IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i5p2419-d1086508.html
   My bibliography  Save this article

PMV Dimension Reduction Utilizing Feature Selection Method: Comparison Study on Machine Learning Models

Author

Listed:
  • Kyung-Yong Park

    (Center for Housing Environment Research and Innovation of Korea Land and Housing Research Institute, 66, Raon-ro, Sejong 30065, Republic of Korea)

  • Deok-Oh Woo

    (College of Engineering, Lawrence Technological University, 21000 W 10 Mile Rd., Southfield, MI 48075, USA)

Abstract

Since P.O. Fanger proposed PMV, it has been the most widely used index to estimate thermal comfort. However, in some cases, it is challenging to measure all six parameters within indoor spaces, which are essential for PMV estimation; a couple of parameters, such as Clo or Met, tend to show a large deviation in accuracy. For these reasons, several studies have suggested methods to estimate PMV but their accuracies were significantly compromised. In this vein, this study proposed a way to reduce the dimensions of parameters for PMV prediction utilizing the machine learning method, in order to provide fast PMV calculations without compromising its prediction accuracy. Throughout this study, the most influential features for PMV were pinpointed using PCA, Best Subset, and the Gini Importance, with each model compared to the others. The results showed that PCA and ANN achieved the highest accuracy of 89.70%, and the combination of Best Subset and Random Forest showed the fastest prediction performance among all.

Suggested Citation

  • Kyung-Yong Park & Deok-Oh Woo, 2023. "PMV Dimension Reduction Utilizing Feature Selection Method: Comparison Study on Machine Learning Models," Energies, MDPI, vol. 16(5), pages 1-14, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2419-:d:1086508
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/5/2419/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/5/2419/
    Download Restriction: no
    ---><---

    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:gam:jeners:v:16:y:2023:i:5:p:2419-:d:1086508. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.