标题： 基于物理和数据驱动方法的概率建筑能耗建模 显示英文标题

标题： Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling

摘要： 建筑能耗建模是优化建筑能源系统性能的关键工具。 历史上，已经探索了广泛的方法——从传统的基于物理的模型到纯粹的数据驱动技术。 最近，结合两种范式优势的混合方法引起了关注。 这些包括学习基于物理模型的替代模型、对模拟数据和观测数据之间的残差进行建模、使用真实世界测量数据微调替代模型、将基于物理的输出作为数据驱动模型的额外输入，以及将基于物理的输出整合到数据驱动模型的损失函数中。 尽管取得了这些进展，仍然存在两个重要的研究空白。 首先，大多数混合方法专注于确定性建模，常常忽视了天气波动和人员行为等因素引起的固有不确定性。 其次，在概率建模框架内缺乏系统的比较。 本研究通过评估五种代表性混合方法在概率建筑能耗建模中的表现，填补了这些空白，重点在于一个实际案例研究中建筑热力学的分位数预测。 我们的结果突出了两个主要发现。 首先，混合方法在不同类型的建筑房间中的表现有所不同，但使用前馈神经网络的残差学习在平均情况下表现最好。 值得注意的是，当应用于分布外测试数据时，残差方法是唯一能产生物理直观预测的模型。 其次，分位数共形预测是在室内温度建模情况下校准分位数预测的有效方法。 显示英文摘要

摘要： Building energy modeling is a key tool for optimizing the performance of building energy systems. Historically, a wide spectrum of methods has been explored -- ranging from conventional physics-based models to purely data-driven techniques. Recently, hybrid approaches that combine the strengths of both paradigms have gained attention. These include strategies such as learning surrogates for physics-based models, modeling residuals between simulated and observed data, fine-tuning surrogates with real-world measurements, using physics-based outputs as additional inputs for data-driven models, and integrating the physics-based output into the loss function the data-driven model. Despite this progress, two significant research gaps remain. First, most hybrid methods focus on deterministic modeling, often neglecting the inherent uncertainties caused by factors like weather fluctuations and occupant behavior. Second, there has been little systematic comparison within a probabilistic modeling framework. This study addresses these gaps by evaluating five representative hybrid approaches for probabilistic building energy modeling, focusing on quantile predictions of building thermodynamics in a real-world case study. Our results highlight two main findings. First, the performance of hybrid approaches varies across different building room types, but residual learning with a Feedforward Neural Network performs best on average. Notably, the residual approach is the only model that produces physically intuitive predictions when applied to out-of-distribution test data. Second, Quantile Conformal Prediction is an effective procedure for calibrating quantile predictions in case of indoor temperature modeling.