IDEAS home Printed from https://ideas.repec.org/a/igg/jmdem0/v11y2020i1p30-50.html
   My bibliography  Save this article

User-Based Load Visualization of Categorical Forecasted Smart Meter Data Using LSTM Network

Author

Listed:
  • Ajay Kumar

    (JSS Academy of Technical Education, Noida, India)

  • Parveen Poon Terang

    (JSS Academy of Technical Education, Noida, India)

  • Vikram Bali

    (JSS Academy of Technical Education, Noida, India)

Abstract

Electrical load forecasting is an essential feature in power systems planning, operation and control. The non-linearity and non-stationary nature of the data, however, poses a challenge in terms of accuracy. This article explores a deep learning technique, a long short-term memory recurrent neural network-based framework to tackle this tricky issue. The proposed machine learning model framework is tested on real time residential smart meter data showing promising results. A web application has also been developed to allow consumers to have access to greater levels of information and facilitate decision-making at their end. The performance of the proposed model is also comprehensively compared to other methods in the field of load forecasting showing more accurate results for the function of forecasting of load on short term basis.

Suggested Citation

  • Ajay Kumar & Parveen Poon Terang & Vikram Bali, 2020. "User-Based Load Visualization of Categorical Forecasted Smart Meter Data Using LSTM Network," International Journal of Multimedia Data Engineering and Management (IJMDEM), IGI Global, vol. 11(1), pages 30-50, January.
  • Handle: RePEc:igg:jmdem0:v:11:y:2020:i:1:p:30-50
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJMDEM.2020010103
    Download Restriction: no
    ---><---

    More about this item

    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:igg:jmdem0:v:11:y:2020:i:1:p:30-50. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.