用物理规律指导深度学习，精准识别热泵系统运行压力。
Physics-Guided Deep Learning for Heat Pump Stress Detection: A Comprehensive Analysis on When2Heat Dataset
- 结合热力学原理筛选特征并定义分类标准
- 在跨国家数据集上达到78.5%验证准确率
- 适合能源系统监控与智能运维人员使用
热泵系统是现代节能建筑的关键组件，但其运行压力检测因复杂的热力学交互和真实场景数据有限而困难。本文基于包含131,483个样本、656个特征的When2Heat数据集，提出一种物理引导的深度神经网络（PG-DNN）方法，用于热泵应力分类。该方法融合物理引导特征选择与类别定义，采用5层隐藏结构的神经网络，结合双正则化策略。模型在测试集上达到78.1%准确率，在验证集上达78.5%，相比浅层网络提升5.0%，相比有限特征集提升4.0%，相比单正则化提升2.0%。消融实验验证了物理引导特征选择、变量阈值设定及跨国家能源模式分析的有效性。系统具备生产部署能力，含181,348个参数，使用AMD Ryzen 9 7950X与RTX 4080硬件训练耗时720秒。
原文摘要 · Abstract (English)
Heat pump systems are critical components in modern energy-efficient buildings, yet their operational stress detection remains challenging due to complex thermodynamic interactions and limited real-world data. This paper presents a novel Physics-Guided Deep Neural Network (PG-DNN) approach for heat pump stress classification using the When2Heat dataset, containing 131,483 samples with 656 features across 26 European countries. The methodology integrates physics-guided feature selection and class definition with a deep neural network architecture featuring 5 hidden layers and dual regularization strategies. The model achieves 78.1\% test accuracy and 78.5% validation accuracy, demonstrating significant improvements over baseline approaches: +5.0% over shallow networks, +4.0% over limited feature sets, and +2.0% over single regularization strategies. Comprehensive ablation studies validate the effectiveness of physics-guided feature selection, variable thresholding for realistic class distribution, and cross-country energy pattern analysis. The proposed system provides a production-ready solution for heat pump stress detection with 181,348 parameters and 720 seconds training time on AMD Ryzen 9 7950X with RTX 4080 hardware.
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