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Sequential attention deep learning architecture with unsupervised pre-training for interpretable and accurate building energy prediction with limited data

Author(s): (Department of Architectural Engineering, Kyung Hee University, Yongin-si, Gyeonggi-do, Republic of Korea)
(Department of Architectural Engineering, Kyung Hee University, Yongin-si, Gyeonggi-do, Republic of Korea)
(Purification Research Center, Samsung Electronics, Suwon-si, Gyeonggi-do, Republic of Korea)
(Department of Architectural Engineering, Kyung Hee University, Yongin-si, Gyeonggi-do, Republic of Korea)
Medium: journal article
Language(s): English
Published in: Journal of Asian Architecture and Building Engineering
Page(s): 1-17
DOI: 10.1080/13467581.2023.2278460
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: 10.1080/13467581.2023.2278460.
  • About this
    data sheet
  • Reference-ID
    10754440
  • Published on:
    14/01/2024
  • Last updated on:
    14/01/2024
 
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