Chillers account for the majority of energy consumption in central air-conditioning refrigeration stations. However, conventional models lack strong out-of-sample generalization, making it difficult to accurately reflect chiller performance variations under multiple operating conditions. Consequently, there is a lack of reliable multi-condition performance data to support energy-efficient regulation of chillers and optimization of refrigeration station control. To address this issue, this study proposes a physics-constrained broad learning System (PCBLS) method by introducing an error backpropagation mechanism and a customized physics-based loss function into the broad learning framework. This approach enhances the out-of-sample generalization of chiller models, enabling accurate prediction of chiller performance under unseen operating conditions based on measured data from existing conditions. The core idea is to ensure that the model’s predictions for unknown conditions remain consistent with physical laws. Experimental results demonstrate that, compared to methods without out-of-sample generalization enhancement, the proposed approach reduces MAE by 53.38%, RMSE by 55.47%, and improves R2 by 19.62%, while decreasing the custom physics-based loss by approximately 99.37%. Additionally, the method maintains high accuracy while achieving fast training speeds.

中文翻译：

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基于物理约束广泛学习系统的在线冷水机组外推场景动态模型

冷水机组占中央空调制冷站的大部分能源消耗。然而，传统模型缺乏强大的样本外泛化，因此难以准确反映多种运行条件下的冷水机组性能变化。因此，缺乏可靠的多条件性能数据来支持冷水机组的节能调节和优化制冷站控制。为了解决这个问题，本研究通过在广泛学习框架中引入误差反向传播机制和定制的基于物理的损失函数，提出了一种物理约束的广泛学习系统 （PCBLS） 方法。这种方法增强了冷水机组模型的样本外泛化，从而能够根据现有条件的测量数据准确预测不可见运行条件下的冷水机组性能。其核心思想是确保模型对未知条件的预测与物理定律保持一致。实验结果表明，与没有样本外泛化增强的方法相比，所提出的方法将 MAE 降低了 53.38%，RMSE 降低了 55.47%，R2 提高了 19.62%，同时将基于自定义物理的损失降低了约 99.37%。此外，该方法在实现快速训练速度的同时保持了高精度。