An optimization-oriented modeling approach using input convex neural networks and its application on optimal chiller loading

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外文摘要：Optimization for the multi-chiller system is an indispensable approach for the operation of highly efficient chiller plants.The optima obtained by model-based optimization algorithms are dependent on precise and solvable objective functions.The classical neural networks cannot provide convex input-output mappings despite capturing impressive nonlinear fitting capabilities,resulting in a reduction in the robustness of model-based optimization.In this paper,we leverage the input convex neural networks(ICNN)to identify the chiller model to construct a convex mapping between control variables and the objective function,which enables the NN-based OCL as a convex optimization problem and apply it to multi-chiller optimization for optimal chiller loading(OCL).Approximation performances are evaluated through a four-model comparison based on an experimental data set,and the statistical results show that,on the premise of retaining prior convexities,the proposed model depicts excellent approximation power for the data set,especially the unseen data.Finally,the ICNN model is applied to a typical OCL problem for a multi-chiller system and combined with three types of optimization strategies.Compared with conventional and meta-heuristic methods,the numerical results suggest that the gradient-based BFGS algorithm provides better energy-saving ratios facing consecutive cooling load inputs and an impressive convergence speed.

外文关键词：

chiller plantinput convex neural networkoptimal load distributionconvex optimization

作者：

Shanshuo Xing、Jili Zhang、Song Mu

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作者单位：

Dalian University of Technology,Dalian 116024,China

Guangdong Airport Baiyun Information Technology Co.,Ltd.,Guangzhou 510000,China

基金：

Dalian Key Field Innovation Team ProjectAirport Terminal Wisdom Environment Security and Energy Saving Laboratory of Guangdong Airport Baiyun Information Technology Co.

项目编号：

2020RT04

出版年：

2024

DOI：

10.1007/s12273-023-1093-2

建筑模拟(英文版)

ISSN：1996-3599

年,卷(期)：2024.17(4)