IDEAS home Printed from https://ideas.repec.org/h/spr/mgmchp/978-981-16-8965-9_5.html
   My bibliography  Save this book chapter

Big Data Analysis of Energy Economics in Photovoltaic Power Generation Market

In: Big Data in Energy Economics

Author

Listed:
  • Hui Liu

    (Central South University)

  • Nikolaos Nikitas

    (University of Leeds)

  • Yanfei Li

    (Hunan Agricultural University)

  • Rui Yang

    (Central South University)

Abstract

As a clean, renewable energy, photovoltaic power generation has a rapid growth in its application range and installed capacity, and has provided great help for alleviating the energy crisis. Developing photovoltaic power generation can exploit the untapped and abundant solar energy resources and contribute to regional economic development. However, photovoltaic power generation systems still face many challenges such as randomness, uncertainty, and intermittence. The fluctuations and instability can easily cause impact and affect stability. In addition, it will increase the difficulty of the economic dispatch of the power system. This chapter first introduces the photovoltaic power generation system, then discusses and summarizes the big data prediction technology in the system, and gives prediction examples. An economic dispatch model involving photovoltaic power generation is sequentially presented, and finally, the solving algorithm is discussed.

Suggested Citation

  • Hui Liu & Nikolaos Nikitas & Yanfei Li & Rui Yang, 2022. "Big Data Analysis of Energy Economics in Photovoltaic Power Generation Market," Management for Professionals, in: Big Data in Energy Economics, chapter 0, pages 117-136, Springer.
    • Handle: RePEc:spr:mgmchp:978-981-16-8965-9_5
    DOI: 10.1007/978-981-16-8965-9_5
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xu, Mengjie & Li, Xiang & Li, Qianwen & Sun, Chuanwang, 2024. "LNBi-GRU model for coal price prediction and pattern recognition analysis," Applied Energy, Elsevier, vol. 365(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:mgmchp:978-981-16-8965-9_5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.