Model Predictive Control for Chilled Water Plants

Anonymous — Thu, 01 Jan 2015 07:00:00 +0000

Model Predictive Control for Chilled Water Plants Anonymous (not verified) Thu, 01/01/2015 - 00:00

Improving the energy efficiency of chilled water plants is important since they account for about 35% the energy consumed by commercial building cooling equipment in the U.S. Model predictive control is a feasible way to enhance the operational efficiency of the chilled water plants. Our research is to develop a Python-based dynamic optimization platform that can perform continuous optimization for various control settings of chilled water plants. By using the Modelica modeling language, we can quickly model a chilled water plant with any system configurations and dynamically simulate its control behavior.

Collaborators

Lawrence Berkeley National Laboratory

Publications:

Journal articles:

S. Huang, A. Malara, W. Zuo, M. Sohn 2018. "A Bayesian Network Model for the Optimization of a Chiller Plant’s Condenser Water Set Point."Journal of Building Performance Simulation, 11(1), pp. 36-47.
S. Huang, W. Zuo, M. Sohn 2018. "A Bayesian Network Model for Predicting Cooling Load of Commercial Buildings." Building Simulation, 11(1), pp. 87-101.
S. Huang, W. Zuo, M. Sohn 2017. "Improved Cooling Tower Control of Legacy Chiller Plants by Optimizing the Condenser Water Set Point." Building and Environment, 111, pp. 33-46.
S. Huang, W. Zuo, M. Sohn 2016. "Amelioration of the Cooling Load Based Chiller Sequencing Control." Applied Energy, 168, pp. 204-215.

Conference proceedings:

S. Huang, W. Zuo, M. D. Sohn 2016. “A Bayesian Network Model for Predicting the Cooling Load of Educational Facilities.” Proceedings of the ASHRAE and IBPSA-USA SimBuild 2016: Building Performance Modeling Conference, pp. 1-8, August 8-12, Salt Lake City, UT.
S. Huang, M.V. Cavey, R. Sterling, W. Zuo, L. Helsen, A. Giretti, M. Bonvini, Z. O’Neill, M. Wetter, A. Costa, G. Boehme, R. Klein, B. Dong, M. M. Keane 2016. “IEA Annex 60 Activity 2.3: Model Use During Operation, Approach and Case Studies.” Proceedings of the 12th REHVA World Congress (CLIMA2016), May 22-25, Aalborg, Denmark.
S. Huang, W. Zuo, M. D. Sohn 2015. “A New Method for the Optimal Chiller Sequencing Control.” Proceedings of the 14th Conference of International Building Performance Simulation Association (Building Simulation 2015), pp. 316-323, December 7-9, Hyderabad, India.
A. C. L. Malara, S. Huang, W. Zuo, M. D. Sohn, N. Celik 2015. “Optimal Control of Chiller Plants Using Bayesian Network.” Proceedings of the 14th Conference of International Building Performance Simulation Association (Building Simulation 2015), pp. 449-455, December 7-9, Hyderabad, India.
T. Li, G. Shao, W. Zuo, S. Huang 2015. “Genetic Algorithm for Building Optimization - State-Of-The-Art Survey.” Proceedings of the 9th International Symposium on Heating, Ventilation and Air Conditioning (ISHVAC) and the 3rd International Conference on Building Energy and Environment (COBEE), pp. 205-210, July 12-15, Tianjin, China.
S. Huang, W. Zuo 2014. “Optimization of the Water-Cooled Chiller Plant System Operation.” Proceedings of the 2014 ASHRAE/IBPSA-USA Building Simulation Conference, pp. 300-307, September 10-12, Atlanta, GA.

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Model Predictive Control for Chilled Water Plants

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