DFA-ODENets:面向周期多阶段复杂系统的预测仿真框架  

DFA-ODENets:Research on predictive simulation framework for periodic multistage complex systems

作　　者：李潇睿 宁春宇 袁兆麟 班晓娟[1,2,3,4] LI Xiaorui;NING Chunyu;YUAN Zhaolin;BAN Xiaojuan(Beijing Advanced Innovation Center for Materials Genome Engineering,University of Science and Technology Beijing,Beijing 100083,China;School of Intelligence Science and Technology,University of Science and Technology Beijing,Beijing 100083,China;Beijing Key Laboratory of Knowledge Engineering for Materials Science,University of Science and Technology Beijing,Beijing 100083,China;Institute of Materials Intelligent Technology,Liaoning Academy of Materials,Shenyang 110004,China)

机构地区：[1]北京科技大学北京材料基因工程高精尖创新中心,北京100083 [2]北京科技大学智能科学与技术学院,北京100083 [3]北京科技大学材料领域知识工程北京市重点实验室,北京100083 [4]辽宁材料实验室,材料智能技术研究所,沈阳110004

出　　处：《工程科学学报》2024年第1期137-147,共11页Chinese Journal of Engineering

基　　金：科技创新2030-重大项目(2022ZD0118001);国家自然科学基金资助项目(U22A2022)。

摘　　要：部分复杂系统受内外部因素影响在运行时会呈现出周期性的阶段变化,且在不同阶段具有完全不同的动态特性.因此在使用数据驱动方法解决此类系统的预测和仿真问题时,使用单一结构模型难以准确地学习系统在不同阶段的动态特性.本研究提出了基于确定性有限状态机-常微分方程网络的预测仿真框架(DFA-ODENets),以建模周期多阶段系统.该模型由多个ODENet组成,每个ODENet能够从不规则采样的序列数据中学习系统在各个阶段内的动态特性.同时模型集成了基于确定性有限状态自动机思想的阶段转换预测器以实现模型预测时在不同阶段之间自动转换.最后,将DFA-ODENet框架应用于某计算中心制冷系统的预测仿真场景中.模型能够在给定系统运行过程中的服务器负载和环境温度下模拟系统运行过程,并对系统的制冷功率、进气口温度等主要输出变量进行预测.其中,对于制冷系统能耗预测的平均相对误差在5%以内.同时,利用制冷系统仿真模型优化了系统停止制冷时的温度设定值,通过仿真实验表明该优化最高可以节省18%的制冷能耗.In some complex systems,because of the influence of internal and external factors,periodic changes occur among runtime stages,with each stage exhibiting distinct dynamics.When we employ data-driven parameterized methods to model and predict such systems,a unified model restricts the learning of the dynamics and transitions of multiple stages.To address the aforementioned challenges,inspired by the ordinary differential equations network(ODENet),this paper proposes a novel predictive simulation framework,referred to as the deterministic finite automaton ordinary differential equation net(DFA-ODENet).This framework is a continuous-time deep learning framework designed to model periodic multistage systems using irregularly-sampled historical system trajectories.The model includes two principal predictions for forecasting system dynamics and stage transition.In terms of learning the dynamics of the system,the model comprises several ODENets,whose number is determined from the number of stages of the modeled system.Each ODENet individually learns the continuous-time nonlinear dynamics within its respective stage.For learning the stage transitions,a stage transition predictor is employed to learn the duration of each stage from observational data.These stage transition predictors are prelabeled based on the prior knowledge of the system.During prediction,the stage transition predictor serves as a switcher for selecting the appropriate ODENet to predict the system outputs.Moreover,the framework incorporates a specific encoder-decoder structure,where the encoder solves the initial state based on historical system inputs and outputs,while the decoder predicts future system outputs using the inputs of the prediction window based on the solved initial state.To evaluate the feasibility and effectiveness of the proposed approach,the encoder-decoder framework is employed in a cooling system of a real data center to simulate specific dynamic variables during operation.After providing multivariate operational data,including serv

关 键 词：复杂系统建模 周期多阶段系统 神经常微分网络 多输入多输出时间序列预测 制冷系统 能耗优化 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]