Skip to main content

CORRECTION article

Front. Energy Res., 22 August 2024
Sec. Sustainable Energy Systems
Volume 12 - 2024 | https://doi.org/10.3389/fenrg.2024.1477060
This article is part of the Research Topic Low to medium-grade thermal energy utilization in renewable energies and industries View all 3 articles

Corrigendum: An experimental analysis and deep learning model to assess the cooling performance of green walls in humid climates

This article is a correction to:

  1. An experimental analysis and deep learning model to assess the cooling performance of green walls in humid climates

    1. Read original article
Abdollah Baghaei Daemei Abdollah Baghaei Daemei1*Tomasz BradeckiTomasz Bradecki2Alina PancewiczAlina Pancewicz2Amirali RazzaghipourAmirali Razzaghipour3Amiraslan DarvishAmiraslan Darvish4Asma JamaliAsma Jamali5Seyedeh Maryam AbbaszadeganSeyedeh Maryam Abbaszadegan6Reza Askarizad,Reza Askarizad7,8Mostafa KazemiMostafa Kazemi9Ayyoob Sharifi,Ayyoob Sharifi10,11
  • 1School of Built Environment, Massey University, Auckland, New Zealand
  • 2Faculty of Architecture, Silesian University of Technology, Gliwice, Poland
  • 3School of Design and the Built Environment, Curtin University, Perth, WA, Australia
  • 4Department of Environmental Conservation, Sustainable Building Systems Engineering, University of Massachusetts Amherst, Amherst, MA, United States
  • 5Department of Architectural Engineering, Rahbord Shomal University, Rasht, Iran
  • 6Department of Engineering, La Trobe University, Melbourne, VIC, Australia
  • 7Department of Urban and Regional Planning, Universidad Politécnica de Madrid, Madrid, Spain
  • 8Department of Civil and Environmental Engineering and Architecture (DICAAR), University of Cagliari, Cagliari, Italy
  • 9School of Environment and Sustainability, Royal Roads University, Victoria, BC, Canada
  • 10Center for Peaceful and Sustainable Futures (CEPEAS), The IDEC Institute, Hiroshima University, Hiroshima, Hiroshima, Japan
  • 11School of Architecture and Design, Lebanese American University, Beirut, Beirut, Lebanon

A Corrigendum on
An experimental analysis and deep learning model to assess the cooling performance of green walls in humid climates

by Daemei AB, Bradecki T, Pancewicz A, Razzaghipour A, Jamali A, Abbaszadegan SM, Askarizad R, Kazemi M and Sharifi A (2024). Front. Energy Res. 12:1447655. doi: 10.3389/fenrg.2024.1447655

Error in Author List

In the published article, there was an error in the Author list, and author Amiraslan Darvish was erroneously excluded. The corrected Author list appears below.

“Abdollah Baghaei Daemei1*, Tomasz Bradecki2, Alina Pancewicz2, Amirali Razzaghipour3, Amiraslan Darvish4, Asma Jamali5, Seyedeh Maryam Abbaszadegan6, Reza Askarizad7,8, Mostafa Kazemi9, Ayyoob Sharifi10,11”

Error in Affiliation

In the published article, there was an error in Affiliation 9. Instead of “Department of Architecture, Tabriz Branch, Islamic Azad University, Tabriz, Iran,” it should be “School of Environment and Sustainability, Royal Roads University, Victoria, Canada.”

The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Keywords: green walls, experimental measurement, humid climate, cooling performance, ambient air temperature, urban heat island, deep learning model, artificial neural network

Citation: Baghaei Daemei A, Bradecki T, Pancewicz A, Razzaghipour A, Darvish A, Jamali A, Abbaszadegan SM, Askarizad R, Kazemi M and Sharifi A (2024) Corrigendum: An experimental analysis and deep learning model to assess the cooling performance of green walls in humid climates. Front. Energy Res. 12:1477060. doi: 10.3389/fenrg.2024.1477060

Received: 07 August 2024; Accepted: 09 August 2024;
Published: 22 August 2024.

Approved by:

Frontiers Editorial Office, Frontiers Media SA, Switzerland

Copyright © 2024 Baghaei Daemei, Bradecki, Pancewicz, Razzaghipour, Darvish, Jamali, Abbaszadegan, Askarizad, Kazemi and Sharifi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Abdollah Baghaei Daemei, YWJhZ2hhZWlAbWFzc2V5LmFjLm56

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.