THERMAL SCIENCE

International Scientific Journal

Authors of this Paper

External Links

Jiamin Du

,

Shuhong Li

,

Xinmei Li

MODELING AND OPTIMIZATION OF A CHILLED-WATER COOLING SYSTEM WITH MULTIPLE CHILLERS

ABSTRACT
In order to reduce energy consumption of the centralized chilled-water cooling system in large buildings, a dynamic control strategy was proposed for cooling plants by modelling and optimization. Combined with the chilled water flow model, this paper analyzed the parallel operation characteristics of the chillers and takes the load distribution as one of the control parameters. Based on the measured data of a typical cooling system that has undergone preliminary energy-saving transformation, the residual neural network is applied to model the relationship among energy consumption, controllable parameters and environmental parameters, and the residual neural network outperforms multi-layer perceptron and support vector regression. To minimize the total energy consumption, the gray wolf optimizer was introduced to optimize the controllable variables of the cooling system. Compared with the energy consumption before optimization, the simulation energy consumption after optimization decreased 10.45% on average, while the energy saving rate is only 7.9% with equal chilled water supply temperature of parallel chillers.
KEYWORDS
chilled-water cooling system, modeling and optimization, cooling load distribution, energy conservation
PAPER SUBMITTED: 2020-06-06
PAPER REVISED: 2020-09-15
PAPER ACCEPTED: 2020-10-06
PUBLISHED ONLINE: 2020-11-07
DOI REFERENCE: https://doi.org/10.2298/TSCI200606328D
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2021, VOLUME 25, ISSUE Issue 5, PAGES [3873 - 3888]
REFERENCES
  1. Luis Pérez-Lombard, et al., A review on buildings energy consumption information, Energy and Buildings,2007,40(3).
  2. Li, F. F., Zhang, Z. P., Yu, J., Study of Public Building Energy Consumption Status and Energy Saving Potential, Applied Mechanics and Materials, 2014, 641-642:982-986.
  3. Yan, J. W., Huang, Q., Zhou, X., Energy-saving Optimization Operation of Central Airconditioning System Based on Double-DQN Algorithm, Journal of South China University of Technology (Natural Science Edition), 2019, 47(01):135-144.
  4. A. P. Wemhoff, M.V. Frank., Predictions of energy savings in HVAC systems by lumped models, Energy & Buildings, 2010, 42(10).
  5. Vahid Vakiloroaya, Jafar Madadnia, Bijan Samali, Modelling and performance prediction of an integrated central cooling plant for HVAC energy efficiency improvement, Building Simulation, 2013, 6(2).
  6. Ching-Liang Chen,Yung-Chung Chang,Tien-Shun Chan, Applying smart models for energy saving in optimal chiller loading, Energy & Buildings, 2014, 68.
  7. Hao Wang,Wenjian Cai,Youyi Wang. Modeling of a hybrid ejector air conditioning system using artificial neural networks
  8. Zeng Zhenwei, Yang, W. C., Li, C., Output distribution of a multiple chiller system under part load conditions, HV&AC, 2008 (03): 107-110
  9. Yung-Chung Chang, Optimal chiller loading by evolution strategy for saving energy, Energy and buildings, 2007,39(4).
  10. Yan Junwei, Chen, C., Zhou, X., Lian, S. Z., Zhou, Y., Simulation study on optimal load distribution strategy of multiple chillers, HV&AC, 2016, 46(04):98-104+110.
  11. Benesty, J., Chen, J., Huang, Y., On the Importance of the Pearson Correlation Coefficient in Noise Reduction, IEEE Transactions on Audio, Speech & Language Processing, 2008, 16(4):757-765.
  12. Friedman, J. H., Greedy Function Approximation: A Gradient Boosting Machine, The Annals of Statistics, 2001, 29(5):1189-1232.
  13. Soteris A. Kalogirou, Applications of artificial neural-networks for energy systems, Applied Energy, 2000, 67(1/2):17-35.
  14. Chevalier, Robert F., et al, Support vector regression with reduced training sets for air temperature prediction: a comparison with artificial neural networks, Neural Computing & Applications, 2011, 20(1):151-159.
  15. He, K., Zhang, X., Ren, S., et al., Deep Residual Learning for Image Recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Computer Society, 2016.
  16. Ioffe, Sergey, Szegedy, Christian, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, arXiv:1502.03167,2015.
  17. Xu, B., Wang, N., Chen, T., et al., Empirical Evaluation of Rectified Activations in Convolutional Network, Computer Science, 2015.
  18. Liu Jinping, Zhou Dejin, Energy efficiency analysis of variable chilled water temperature adjustment in air conditioning systems, HV&AC, 2004 (05): 90-91 + 96
  19. Lu Lu, Cai, W. J., Xie, L. H., Li, S. J., Soh, Y. C., HVAC system optimization—in-building section, Energy and Buildings, 2003, 37(1).
  20. Junqi Yu, Liu, Q. T., Zhao, A. J., Qian, X. G., Zhang, R., Optimal chiller loading in HVAC System Using a Novel Algorithm Based on the distributed framework, Journal of Building Engineering, 2020, 28.
  21. Wen-Shing Lee, Chen, Y. T., Kao, Y. C., Optimal chiller loading by differential evolution algorithm for reducing energy consumption, Energy and Buildings,2010,43(2).
  22. Seyedali Mirjalili, Mirjalili, S. M., Lewis, A., Grey Wolf Optimizer, Advances in Engineering Software, 2014, 69.
PDF VERSION [DOWNLOAD]
Modeling and optimization of a chilled-water cooling system with multiple chillers

© 2024 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence