Virtual models of indoor-air-quality sensors

A data-driven approach for modeling indoor-air-quality (IAQ) sensors used in heating, ventilation, and air conditioning (HVAC) systems is presented. The IAQ sensors considered in the paper measure three basic parameters, temperature, CO2, and relative humidity. Three models predicting values of IAQ parameters are built with various data mining algorithms. Four data mining algorithms have been tested on the HVAC data set collected at an office-type facility. The computational results produced by models built with different data mining algorithms are discussed. The neural network (NN) with multi-layer perceptron (MLP) algorithms produced the best results for all three IAQ sensors among all algorithms tested. The models built with data mining algorithms can serve as virtual IAQ sensors in buildings and be used for on-line monitoring and calibration of the IAQ sensors. The approach presented in this paper can be applied to HVAC systems in buildings beyond the type considered in this paper.

[1]  Vipin Kumar,et al.  Introduction to Data Mining , 2022, Data Mining and Machine Learning Applications.

[2]  Andrew Kusiak,et al.  Combustion efficiency optimization and virtual testing: a data-mining approach , 2006, IEEE Transactions on Industrial Informatics.

[3]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[4]  Arnaud G. Malan,et al.  HVAC control strategies to enhance comfort and minimise energy usage , 2001 .

[5]  Andrew Kusiak,et al.  Data mining: manufacturing and service applications , 2006 .

[6]  P. Backus,et al.  Factory cycle-time prediction with a data-mining approach , 2006, IEEE Transactions on Semiconductor Manufacturing.

[7]  Douglas C. Montgomery,et al.  Introduction to Statistical Quality Control , 1986 .

[8]  E. L. Krüger,et al.  Thermal performance evaluation of a low-cost housing prototype made with plywood panels in Southern Brazil , 2010 .

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

[10]  Orhan Büyükalaca,et al.  A case study for influence of building thermal insulation on cooling load and air-conditioning system in the hot and humid regions , 2010 .

[11]  Zhimin Du,et al.  Fault diagnosis for temperature, flow rate and pressure sensors in VAV systems using wavelet neural network , 2009 .

[12]  P. Seidel,et al.  Multilayer perceptron tumour diagnosis based on chromatography analysis of urinary nucleosides , 2007, Neural Networks.

[13]  Miloslav Suchánek,et al.  Multivariate control charts: Control charts for calibration curves , 1994 .

[14]  Andrew Kusiak,et al.  Models for monitoring wind farm power , 2009 .

[15]  Jan Hensen,et al.  Thermal comfort in residential buildings: Comfort values and scales for building energy simulation , 2009 .

[16]  S. Sathiya Keerthi,et al.  Improvements to the SMO algorithm for SVM regression , 2000, IEEE Trans. Neural Networks Learn. Syst..

[17]  Krishna R. Pattipati,et al.  Data-Driven Modeling, Fault Diagnosis and Optimal Sensor Selection for HVAC Chillers , 2007, IEEE Transactions on Automation Science and Engineering.

[18]  Michael J. A. Berry,et al.  Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management , 2004 .

[19]  D. J. Wheeler,et al.  Understanding Variation: The Key to Managing Chaos , 2000 .

[20]  Andrew Kusiak,et al.  Cooling output optimization of an air handling unit , 2010 .

[21]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[22]  L. T. Wong,et al.  A transient ventilation demand model for air-conditioned offices , 2008 .

[23]  Vincent Wertz,et al.  Fuzzy Logic, Identification and Predictive Control , 2004 .

[24]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[25]  Richard C. Diamond,et al.  Performance validation and energy analysis of HVAC systems using simulation , 2000 .

[26]  Deng Shiming,et al.  Development of a method for calculating steady-state equipment sensible heat ratio of direct expansion air conditioning units , 2008 .

[27]  Lan Kang,et al.  On-Line Monitoring When the Process Yields a Linear Profile , 2000 .

[28]  Douglas C. Montgomery,et al.  Using Control Charts to Monitor Process and Product Quality Profiles , 2004 .

[29]  Thomas Rbement,et al.  Fundamentals of quality control and improvement , 1993 .

[30]  Zhiwei Lian,et al.  Data mining based sensor fault diagnosis and validation for building air conditioning system , 2006 .

[31]  G. Casella,et al.  Statistical Inference , 2003, Encyclopedia of Social Network Analysis and Mining.

[32]  M. Zaheer-uddin,et al.  Multi‐fault detection and diagnosis of HVAC systems: an experimental study , 2005 .

[33]  L. T. Wong,et al.  An energy benchmarking model for ventilation systems of air-conditioned offices in subtropical climates , 2007 .

[34]  Steven T. Bushby,et al.  A rule-based fault detection method for air handling units , 2006 .