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<p>Accurate and efficient prediction of energy consumption in district building groups is a critical foundation for effective energy management and achieving building energy-saving and emission reduction goals. In recent years, deep learning methods have been widely applied to building energy consumption prediction. However, most existing approaches rely heavily on abundant historical data for model training. In practice, data scarcity, due to factors such as data privacy concerns or newly constructed buildings, poses a significant challenge. Additionally, many methods fail to account for spatial dependencies between buildings. To address these limitations, this paper proposes a novel prediction framework based on a spatiotemporal graph convolutional network integrated with adversarial domain adaptation, specifically designed for energy consumption prediction in district building groups under data-scarce conditions. The approach combines a graph convolutional network (GCN) based on an adaptive adjacency matrix with a graph attention network based on a dynamic time warping (DTW) correlation matrix to capture the spatiotemporal features of energy consumption, across the building group. The proposed model is first pre-trained on a data-rich source domain. Then, adversarial domain adaptation is employed to extract domain-invariant spatiotemporal features from both the source and target domains. This enables a “multi-buildings to multi-buildings” transfer of predictive knowledge from the source domain to the target domain. Extensive experiments were conducted using historical energy consumption data from two real-world district building groups to validate the effectiveness and robustness of the proposed method. Results demonstrate that adversarial domain adaptation can successfully leverage domain-invariant knowledge to improve prediction accuracy in data-scarce scenarios. Compared to models trained directly on the target domain without transfer learning, the proposed method reduces prediction error by 2.36% to 16.08%. Furthermore, when benchmarked against various mainstream transfer learning methods, the adversarial domain adaptation approach more effectively bridges the domain gap, yielding superior predictive performance. Additionally, to simulate real-world data collection scenarios, the impact of varying training data ratios on the proposed model performance was also evaluated.</p>

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

  • 期刊论文

  • 发表日期：

    2026年1月20日

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

摘要

Accurate and efficient prediction of energy consumption in district building groups is a critical foundation for effective energy management and achieving building energy-saving and emission reduction goals. In recent years, deep learning methods have been widely applied to building energy consumption prediction. However, most existing approaches rely heavily on abundant historical data for model training. In practice, data scarcity, due to factors such as data privacy concerns or newly constructed buildings, poses a significant challenge. Additionally, many methods fail to account for spatial dependencies between buildings. To address these limitations, this paper proposes a novel prediction framework based on a spatiotemporal graph convolutional network integrated with adversarial domain adaptation, specifically designed for energy consumption prediction in district building groups under data-scarce conditions. The approach combines a graph convolutional network (GCN) based on an adaptive adjacency matrix with a graph attention network based on a dynamic time warping (DTW) correlation matrix to capture the spatiotemporal features of energy consumption, across the building group. The proposed model is first pre-trained on a data-rich source domain. Then, adversarial domain adaptation is employed to extract domain-invariant spatiotemporal features from both the source and target domains. This enables a “multi-buildings to multi-buildings” transfer of predictive knowledge from the source domain to the target domain. Extensive experiments were conducted using historical energy consumption data from two real-world district building groups to validate the effectiveness and robustness of the proposed method. Results demonstrate that adversarial domain adaptation can successfully leverage domain-invariant knowledge to improve prediction accuracy in data-scarce scenarios. Compared to models trained directly on the target domain without transfer learning, the proposed method reduces prediction error by 2.36% to 16.08%. Furthermore, when benchmarked against various mainstream transfer learning methods, the adversarial domain adaptation approach more effectively bridges the domain gap, yielding superior predictive performance. Additionally, to simulate real-world data collection scenarios, the impact of varying training data ratios on the proposed model performance was also evaluated.