BuildSTG: A Multi-building Energy Load Forecasting Method using Spatio-Temporal Graph Neural Network

• URL: http://arxiv.org/abs/2507.20838v1
• Date: Mon, 28 Jul 2025 13:47:36 GMT
• Title: BuildSTG: A Multi-building Energy Load Forecasting Method using Spatio-Temporal Graph Neural Network
• Abstract summary: Buildings with similar features often share energy, reflected by spatial dependencies in their operational data.<n>We propose a multi-building prediction approach using graph-temporal graph neural networks.<n> Experiments on the Building Data Genome Project 2 dataset confirm superior performance over baselines.
• Score: 7.196781503036909
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Due to the extensive availability of operation data, data-driven methods show strong capabilities in predicting building energy loads. Buildings with similar features often share energy patterns, reflected by spatial dependencies in their operational data, which conventional prediction methods struggle to capture. To overcome this, we propose a multi-building prediction approach using spatio-temporal graph neural networks, comprising graph representation, graph learning, and interpretation. First, a graph is built based on building characteristics and environmental factors. Next, a multi-level graph convolutional architecture with attention is developed for energy prediction. Lastly, a method interpreting the optimized graph structure is introduced. Experiments on the Building Data Genome Project 2 dataset confirm superior performance over baselines such as XGBoost, SVR, FCNN, GRU, and Naive, highlighting the method's robustness, generalization, and interpretability in capturing meaningful building similarities and spatial relationships.

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