Enhancing the Reliability of Chiller Control Using Fused Measurement of Building Cooling Load

This paper presents a general framework of utilizing a fused measurement of the building instantaneous cooling load to improve the reliability of the chiller sequencing control in building automation systems. The fused measurement is obtained by combining the complementary advantages of two different approaches to measuring the building cooling load. One approach is the direct measurement, which calculates the building cooling load directly, using the differential water temperature and water flow rate measurements. The other is the indirect measurement, which calculates building cooling load based on chiller models using the instantaneous chiller electrical power input, etc. The combination strategy is tested using the field data collected from the central plant of the air-conditioning system in a high-rise building in Hong Kong. The confidence degree associated with the fused measurement is systematically evaluated. Periodic update of the fusion algorithm parameters is also developed to improve the performance of the fusion strategy and the chiller sequencing control.

[1]  Shengwei Wang,et al.  Sensor-fault detection, diagnosis and estimation for centrifugal chiller systems using principal-component analysis method , 2005 .

[2]  Yung-Chung Chang,et al.  Optimal chiller sequencing by branch and bound method for saving energy , 2005 .

[3]  Tyler A. Soderstrom,et al.  A mixed integer optimization approach for simultaneous data reconciliation and identification of measurement bias , 2000 .

[4]  Michał K. Urbański,et al.  Fuzzy approach to the theory of measurement inexactness , 2003 .

[5]  Yung-Chung Chang,et al.  Optimal chiller loading by genetic algorithm for reducing energy consumption , 2005 .

[6]  Manus P. Henry,et al.  The fusion of redundant SEVA measurements , 2005, IEEE Transactions on Control Systems Technology.

[7]  Jeffrey M. Gordon,et al.  How varying condenser coolant flow rate affects chiller performance: thermodynamic modeling and experimental confirmation , 2000 .

[8]  Xinhua Xu,et al.  Enhanced chiller sensor fault detection, diagnosis and estimation using wavelet analysis and principal component analysis methods , 2008 .

[9]  Jose A. Romagnoli,et al.  A strategy for simultaneous dynamic data reconciliation and outlier detection , 1998 .

[10]  Joseph C. Lam,et al.  Energy analysis of commercial buildings in subtropical climates , 2000 .

[11]  R. J. Hackner,et al.  HVAC system dynamics and energy use in existing buildings. I , 1984 .

[12]  Karl H. Ruhm.  Sensor fusion and data fusion - Mapping and reconstruction , 2007 .

[13]  Victor M. Becerra,et al.  Combined bias and outlier identification in dynamic data reconciliation , 2002 .

[14]  Mohinder S. Grewal,et al.  Kalman Filtering: Theory and Practice Using MATLAB , 2001 .

[15]  John Burnett,et al.  Mechanistic model of centrifugal chillers for HVAC system dynamics simulation , 2000 .

[16]  Ronald R. Yager,et al.  A framework for multi-source data fusion , 2004, Inf. Sci..

[17]  Martin Brown,et al.  Neurofuzzy adaptive modelling and control , 1994 .

[18]  Fu Xiao,et al.  A data fusion scheme for building automation systems of building central chilling plants , 2009 .

[19]  Derya B. Özyurt,et al.  Theory and practice of simultaneous data reconciliation and gross error detection for chemical processes , 2004, Comput. Chem. Eng..

[20]  Andrew Starr,et al.  A Review of data fusion models and architectures: towards engineering guidelines , 2005, Neural Computing & Applications.