TY - JOUR
T1 - Autonomous-Jump-ODENet
T2 - Identifying Continuous-Time Jump Systems for Cooling-System Prediction
AU - Yuan, Zhaolin
AU - Wang, Yewan
AU - Ban, Xiaojuan
AU - Ning, Chunyu
AU - Dai, Hong Ning
AU - Wang, Hao
N1 - Funding information:
This research was funded by National Natural Science Foundation of China with grant (No.61873299, No.61902022, No.61972028), Scientific and Technological Innovation Foundation of Shunde Graduate School, USTB (No.BK21BF002), Macao Science and Technology Development Fund under Macao Funding Scheme for Key R & D Projects (0025/2019/AKP), Fundamental Research Funds for the Central Universities of China (FRF-TP-20- 061A1Z). (Corresponding author: Xiaojuan Ban and Hong-Ning Dai.)
Publisher Copyright:
© 2022 IEEE.
PY - 2022/9/19
Y1 - 2022/9/19
N2 - 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 ODENet 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.
AB - 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 ODENet 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.
KW - Contained cooling system
KW - jump system modeling
KW - multi-input-multi-output system prediction
KW - ordinary differential equations network
UR - http://www.scopus.com/inward/record.url?scp=85139407558&partnerID=8YFLogxK
U2 - 10.1109/TII.2022.3207835
DO - 10.1109/TII.2022.3207835
M3 - Article
JO - IEEE Transactions on Industrial Informatics
JF - IEEE Transactions on Industrial Informatics
SN - 1551-3203
ER -