一种基于深度学习模型训练与评估的零能耗建筑监测方法
By combining deep learning models with technologies such as molecular dynamics and graph attention networks, the problems of correlation of building material micro-features and energy scheduling adaptation in zero-energy buildings have been solved, achieving precise location of energy loss and efficient energy utilization, and improving the management level of zero-energy buildings.
CN122414940APending Publication Date: 2026-07-17FUJIAN JIUDING CONSTR GRP CO LTD +2
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Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- FUJIAN JIUDING CONSTR GRP CO LTD
- Filing Date
- 2026-06-22
- Publication Date
- 2026-07-17
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Figure CN122414940A_ABST
Abstract
本发明公开了一种基于深度学习模型训练与评估的零能耗建筑监测方法，包括以下步骤：构建包含建材属性数据、环境参数数据、建筑能耗实时数据及建筑结构与设备运行状态数据的基础数据库，对数据进行清洗和标准化处理；运用分子动力学模拟获取建材老化微观参数并编码为深度学习先验特征向量，设计U‑Net变体架构深度神经网络融合微观特征与宏观能耗数据，构建“微观材料劣化→宏观建筑能耗损失”端到端映射模型，本发明，提升能耗漏洞定位精度：通过分子动力学模拟获取建材老化微观参数，并与宏观能耗数据融合构建端到端映射模型，能精准定位建筑不同区域因建材劣化导致的能耗贡献度，有效减少无效能耗损失，为建筑能耗优化提供精准依据。
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