Efficient prediction of indoor temperature distribution (ITD) is crucial for various building-related applications, particularly in real-time thermal management. Despite efforts from both physics-based and data-driven perspectives, ITD prediction still faces challenges, particularly in balancing prediction accuracy with computational speed and addressing the need for extensive high-quality data in practical applications. To address these challenges, this study develops a hybrid temperature distribution prediction model (HTDPM) that integrates deep learning with physical mechanisms, enabling the effective capture of spatial-temporal dependencies and uncertainties with limited input parameters, achieving a balance between prediction accuracy and computational efficiency. Meanwhile, a novel transfer learning approach is applied to HTDPM (HTDPM-TL), significantly reducing data requirements and enhancing model generalization across various practical scenarios. Besides, orthogonal and randomized experiments are designed to simulate multiple real-world scenarios, thus constructing an extensive source domain dataset. The HTDPM-TL was tested in various real-world scenarios with several baseline methods, demonstrating an average RMSE of 0.794∘C, a computational time of 0.289 s, and limited data volume. The computational speed was improved by three orders of magnitude compared to CFD simulations, and the prediction resolution was enhanced threefold compared to traditional data-driven models. These results highlight the potential of HTDPM-TL to achieve an optimal trade-off between prediction accuracy, computational efficiency, and data requirement.

中文翻译：

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一种基于机制和数据驱动的混合模型，用于通过迁移学习进行高效的室内温度分布预测


室内温度分布 （ITD） 的有效预测对于各种与建筑相关的应用至关重要，尤其是在实时热管理中。尽管从基于物理和数据驱动的角度做出了努力，但 ITD 预测仍然面临挑战，特别是在平衡预测精度与计算速度以及满足实际应用中对大量高质量数据的需求方面。为了应对这些挑战，本研究开发了一种混合温度分布预测模型 （HTDPM），该模型将深度学习与物理机制相结合，能够以有限的输入参数有效捕获时空依赖性和不确定性，实现预测精度和计算效率之间的平衡。同时，将一种新的迁移学习方法应用于 HTDPM （HTDPM-TL），显著降低了数据需求并增强了各种实际场景中的模型泛化。此外，设计了正交和随机实验来模拟多个真实场景，从而构建了一个广泛的源域数据集。HTDPM-TL 在各种真实场景中使用多种基线方法进行了测试，结果表明平均 RMSE 为 0.794∘C，计算时间为 0.289 s，数据量有限。与传统的数据驱动模型相比，计算速度提高了三个数量级，预测分辨率提高了三倍。这些结果突出了 HTDPM-TL 在预测精度、计算效率和数据需求之间实现最佳权衡的潜力。