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Indexed by:
Article
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First Author:
Na Zhu
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Correspondence Author:
Shengwei Wang
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Co-author:
Kui Shan,Yongjun Sun
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Journal:
International Journal of Thermal Science
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Included Journals:
SCI
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Affiliation of Author(s):
The Hong Kong Polytechnic University
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Place of Publication:
China
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Discipline:
Engineering
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First-Level Discipline:
Civil Engineering
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Funded by:
Hong Kong Research Grants Council
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Document Type:
J
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Volume:
67
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Page Number:
540-550
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ISSN No.:
0378-7788
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Key Words:
Air-conditioning system; Optimal control strategy; Model uncertainty; Measurement uncertainty; Fuzzy c-means clustering; Machine learning
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DOI number:
10.1016/j.enbuild.2013.08.050
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Date of Publication:
2013-12-01
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Impact Factor:
7.201
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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
