基于大数据与AI预测的楼宇集群能耗动态优化方法及系统
By constructing a building energy consumption coupling graph and graph convolutional network, and combining physical constraint neural networks with automatic differential calculation, an executable future control sequence is generated, which solves the problem of difficulty in characterizing the relationship between building nodes in building cluster energy consumption management, and realizes dynamic optimization and precise management of building cluster energy consumption.
CN121724203BActive Publication Date: 2026-07-17CHINA NAT INST OF STANDARDIZATION
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA NAT INST OF STANDARDIZATION
- Filing Date
- 2025-12-18
- Publication Date
- 2026-07-17
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Figure CN121724203B_ABST
Abstract
本发明公开了基于大数据与AI预测的楼宇集群能耗动态优化方法及系统，涉及建筑能源管理技术领域，包括，将每栋楼宇视作楼宇节点，以楼宇设备物理关系作为边，将楼宇集群抽象为楼宇图结构，构建出楼宇能耗耦合图谱；将楼宇能耗耦合图谱和楼宇运行状态数据包输入图卷积网络模型，生成楼宇能耗调节预测包；将楼宇能耗调节预测包与楼宇运行状态数据包同步输入物理约束神经网络，通过自动微分计算局部敏感度，并在局部敏感度上构建并求解控制动态优化问题，输出未来控制序列；本发明实现了能耗预测与动态控制的有效衔接，提升了楼宇集群能耗优化的协同性与可实施性。
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