Autonomous-Jump-ODENet: Identifying Continuous-Time Jump Systems for Cooling-System Prediction

Zhaolin Yuan, Yewan Wang, Xiaojuan Ban, Chunyu Ning, Hong-Ning Dai, Hao Wang

Abstract: Periodic Jump processes commonly occur in complex industrial systems. As the systems vary dynamically between different stages, learning their dynamics in an unified model, so as to forecast and simulation accurately is challenging. In this study, we propose autonomous jump ordinary differential equation net (AJ-ODENet) to learn the continuous-time periodic jump system. The model consists of several Hierarchical ODENets (H-ODENets) and a stage transition predictor. Each H-ODENet is an advanced version of ordinary differential equations network to individually learn specific dynamics in each stage from irregularly sampled sequence data. The stage transition predictor realizes autonomous stage transition during open-loop simulation. Furthermore, an encoder–decoder framework built on AJ-ODENet is employed on a real cooling system of data center to simulate some variables in runtime. With multivariate data given, such as server power and environmental temperature, the model can simulate the working patterns as in reality, and the relative error of the predicted energy consumption is within 5%. Furthermore, based on the model, we infer the optimal cooling temperature settings under different heat loads. The simulation results indicate that 6%–25% of cooling energy consumption can be optimized.