Chiller Plant Operation Planning via Collaborative Neurodynamic Optimization

被引:10
|
作者
Chen, Zhongying [1 ]
Wang, Jun [1 ,2 ]
Han, Qing-Long [3 ]
机构
[1] City Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China
[2] City Univ Hong Kong, Sch Data Sci, Hong Kong, Peoples R China
[3] Swinburne Univ Technol, Sch Sci Comp & Engn Technol, Melbourne, Vic 3122, Australia
基金
澳大利亚研究理事会;
关键词
Optimization; Cooling; Power demand; Poles and towers; HVAC; Neurodynamics; Space heating; Chiller plant; collaborative neurodynamic optimization (CNO); heating; ventilation; and air conditioning (HVAC) systems; GLOBAL OPTIMIZATION; SYSTEM; MANAGEMENT; ALGORITHM; STRATEGY;
D O I
10.1109/TSMC.2023.3247633
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
A chiller plant is an essential part of a heating, ventilation, and air conditioning system. Chiller plant operation planning is to determine the throughput of active chillers, pumps, and fans in a chiller plant to meet cooling load demands with minimized power consumption. Existing planning methods are limited to chiller plant operation with homogeneous devices subject to constraints for the conservation of energy or with heterogeneous devices without considering the conservation of energy. In this article, a mixed-integer optimization problem is formulated for chiller plant operation planning with heterogeneous devices to minimize power consumption subject to various constraints, including the constraints for the conservation of energy. The formulated problem is reformulated as a global optimization problem and solved via collaborative neurodynamic optimization with multiple projection neural networks. Experimental results based on equipment manufacturers' specifications are elaborated to demonstrate the significantly higher performance of the proposed approach than four mainstream methods in terms of power consumption wattage.
引用
收藏
页码:4623 / 4635
页数:13
相关论文
共 50 条
  • [21] Safety-Critical Receding-Horizon Planning and Formation Control of Autonomous Surface Vehicles via Collaborative Neurodynamic Optimization
    Lyu, Guanghao
    Peng, Zhouhua
    Wang, Jun
    IEEE TRANSACTIONS ON CYBERNETICS, 2024, 54 (12) : 7236 - 7247
  • [22] A Collaborative Neurodynamic Approach to Distributed Global Optimization
    Xia, Zicong
    Liu, Yang
    Wang, Jun
    IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2023, 53 (05): : 3141 - 3151
  • [23] Alternative Mutation Operators in Collaborative Neurodynamic Optimization
    Li, Xinqi
    Wang, Jun
    Kwong, Sam
    2020 10TH INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE AND TECHNOLOGY (ICIST), 2020, : 126 - 133
  • [24] A collaborative neurodynamic approach to global and combinatorial optimization
    Che, Hangjun
    Wang, Jun
    NEURAL NETWORKS, 2019, 114 : 15 - 27
  • [25] Balanced clustering based on collaborative neurodynamic optimization
    Dai, Xiangguang
    Wang, Jun
    Zhang, Wei
    KNOWLEDGE-BASED SYSTEMS, 2022, 250
  • [26] Modeling and optimization of a chiller plant
    Wei, Xiupeng
    Xu, Guanglin
    Kusiak, Andrew
    ENERGY, 2014, 73 : 898 - 907
  • [27] Collaborative neurodynamic optimization for solving nonlinear equations
    Guan, Huimin
    Liu, Yang
    Kou, Kit Ian
    Cao, Jinde
    Rutkowski, Leszek
    NEURAL NETWORKS, 2023, 165 : 483 - 490
  • [28] Chiller Plant Optimization Responds
    Blaine, Steve
    ASHRAE JOURNAL, 2008, 50 (10) : 8 - 8
  • [29] Nonlinear system identification via sparse Bayesian regression based on collaborative neurodynamic optimization
    Okunev, Alexey
    Burnaev, Evgeny
    JOURNAL OF INVERSE AND ILL-POSED PROBLEMS, 2024, 32 (06): : 1161 - 1174
  • [30] Chiller Plant Optimization - An Integrated Optimization Approach for Chiller Sequencing and Control
    Torzhkov, Andrey
    Sharma, Puneet
    Li, Chengbo
    Toso, Rodrigo
    Chakraborty, Amit
    49TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC), 2010, : 2741 - 2746