Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/16102
Title: An optimal control strategy with enhanced robustness for air-conditioning systems considering model and measurement uncertainties
Authors: Zhu, N
Shan, K
Wang, S 
Sun, Y
Keywords: Air-conditioning system
Fuzzy c-means clustering
Machine learning
Measurement uncertainty
Model uncertainty
Optimal control strategy
Issue Date: 2013
Publisher: Elsevier
Source: Energy and buildings, 2013, v. 67, p. 540-550 How to cite?
Journal: Energy and buildings 
Abstract: Model-based optimal controls in HVAC systems involve uncertainties due to model uncertainties and measurement uncertainties. These uncertainties affect the accuracy and reliability of the outputs of optimal control strategies, and therefore affect the energy and environmental performance of buildings. This study proposes a method to enhance the robustness of optimal control strategies. A fuzzy approach is adopted to predict the errors in models outputs. Such predicted errors are then used to correct the model outputs. The method is validated in an optimal control strategy for HVAC cooling water systems. The operation data of a real building system is used to validate the error prediction method. A simulation platform is built to validate the enhanced strategy. Measurement uncertainties are deliberately added to the simulated system for validation tests. Test results indicate that the method is effective in predicting the errors in model outputs. Significant energy savings are achieved compared with the conventional optimal control method.
URI: http://hdl.handle.net/10397/16102
ISSN: 0378-7788
EISSN: 1872-6178
DOI: 10.1016/j.enbuild.2013.08.050
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