Abstract Operational risk evaluation for chillers is beneficial for reducing building energy wastage. An accurate reference model can significantly improve the evaluation performance. This paper presents a novel framework for developing and applying the reference model by integrating density clustering and deep learning. Unsupervised density-based spatial clustering of applications with noise (DBSCAN) is adopted to construct the library of operating conditions and recognize operating pattern of chillers. Deep learning approach, deep belief network (DBN), is presented to learn all process data in each operating pattern. Subsequently, multiple DBN models are developed for matching various operating patterns in the condition library. A simple strategy for tuning the hyperparameters of DBN is further presented to obtain a better performance. The prediction and generalization abilities of the proposed approach are validated and compared with multivariate linear regression (MLR), support vector regression (SVR) and radial basis function (RBF) models based on the experimental data obtained from a real screw chiller. Results reveal that the proposed method yields a significant performance advantage than MLR, SVR and RBF models, especially for the extended conditions in actual applications, the mean relative errors of MLR, SVR, RBF and the proposed method are 5.8%, 11.41%, 13.73%, and 2.11%, respectively.

中文翻译：

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基于深度学习的螺杆式冷水机组能效运行风险评估参考模型

摘要 冷水机组运行风险评估有利于减少建筑能源浪费。准确的参考模型可以显着提高评估性能。本文提出了一种通过集成密度聚类和深度学习来开发和应用参考模型的新框架。采用无监督的基于密度的噪声应用空间聚类（DBSCAN）构建运行条件库并识别冷水机组的运行模式。提出了深度学习方法，即深度信念网络 (DBN)，以学习每种操作模式中的所有过程数据。随后，开发了多个 DBN 模型以匹配条件库中的各种操作模式。进一步提出了一种调整 DBN 超参数的简单策略，以获得更好的性能。基于从真实螺杆式冷水机获得的实验数据，对所提出方法的预测和泛化能力进行了验证，并与多元线性回归 (MLR)、支持向量回归 (SVR) 和径向基函数 (RBF) 模型进行了比较。结果表明，所提方法比MLR、SVR和RBF模型具有显着的性能优势，尤其是在实际应用中的扩展条件下，MLR、SVR、RBF与所提方法的平均相对误差分别为5.8%、11.41%、13.73 % 和 2.11%。