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Interpretable Urban Building Energy Modeling by Heterogeneous Graph Neural Networks: A Case Study of Residential Blocks in Wuhan.

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Title: Interpretable Urban Building Energy Modeling by Heterogeneous Graph Neural Networks: A Case Study of Residential Blocks in Wuhan.
Authors: Yao, Chuyue1, Li, Dan2, Fang, Sitao3, Li, Jingyi4, jingyili87@whu.edu.cn
Source: Buildings (2075-5309); Jun2026, Vol. 16 Issue 11, p2270, 38p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 194780579
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PubType: Academic Journal
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  Data: Interpretable Urban Building Energy Modeling by Heterogeneous Graph Neural Networks: A Case Study of Residential Blocks in Wuhan.
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  Data: <searchLink fieldCode="JN" term="%22Buildings+%282075-5309%29%22">Buildings (2075-5309)</searchLink>; Jun2026, Vol. 16 Issue 11, p2270, 38p
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=194780579
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    Identifiers:
      – Type: doi
        Value: 10.3390/buildings16112270
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 38
        StartPage: 2270
    Titles:
      – TitleFull: Interpretable Urban Building Energy Modeling by Heterogeneous Graph Neural Networks: A Case Study of Residential Blocks in Wuhan.
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            NameFull: Yao, Chuyue
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            NameFull: Li, Dan
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            NameFull: Fang, Sitao
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            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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              Value: 16
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              Value: 11
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