Reduced-Order Modeling of Transient Events in Data Centers Using Dimensionality Reduction Techniques

The present paper proposes a methodology for the analysis and modelling of transient events in a data center based on real-world high-resolution voltage and current measurements. The proposed approach includes the identification of relevant events, temporal segmentation, multivariate representation, and the application of dimensionality reduction techniques to obtain compact representations of the observed dynamics. A total of eight representative transient events were identified in the available dataset. These events were characterized by short-duration disturbances of moderate magnitude, which is consistent with the operation of highly reliable infrastructures. Three main methods were evaluated: PCA/POD, Kernel PCA, and Autoencoder. The results show that all three approaches are capable of reconstructing the event dynamics with low reconstruction errors, suggesting the presence of a low-dimensional structure in the analyzed data. Among the evaluated methods, PCA/POD provided the best balance between compactness, interpretability, and computational efficiency, while Kernel PCA and Autoencoder offered advantages for representing nonlinear behaviors. The results provide case-study evidence on the feasibility of constructing reduced-order representations for the analysis and monitoring of transient events in data centers under limited-data conditions.