV) power generation market to alleviate the energy problem. With the rapid development of the Internet, Internet of Things (IOT), and smart phones, more people express their views and concerns about rooftop PV through network channels. In this chapter, through keyword mining and extraction of Internet public opinion (IPO) data combined with literature research, a method for evaluating the comprehensive potential of rooftop PV is proposed. Then, the gray relation projection method (GRPM) is used to evaluate and rank the comprehensive potential of different objects. The case study selected five types of land for evaluation. The results show that the industrial land has the greatest potential for rooftop PV. This method can help decision makers to have a clearer understanding of PV development in the future. It is of great significance for promoting China’s PV industry development.

Suggested Citation

  • Qiong Shen & Xue Wan & Wanyu Ni & Benjamin Lev & Lu Gan, 2021. "Comprehensive Potential Evaluation for the Rooftop PV Development Based on IPO," International Series in Operations Research & Management Science, in: Fausto Pedro García Márquez & Benjamin Lev (ed.), Introduction to Internet of Things in Management Science and Operations Research, pages 259-293, Springer.
    • Handle: RePEc:spr:isochp:978-3-030-74644-5_13
    DOI: 10.1007/978-3-030-74644-5_13
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