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Yudong Xia, Qiang Ding, Zhao Li, Aipeng Jiang (2021)
Fault detection for centrifugal chillers using a Kernel Entropy Component Analysis (KECA) methodBuilding Simulation, 14
P. Lee, E. Chan, Queena Qian, P. Lam (2019)
Development of a user-friendly regression model to evaluate carbon emissions of office buildings design in the subtropicsFacilities
M. Ahmad, M. Mourshed, B. Yuce, Y. Rezgui (2016)
Computational intelligence techniques for HVAC systems: A reviewBuilding Simulation, 9
Anisha Maske, Bela Joglekar (2019)
An Algorithmic Approach for Mining Customer Behavior Prediction in Market Basket AnalysisInnovations in Computer Science and Engineering
Chaobo Zhang, Xue Xue, Yang Zhao, Xuejun Zhang, Tingting Li (2019)
An improved association rule mining-based method for revealing operational problems of building heating, ventilation and air conditioning (HVAC) systemsApplied Energy
(2009)
BEAM Plus
Hussain Asad, R. Yuen, Jinfeng Liu, Junqi Wang (2019)
Adaptive modeling for reliability in optimal control of complex HVAC systemsBuilding Simulation, 12
N. Koçyiğit (2015)
Fault and sensor error diagnostic strategies for a vapor compression refrigeration system by using fuzzy inference systems and artificial neural networkInternational Journal of Refrigeration-revue Internationale Du Froid, 50
(2018)
AHRI standard 550/590: ‘performance rating of water-chilling and heat pump water-heating packages using the vapor compression cycle’
T. Zhao, Jiaming Wang, Meng Xu, Kuishan Li (2020)
An online predictive control method with the temperature based multivariable linear regression model for a typical chiller plant systemBuilding Simulation, 13
F. Yu, K. Chan, R. Sit, Yang Junqi (2014)
Review of Standards for Energy Performance of Chiller Systems Serving Commercial BuildingsEnergy Procedia, 61
C. Borgelt, R. Kruse (2002)
Induction of Association Rules: Apriori Implementation
(2005)
R: a language and environment for statistical computing
Jiangyan Liu, Daliang Shi, Guannan Li, Yi Xie, Kuining Li, Bin Liu, Zhipeng Ru (2020)
Data-driven and association rule mining-based fault diagnosis and action mechanism analysis for building chillersEnergy and Buildings
N. Nassif (2013)
Modeling and optimization of HVAC systems using artificial neural network and genetic algorithmBuilding Simulation, 7
Guannan Li, Yunpeng Hu, Huanxin Chen, Haorong Li, Min Hu, Yabin Guo, Jiangyan Liu, Shaobo Sun, Miao Sun (2017)
Data partitioning and association mining for identifying VRF energy consumption patterns under various part loads and refrigerant charge conditionsApplied Energy, 185
Journal of Statistical Software, 14
Xuan Zhou, Bingwen Wang, Lie-Quan Liang, Junwei Yan, D. Pan (2019)
An operational parameter optimization method based on association rules mining for chiller plantJournal of Building Engineering
Handong Wang (2017)
A steady-state empirical model for evaluating energy efficient performance of centrifugal water chillersEnergy and Buildings, 154
(2012)
The building energy efficiency ordinanceElectrical and Mechanical Services Department (EMSD), HKSAR
Man-feng Li, Y. Ju (2017)
The analysis of the operating performance of a chiller system based on hierarchal cluster methodEnergy and Buildings, 138
Yang Zhao, Chaobo Zhang, Yiwen Zhang, Zihao Wang, Junyang Li (2020)
A review of data mining technologies in building energy systems: Load prediction, pattern identification, fault detection and diagnosis, 1
(2019)
ANSI/ASHRAE/IES standard 90.1-2019
Ronggeng Huang, Jiangyan Liu, Huanxin Chen, Zhengfei Li, Jiahui Liu, Guannan Li, Yabin Guo, Jiangyu Wang (2018)
An effective fault diagnosis method for centrifugal chillers using associative classificationApplied Thermal Engineering, 136
C. Fan, F. Xiao, Zhengdao Li, Jiayuan Wang (2018)
Unsupervised data analytics in mining big building operational data for energy efficiency enhancement: A reviewEnergy and Buildings, 159
P. Catrini, A. Piacentino, F. Cardona, G. Ciulla (2020)
Exergoeconomic analysis as support in decision-making for the design and operation of multiple chiller systems in air conditioning applicationsEnergy Conversion and Management, 220
X. Gui, Z. Gou, Fan Zhang (2020)
The relationship between energy use and space use of higher educational buildings in subtropical AustraliaEnergy and Buildings, 211
Shunian Qiu, Fan Feng, Zhengwei Li, Guang Yang, P. Xu, Zhenhai Li (2018)
Data mining based framework to identify rule based operation strategies for buildings with power metering systemBuilding Simulation, 12
Tien-Shun Chan, Yung-Chung Chang, Jianxue Huang (2017)
Application of artificial neural network and genetic algorithm to the optimization of load distribution for a multiple-type-chiller plantBuilding Simulation, 10
(2017)
Technical guidelines on retro-commissioningElectrical and Mechanical Services Department (EMSD), HKSAR
Chengliang Xu, Huanxin Chen (2020)
Abnormal energy consumption detection for GSHP system based on ensemble deep learning and statistical modeling methodInternational Journal of Refrigeration-revue Internationale Du Froid, 114
Zhe Chen, P. Xu, Fan Feng, Yifan Qiao, Wei Luo (2021)
Data mining algorithm and framework for identifying HVAC control strategies in large commercial buildingsBuilding Simulation, 14
F. Yu, K. Chan (2013)
Energy management of chiller systems by data envelopment analysisFacilities, 31
Michael Hahsler, B. Grün, K. Hornik (2009)
Introduction to arules – A computational environment for mining association rules and frequent item sets
K. Hornik (2005)
A CLUE for CLUster EnsemblesJournal of Statistical Software, 14
Wang Yalan, Zhiwei Wang, Suowei He, Zhanwei Wang (2019)
A practical chiller fault diagnosis method based on discrete Bayesian networkInternational Journal of Refrigeration
Keke Zheng, J. Watt, Chao Wang, Y. Cho (2016)
Development and implementation of a virtual outside air wet-bulb temperature sensor for improving water-cooled chiller plant energy efficiencySustainable Cities and Society, 23
R. Agrawal, T. Imielinski, A. Swami (1993)
Mining association rules between sets of items in large databasesACM SIGMOD Record, 22
Xu Zhu, Zhimin Du, Zhijie Chen, Xin-qiao Jin, Xiaoqing Huang (2019)
Hybrid model based refrigerant charge fault estimation for the data centre air conditioning systemInternational Journal of Refrigeration
(2020)
Historian
Yanfei Li, Zheng O’Neill (2018)
A critical review of fault modeling of HVAC systems in buildingsBuilding Simulation, 11
Bart Goethals, Mohammed Zaki (2003)
FIMI'03: Workshop on Frequent Itemset Mining Implementations
This study aims to apply association rule mining (ARM) to uncover specific associations between operating components of a chiller system and improve its coefficient of performance (COP), hence reducing the electricity use of buildings with central air conditioning.Design/methodology/approachFirst, 13 operating variables were identified, comprising measures of temperatures and flow rates of system components and their switching statuses. The variables were grouped into four bins before carrying out ARM. Strong rules were produced to associate the variables and switching statuses with different COP classes.FindingsThe strong rules explain existing constraints on practising chiller sequencing and prioritise variables for optimisation. Based on strong rules for the highest COP class, the optimal operating strategy involves rescheduling chillers and their associated components in pairs during a high load operation. Resetting the chilled water supply temperature is the next best strategy, followed by resetting the condenser water entering temperature, subject to operating constraints.Research limitations/implicationsThis study considers the even frequency method with four bins only. Replication work can be done with other discretisation methods and different numbers of classes to compare potential differences in the bin ranges of the optimised variables.Practical implicationsThe strong rules identified by ARM highlight associations between variables and high or low COPs. This supports the selection of critical variables and the operating status of system components to maximise the COP. Tailor-made optimisation strategies and the associated electricity savings can be further evaluated.Originality/valuePrevious studies applied ARM for chiller fault detection but without considering system performance under the interaction of different components. The novelty of this study is its demonstration of ARM’s intelligence at discovering associations in past operating data. This enables the identification of tailor-made energy management opportunities, which are essential for all engineering systems. ARM is free from the prediction errors of typical regression and black-box models.
Facilities – Emerald Publishing
Published: Jun 2, 2021
Keywords: Optimisation techniques; Efficiency; Energy management; Energy saving; Energy consumption; Air-conditioning; Association rule mining; Coefficient of performance; Market basket analysis; Water-cooled chiller
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