Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/102986
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Title: Development and validation of an effective and robust chiller sequence control strategy using data-driven models
Authors: Shan, K 
Wang, S 
Gao, DC 
Xiao, F 
Issue Date: May-2016
Source: Automation in construction, May 2016, v. 65, p. 78-85
Abstract: Chiller sequence control significantly affects the efficiency and operation stability of plants with multiple chillers. However, in real practice, the energy efficiency is commonly sacrificed to avoid uncertainties. Also, the strategies found in literature may be too complicated to be used practically. An effective and robust strategy for centrifugal chiller plants is therefore developed. The strategy innovatively utilizes chillers inlet guide vane openings as the load, and more particularly the energy efficiency indicator. A validation of the use of such an indicator is conducted using the in situ measurements from the chiller plant in a high-rise building. The strategy is compared with two other commonly used strategies through tests. In the ideal condition (no measurement errors), the proposed strategy saves 3% of the energy comparing to the original strategy. When systematic errors exist in the cooling load measurements, energy performance of the plant is not affected when controlled by the proposed strategy.
Keywords: Centrifugal chiller
Chiller sequence control
Data-driven models
Measurement error
Optimal chiller loading
Robust control
Publisher: Elsevier
Journal: Automation in construction 
ISSN: 0926-5805
EISSN: 1872-7891
DOI: 10.1016/j.autcon.2016.01.005
Rights: © 2016 Elsevier B.V. All rights reserved.
© 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/.
The following publication Shan, K., Wang, S., Gao, D. C., & Xiao, F. (2016). Development and validation of an effective and robust chiller sequence control strategy using data-driven models. Automation in Construction, 65, 78-85 is available at https://doi.org/10.1016/j.autcon.2016.01.005.
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