Self-Tuning Dynamic Models of HVAC System Components

A great majority of modern buildings are equipped with Energy Management and Control Systems (EMCS) which monitor and collect operating data from different components of heating ventilating and air conditioning (HVAC) systems. Models derived and tuned by using the collected data can be incorporated into the EMCS for online prediction of the system performance. To that end, HVAC component models with self-tuning parameters were developed and validated in this paper. The model parameters were tuned online by using a genetic algorithm which minimizes the error between measured and estimated performance data. The developed models included: a zone temperature model, return air enthalpy/humidity and CO2 concentration models, a cooling and heating coil model, and a fan model. The study also includes tools for estimating the thermal and ventilation loads. The models were validated against real data gathered from an existing HVAC system. The validation results show that the component models augmented with an online parameter tuner, significantly improved the accuracy of predicted outputs. The use of such models offers several advantages such as designing better real-time control, optimization of overall system performance, and online fault detection.

[1]  Shengwei Wang,et al.  Dynamic simulation of building VAV air-conditioning system and evaluation of EMCS on-line control strategies , 1999 .

[2]  Sanford Klein,et al.  Application of general regression neural network (GRNN) in HVAC process identification and control , 1996 .

[3]  G. Zheng,et al.  Optimization of thermal processes in a variable air volume HVAC system , 1996 .

[4]  Michael J. Title Algorithms Brandemuehl,et al.  HVAC 2 toolkit : a toolkit for secondary HVAC system energy calculations , 1993 .

[5]  Refrigerating ASHRAE handbook of fundamentals , 1967 .

[6]  Dominique Marchio,et al.  Simplified Model for the Operation of Chilled Water Cooling Coils Under Nonnominal Conditions , 2002 .

[7]  R. K. Ursem Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[8]  M. Zaheer-uddin Combined energy balance and recursive least squares method for the identification of system parameters , 1990 .

[9]  G. E. Kelly,et al.  Fault diagnosis of an air-handling unit using artificial neural networks , 1996 .

[10]  Robert Sabourin,et al.  Optimization of HVAC Control System Strategy Using Two-Objective Genetic Algorithm , 2005 .

[11]  John E. Seem,et al.  Using intelligent data analysis to detect abnormal energy consumption in buildings , 2007 .

[12]  Stanislaw Kajl,et al.  MODELING AND VALIDATION OF EXISTING VAV SYSTEM COMPONENTS , 2004 .

[13]  Shengwei Wang,et al.  Model-based optimal control of VAV air-conditioning system using genetic algorithm , 2000 .

[14]  H. N. Lam,et al.  Using genetic algorithms to optimize controller parameters for HVAC systems , 1997 .

[15]  Refrigerating ASHRAE handbook and product directory /published by the American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc , 1977 .