0
  • DE
  • EN
  • FR
  • International Database and Gallery of Structures

Advertisement

The Personalized Thermal Comfort Prediction Using an MH-LSTM Neural Network Method

Author(s):
ORCID


Medium: journal article
Language(s): English
Published in: Advances in Civil Engineering, , v. 2024
Page(s): 1-14
DOI: 10.1155/2024/2106137
Abstract:

As demand for indoor thermal comfort increases, occupants’ subjective thermal sensation is becoming an important indicator of the building environment. Traditional models like the predicted mean vote-based model may not be reliable for individual comfort. This study proposed the multihead long short_term memory (LSTM) model to reflect physical and environment-driven data variation. Controlled experiments were conducted with individual temperature measurements of six participants, and the collected data showed significant potential to predict individual thermal comfort using a model trained for each person. The results derived from this study can be utilized, in future, for predicting the thermal comfort and for optimizing the thermal environments using personal body temperature and surrounding environmental data in a space where mainly independent activities are performed. This study contributes to the relevant literature by suggesting a method that predicts thermal comfort based on the multihead LSTM method.

Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.1155/2024/2106137.
  • About this
    data sheet
  • Reference-ID
    10771562
  • Published on:
    29/04/2024
  • Last updated on:
    29/04/2024
 
Structurae cooperates with
International Association for Bridge and Structural Engineering (IABSE)
e-mosty Magazine
e-BrIM Magazine