IDEAS home Printed from https://ideas.repec.org/h/spr/spochp/978-3-030-28565-4_25.html
   My bibliography  Save this book chapter

L 1 Optimization for Sparse Structure Machine Learning Based Electricity Demand Prediction

In: Optimization in Large Scale Problems

Author

Listed:
  • Dinh Hoa Nguyen

    (Kyushu University
    Kyushu University)

Abstract

This chapter presents a study on L 1 optimization for the problem of electricity demand prediction based on machine learning. This electricity demand prediction is very important for balancing the power supply and demand in smart power grids, a critical infrastructure in smart societies, where the energy consumption increases every year. Due to its robustness to outliers, L 1 optimization is suitable to deal with challenges posed by the uncertainties on weather forecast, consumer behaviors, and renewable generation. Therefore, L 1 optimization will be utilized in this research for machine learning techniques, which are based on artificial neural networks (ANNs), to cope with the nonlinearity and uncertainty of demand curves. In addition, two approaches, namely L 2 and alternating direction method of multiplier (ADMM), will be used to solve the L 1 optimization problem and their performances will be compared to find out which one is better. Test cases for realistic weather and electricity consumption data in Tokyo will be introduced to demonstrate the efficiency of the employed optimization approaches.

Suggested Citation

  • Dinh Hoa Nguyen, 2019. "L 1 Optimization for Sparse Structure Machine Learning Based Electricity Demand Prediction," Springer Optimization and Its Applications, in: Mahdi Fathi & Marzieh Khakifirooz & Panos M. Pardalos (ed.), Optimization in Large Scale Problems, pages 305-317, Springer.
    • Handle: RePEc:spr:spochp:978-3-030-28565-4_25
    DOI: 10.1007/978-3-030-28565-4_25
    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.

    More about this item

    Keywords

    Electric demand prediction; Machine learning; Radial basis function neural network; L1 optimization; Alternating direction method of multipliers;
    All these keywords.

    JEL classification:

    • L1 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance

    Statistics

    Access and download statistics

    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:spochp:978-3-030-28565-4_25. 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.