Deep learning based reference model for operational risk evaluation of screw chillers for energy efficiency

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.