Heating system performance estimation using optimization tool and BEMS data

Causes and effects of a few real faults in a hydronic heating system are explained in this paper. Since building energy management system (BEMS) has to be utilized in fault detection and diagnosis (FDD), practical explanations of faults and their related effects are important to building caretakers. A simple heat balance model is used in this study. The model is calibrated using the optimization tool. Site data from the BEMS of a real building are calibrated against the model. Desired and real data are compared, so that the effects of the following faults are analyzed: faults in an outdoor air temperature sensor, fault in the time schedule, and a water flow imbalance problem. This paper presents an overview of the real causes of the faults and their effects both on the energy consumption and on the indoor air temperature. In addition, simple instructions for the building caretakers for fault detection in the hydronic heating systems are given.

[1]  Milorad Bojić,et al.  Linear programming optimization of heat distribution in a district-heating system by valve adjustments and substation retrofit , 2000 .

[2]  F Déqué,et al.  Grey boxes used to represent buildings with a minimum number of geometric and thermal parameters , 2000 .

[3]  Fu Xiao,et al.  AHU sensor fault diagnosis using principal component analysis method , 2004 .

[4]  Yasunori Akashi,et al.  A development of easy-to-use tool for fault detection and diagnosis in building air-conditioning systems , 2008 .

[5]  Nader Sadegh,et al.  Calibration of a lumped simulation model for double-skin façade systems , 2004 .

[6]  Gang Wu,et al.  Calibrated building energy simulation and its application in a high-rise commercial building in Shanghai , 2007 .

[7]  T. I. Salsbury.  A practical algorithm for diagnosing control loop problems , 1999 .

[8]  D K Smith,et al.  Numerical Optimization , 2001, J. Oper. Res. Soc..

[9]  J.-P.A. Snäkin.  An engineering model for heating energy and emission assessment The case of North Karelia, Finland , 2000 .

[10]  K. F. Fong,et al.  HVAC system optimization for energy management by evolutionary programming , 2006 .

[11]  Jouko Pakanen,et al.  Automation-assisted fault detection of an air-handling unit; implementing the method in a real building , 2003 .

[12]  Shengwei Wang,et al.  Parameter estimation of internal thermal mass of building dynamic models using genetic algorithm , 2006 .

[13]  Arthur L. Dexter,et al.  Fault-tolerant supervisory control of VAV air-conditioning systems , 2001 .

[14]  B Yu,et al.  Open window and defective radiator valve detection , 2003 .

[15]  M. Zaheer-uddin,et al.  Dynamic simulation of energy management control functions for HVAC systems in buildings , 2006 .

[16]  Stig-Inge Gustafsson,et al.  Mixed 0–1 sequential linear programming optimization of heat distribution in a district-heating system , 2000 .