Triggering Optimal Control of Air Conditioning Systems by Event-Driven Mechanism: Comparing Direct and Indirect Approaches

Real-time optimal control of air conditioning (AC) is important, and should respond to the condition changes for an energy efficient operation. The traditional optimal control triggering mechanism is based on the “time clock” (called time-driven), and has certain drawbacks (e.g., delayed or unnecessary actions). Thus, an event-driven optimal control (EDOC) was proposed. In previous studies, the part-load ratio (PLR) of chiller plants was used as events to trigger optimal control actions. However, PLR is an indirect indicator of operation efficiency, which could misrepresent the system coefficient of performance (SCOP). This study thus proposes to directly monitor the SCOP deviations from the desired SCOP values. Two events are defined based on transient and cumulative SCOP deviations, which are systematically investigated in terms of energy performance and robustness. The PLR-based and SCOP-based EDOC are compared, in which energy saving and optimal control triggering time are analyzed. Results suggest that SCOP-based EDOC has better energy performance compared with PLR-based EDOC, but the frequent event triggering might happen due to the parameter uncertainty. For actual applications, the SCOP-based EDOC can be recommended when the ideal SCOP model is available with the properly-handled uncertainty. Nevertheless, the PLR-based EDOC could still be a more practical option to replace the traditional TDOC considering its acceptable energy performance and better robustness.

[1]  Wei Tian,et al.  A review of sensitivity analysis methods in building energy analysis , 2013 .

[2]  Michael J. Brusco,et al.  Initializing K-means Batch Clustering: A Critical Evaluation of Several Techniques , 2007, J. Classif..

[3]  Xi Chen,et al.  Optimization based integrated control of building HVAC system , 2014 .

[4]  Cardona Alzate,et al.  Predicción y selección de variables con bosques aleatorios en presencia de variables correlacionadas , 2020 .

[5]  Jin Wen,et al.  A model-based fault detection and diagnostic methodology based on PCA method and wavelet transform , 2014 .

[6]  Eamonn J. Keogh,et al.  Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases , 2001, Knowledge and Information Systems.

[7]  Kellie J. Archer,et al.  Empirical characterization of random forest variable importance measures , 2008, Comput. Stat. Data Anal..

[8]  Yongjun Sun,et al.  Recent Developments in HVAC System Control and Building Demand Management , 2017 .

[9]  Gongsheng Huang,et al.  Event-driven Optimal Control of Complex HVAC Systems Based on COP·mins☆ , 2017 .

[10]  Sen Huang,et al.  Improved cooling tower control of legacy chiller plants by optimizing the condenser water set point , 2017 .

[11]  Stephen B. M. Beck,et al.  Illustrating the relationship between the coefficient of performance and the coefficient of system performance by means of an R404 supermarket refrigeration system , 2016 .

[12]  Yanan Li,et al.  An Improved Commutation Prediction Algorithm to Mitigate Commutation Failure in High Voltage Direct Current , 2017 .

[13]  Ercan Atam.  New Paths Toward Energy-Efficient Buildings: A Multiaspect Discussion of Advanced Model-Based Control , 2016, IEEE Industrial Electronics Magazine.

[14]  Fan Yang,et al.  Effect of input variables on cooling load prediction accuracy of an office building , 2018 .

[15]  Yongjun Sun,et al.  Uncertainty analysis for chiller sequencing control , 2014 .

[16]  Jee-Heon Kim,et al.  Modeling and Optimizing a Chiller System Using a Machine Learning Algorithm , 2019, Energies.

[17]  F. W. Yu,et al.  Economic benefits of optimal control for water-cooled chiller systems serving hotels in a subtropical climate , 2010 .

[18]  Yacine Rezgui,et al.  Computational intelligence techniques for HVAC systems: A review , 2016, Building Simulation.

[19]  Xing Fang,et al.  The evaluation of operation performance of HVAC system based on the ideal operation level of system , 2016 .

[20]  Sen Huang,et al.  Amelioration of the cooling load based chiller sequencing control , 2016 .

[21]  Gongsheng Huang,et al.  Event-driven optimization of complex HVAC systems , 2016 .

[22]  Xing Fang,et al.  A dual-benchmark based energy analysis method to evaluate control strategies for building HVAC systems , 2016 .

[23]  Zeyu Wang,et al.  A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models , 2017 .

[24]  Andrew Kusiak,et al.  Multi-objective optimization of the HVAC (heating, ventilation, and air conditioning) system performance , 2015 .

[25]  Andrew Kusiak,et al.  Modeling and optimization of HVAC energy consumption , 2010 .

[26]  Hyeun Jun Moon,et al.  Energy consumption model with energy use factors of tenants in commercial buildings using Gaussian process regression , 2018, Energy and Buildings.

[27]  Michael Nikolaou,et al.  RTO: An overview and assessment of current practice , 2011 .

[28]  Kamel Ghali,et al.  Optimal control strategy for a multi-zone air conditioning system using a genetic algorithm , 2009 .

[29]  Guoqiang Hu,et al.  PMV-based event-triggered mechanism for building energy management under uncertainties , 2017, Energy and Buildings.

[30]  Yong Cheng,et al.  Dynamic control of room air temperature for stratum ventilation based on heat removal efficiency: Method and experimental validations , 2018, Building and Environment.

[31]  Jose I. Bilbao,et al.  Recent advances in the analysis of residential electricity consumption and applications of smart meter data , 2017 .

[32]  Noradin Ghadimi,et al.  Robust optimization based optimal chiller loading under cooling demand uncertainty , 2019, Applied Thermal Engineering.

[33]  Zhenjun Ma,et al.  Supervisory and Optimal Control of Building HVAC Systems: A Review , 2008 .

[34]  F. W. Yu,et al.  Critique of operating variables importance on chiller energy performance using random forest , 2017 .

[35]  Jasper Mbachu,et al.  Energy Management through Cost Forecasting for Residential Buildings in New Zealand , 2019, Energies.

[36]  Jian Bi.  A REVIEW OF STATISTICAL METHODS FOR DETERMINATION OF RELATIVE IMPORTANCE OF CORRELATED PREDICTORS AND IDENTIFICATION OF DRIVERS OF CONSUMER LIKING , 2012 .

[37]  Chao Huan,et al.  Optimization of room air temperature in stratum-ventilated rooms for both thermal comfort and energy saving , 2017 .

[38]  Jinfeng Liu,et al.  Adaptive modeling for reliability in optimal control of complex HVAC systems , 2019, Building Simulation.

[39]  Qing-Shan Jia,et al.  Event-driven optimal control of central air-conditioning systems: Event-space establishment , 2018 .

[40]  Tianzhen Hong,et al.  Data mining of space heating system performance in affordable housing , 2015, Building and Environment.

[41]  Shyan-Shu Shieh,et al.  Energy conservation improvement and ON–OFF switch times reduction for an existing VFD-fan-based cooling tower , 2015 .

[42]  Xiaoshu Lü,et al.  A novel dynamic modeling approach for predicting building energy performance , 2014 .

[43]  Shengwei Wang,et al.  Quantitative evaluation of the impact of building load characteristics on energy performance of district cooling systems , 2017 .