IDEAS home Printed from https://ideas.repec.org/h/spr/ssrchp/978-3-030-31375-3_9.html
   My bibliography  Save this book chapter

A Hidden Markov Model for a Day-Ahead Prediction of Half-Hourly Energy Demand in Romanian Electricity Market

In: Advances in Reliability Analysis and its Applications

Author

Listed:
  • Anatoli Paul Ulmeanu

    (Polytechnic University of Bucharest)

Abstract

In this chapter we introduce an original approach to predict a day-ahead energy demand, based on machine learning and pattern recognition. Our Hidden Markov Model (HMM) is a simple and explainable model that uses integer sequences to define emission probability distributions attached to states. We develop a Mathematica code which relies on the maximum likelihood principle in HMM environment. Based on these exploratory results, we conclude that the HMM approach is an efficient way in modeling short-term/day-ahead energy demand prediction, especially during peak period(s) and in accounting for the inherent stochastic nature of demand conditions. The model can be easily extended to predict energy demand values for more than one day in the future. However, the accuracy of such predictions would decrease as we expect.

Suggested Citation

  • Anatoli Paul Ulmeanu, 2020. "A Hidden Markov Model for a Day-Ahead Prediction of Half-Hourly Energy Demand in Romanian Electricity Market," Springer Series in Reliability Engineering, in: Mangey Ram & Hoang Pham (ed.), Advances in Reliability Analysis and its Applications, pages 307-317, Springer.
    • Handle: RePEc:spr:ssrchp:978-3-030-31375-3_9
    DOI: 10.1007/978-3-030-31375-3_9
    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.

    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:ssrchp:978-3-030-31375-3_9. 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.