Autonomous-Jump-ODENet: Identifying Continuous-Time Jump Systems for Cooling-System Prediction
Published in IEEE Transactions on Industrial Informatics, 2023
This study presents the Autonomous Jump Ordinary Differential Equation Net (AJ-ODENet), a continuous-time modeling framework designed for complex industrial systems exhibiting periodic jump dynamics. AJ-ODENet combines multiple Hierarchical ODENets to capture stage-specific behaviors from irregularly sampled data, while a stage transition predictor enables autonomous transitions during open-loop simulation. An encoder–decoder implementation of the model is applied to a real data center cooling system, using multivariate inputs such as server power and environmental temperature to simulate runtime behavior. Results show accurate prediction of energy consumption within a 5% relative error, and the model further informs optimal cooling temperature settings, enabling 6%–25% reduction in energy usage.
Recommended citation: Zhaolin Yuan, Yewan Wang, Xiaojuan Ban, Chunyu Ning, Hong-Ning Dai, Hao Wang, "Autonomous-Jump-ODENet: Identifying Continuous-Time Jump Systems for Cooling-System Prediction." IEEE Transactions on Industrial Informatics, 2023.
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