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A transfer learning framework using spatiotemporal graph convolutional network and adversarial domain adaptation for cross-district building group energy prediction

Yingjun Ruan Yamei Ma Hua Meng Tingting Xu Yuting Yao Fanyue Qian Chaoliang Wang Wei Liu

建筑模拟（英文版）2026，Vol.19Issue(1)：235-255,21.

建筑模拟（英文版）2026，Vol.19Issue(1)：235-255,21.DOI:10.1007/s12273-025-1370-3

A transfer learning framework using spatiotemporal graph convolutional network and adversarial domain adaptation for cross-district building group energy prediction

Yingjun Ruan ¹Yamei Ma ¹Hua Meng ¹Tingting Xu ¹Yuting Yao ¹Fanyue Qian ¹Chaoliang Wang ²Wei Liu²

作者信息

1. College of Mechanical and Energy Engineering,Tongji University,Shanghai 200092,China
2. State Grid Zhejiang Marketing Service Centre,Hangzhou 310014,China
折叠

摘要

关键词

data scarcity/district building energy consumption prediction/cross-district transfer/transfer learning/spatiotemporal graph convolutional network/adversarial domain adaptation

Key words

data scarcity/district building energy consumption prediction/cross-district transfer/transfer learning/spatiotemporal graph convolutional network/adversarial domain adaptation

引用本文复制引用

Yingjun Ruan,Yamei Ma,Hua Meng,Tingting Xu,Yuting Yao,Fanyue Qian,Chaoliang Wang,Wei Liu..A transfer learning framework using spatiotemporal graph convolutional network and adversarial domain adaptation for cross-district building group energy prediction[J].建筑模拟（英文版）,2026,19(1):235-255,21.

基金项目

This research is supported by the National Key R&D Program of China(No.2023YFC3807100). （No.2023YFC3807100）

建筑模拟（英文版）

ISSN：1996-3599

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