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Modeling and Control in Air-conditioning Systems

Part of the book series: Energy and Environment Research in China ((EERC))

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Abstract

With the development of economy and growth of people’s living standards, air-conditioning systems are increasingly popular for the improvement of thermal environment indoors, which results in ever-increasing energy consumption of buildings.

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References

  1. US Energy Information Administration (EIA).: Electric Power Monthly, Table 5.1, 11 March 2011

    Google Scholar 

  2. US Department of Energy (DOE).: 2010 Buildings Energy Data Book, Sections 2.1.5 and 3.1.4 (2011)

    Google Scholar 

  3. Building energy conservation research center of Tsinghua University, Annual Report on China Building Energy Efficiency (2013)

    Google Scholar 

  4. Tsinghua university architecture technology science. DeST User Manual (2004)

    Google Scholar 

  5. Park, C.: HVACSIM+ User’s Guide Update. National Institute of Standards and Technology (2008)

    Google Scholar 

  6. Dols, W.S., Walton, G.N.: CONTAMW 2.0 User Manual. National Institute of Standards and Technology (2002)

    Google Scholar 

  7. ASHRAE.: ASHRAE Handbook—Fundamentals. Atlanta (USA), GA (2009)

    Google Scholar 

  8. Sami, S.M., Duong, T., Mercadier, Y., Galanis, N.: Prediction of the transient response of heat pumps. ASHRAE Trans. 93, 471–478 (1987)

    Google Scholar 

  9. Sami, S.M., Comeau, M.A.: Development of a simulation model for predicting dynamic behavior of heat pumps with non-azeotropic refrigerant mixtures. Int. J. Energy Res. 16(4), 443–450 (1992)

    Google Scholar 

  10. Sami, S.M., Dahmani, A.: Numerical prediction of dynamic performance of vapor compression heat pump using new HFC alternatives to HCFC-22. J. Appl. Thermal Eng. 16(8/9), 691–705 (1996)

    Article  Google Scholar 

  11. Lei, Z., Zaheeruddin, M.: Dynamic simulation and analysis of a water chiller refrigeration system. Appl. Therm. Eng. 25(14–15), 2258–2271 (2005)

    Article  Google Scholar 

  12. Schalbart, P., Haberschill, P.: Simulation of the behaviour of a centrifugal chiller during quick start-up. Int. J. Refrig. 36(1), 222–236 (2013)

    Article  Google Scholar 

  13. Grald, E.W., MacArthur, J.W.A.: Moving-boundary formulation for modeling time-dependent two-phase flows. Int. J. Heat Fluid Flow 13(3), 266–272 (1992)

    Article  Google Scholar 

  14. Willatzen, M., Pettit, N.B.O.L., Ploug-Sørensen, L.: A general dynamic simulation model for evaporators and condensers in refrigeration. Part I: moving-boundary formulation of two-phase flows with heat exchange. Int. J. Refrig. 21(5), 398–403 (1998)

    Article  Google Scholar 

  15. Nyers, J., Stoyan, G.: A dynamical model adequate for controlling the evaporator of a heat pump. Int. J. Refrig. 17(2), 101–108 (1994)

    Article  Google Scholar 

  16. He, X., Liu, S., Asada, H.H.: Modeling of vapor compression cycles for multivariable feedback control of HVAC systems. ASME J. Dyn. Syst. Measur. Control 119(2), 183–191 (1997)

    Article  MATH  Google Scholar 

  17. McKinley, T.L., Alleyne, A.G.: An advanced nonlinear switched heat exchanger model for vapor compression cycles using the moving-boundary method. Int. J. Refrig. 31(7), 1253–1264 (2008)

    Article  Google Scholar 

  18. Cecchinato, L., Mancini, F.: An intrinsically mass conservative switched evaporator model adopting the moving-boundary method. Int. J. Refrig. 35(2), 349–364 (2012)

    Article  Google Scholar 

  19. Bell, I.H., Quoilin, S., Georges, E., Braun, J.E., Groll, E.A., Horton, W.T., Lemort, V.: A generalized moving-boundary algorithm to predict the heat transfer rate of counterflow heat exchangers for any phase configuration. Appl. Therm. Eng. 79(3), 192–201 (2015)

    Article  Google Scholar 

  20. Bendapudi, S., Braun, J.E., Groll, E.A.: A comparison of moving-boundary and finite-volume formulations for transients in centrifugal chillers. Int. J. Refrig. 31(8), 1437–1452 (2008)

    Article  Google Scholar 

  21. Jin, G.Y., Cai, W.J., Lu, L., Lee, E.L., Chiang, A.: A simplified modeling of mechanical cooling tower for control and optimization of HVAC systems. Energy Convers. Manag. 48(2), 355–365 (2007)

    Article  Google Scholar 

  22. Kloppers, C., Kroger, G.: Cooling tower performance evaluation: Merkel, Poppe, and e-NTU methods of analysis. J. Eng. Gas Turbines Power 127(1), 1–7 (2005)

    Article  Google Scholar 

  23. Tan, K., Deng, S.: A numerical analysis of heat and mass transfer inside a reversibly used water cooling tower. Build. Environ. 38(1), 91–97 (2003)

    Article  Google Scholar 

  24. Fisenko, S.P., Petruchik, A.I., Solodukhin, A.D.: Evaporative cooling of water in a natural draft cooling tower. Int. J. Heat Mass Transf. 45(23), 4683–4694 (2002)

    Article  MATH  Google Scholar 

  25. Fisenko, S.P., Brin, A.A., Petruchik, A.I.: Evaporative cooling of water in a mechanical draft cooling tower. Int. J. Heat Mass Transf. 47(1), 165–177 (2004)

    Article  MATH  Google Scholar 

  26. Mirth, D.R., Ramadhyani, S., Hittle, D.C.: Thermal performance of chilled-water cooling coils operating at low water velocities. ASHRAE Trans. 99(1), 43–53 (1993)

    Google Scholar 

  27. Yu, X., Wen, J., Smith, T.F.: A model for the dynamic response of a cooling coil. Energy Build. 37(12), 1278–1289 (2005)

    Article  Google Scholar 

  28. Threlkeld, J.L.: Thermal Environmental Engineering, 2nd edn. Prentice-Hall, Engledood Cliffs (1970)

    Google Scholar 

  29. Mullen, C.E., Bridges, B.D., Porter, K.J., Hahn, G.W., Bullard, C.W.: Development and validation of a room air-conditioning simulation model. ASHRAE Trans. 104(1), 389–397 (1998)

    Google Scholar 

  30. Deng, S.M.: A dynamic mathematical model of a direct expansion (DX) water cooled air-conditioning plant. Build. Environ. 35(7), 603–613 (2000)

    Article  Google Scholar 

  31. Wang, J., Hihara, E.: Prediction of air coil performance under partially wet and totally wet cooling conditions using equivalent dry-bulb temperature method. Int. J. Refrig. 26(3), 293–301 (2003)

    Article  Google Scholar 

  32. Bielski, S., Malinowski, L.: An analytical method for determining transient temperature field in a parallel-flow three-fluid heat exchanger. Int. Commun. Heat Mass Transfer 32(8), 1034–1044 (2005)

    Article  Google Scholar 

  33. Yin, J.: M. Jensen K. Analytic model for transient heat exchanger response. Int. J. Heat Mass Transf. 46(17), 3255–3264 (2003)

    Article  MATH  Google Scholar 

  34. Ren, C., Yang, H.: An analytical model for the heat and mass transfer processes in indirect evaporative cooling with parallel/counter flow configurations. Int. J. Heat Mass Transf. 49(3–4), 617–627 (2006)

    MATH  Google Scholar 

  35. Xia, L., Chan, M.Y., Deng, S.M., Xu, X.G.: Analytical solutions for evaluating the thermal performances of wet air cooling coils under both unit and non-unit Lewis factors. Energy Convers. Manag. 51(10), 2079–2086 (2010)

    Article  Google Scholar 

  36. Yao, Y., Lian, Z., Hou, Z.: Thermal analysis of cooling coils based on a dynamic model. Appl. Therm. Eng. 24(7), 1037–1050 (2004)

    Article  Google Scholar 

  37. Yao, Y., Huang, M., Mo, J., Dai, S.: State-space model for transient behavior of water-to-air surface heat exchanger. Int. J. Heat Mass Transfer 66(9), 173–192 (2013)

    Article  Google Scholar 

  38. Eppelheimer, D.M.: Variable flow—the quest for system energy efficiency. ASHRAE Trans. 102(2), 673–678 (1996)

    Google Scholar 

  39. Carrdo, V., Mazza, A.: Axial fan. IEA annex 17 Report, Politecnico Ditorino, Italy (1991)

    Google Scholar 

  40. Wang, S.W.: Dynamic simulation of a building central chilling system and evaluation of EMCS on-line control strategies. Build. Environ. 33(1), 1–20 (1998)

    Article  Google Scholar 

  41. Shao, L., Riffat, S.B.: Accuracy of CFD for predicting pressure losses in HVAC duct fittings. Appl. Energy 51(3), 233–248 (1995)

    Article  Google Scholar 

  42. Fisk, W.J., Delp, W., Diamond, R.: Duct systems in large commercial buildings: physical characterization, air leakage, and heat conduction gains. Energy Build. 32(1), 109–119 (2000)

    Article  Google Scholar 

  43. Rokni, M., Gatski, T.B.: Predicting turbulent convective heat transfer in fully developed duct flows. Int. J. Heat Fluid Flow 22(4), 381–392 (2001)

    Article  Google Scholar 

  44. Sugiyama, H., Akiyama, M., Nemoto, Y., Gessner, F.B.: Calculation of turbulent heat flux distributions in a square duct with one roughened wall by means of algebraic heat flux models. Int. J. Heat Fluid Flow 23(1), 13–21 (2002)

    Article  Google Scholar 

  45. Wu, S., Sun, J.Q.: A physics-based linear parametric model of room temperature in office buildings. Build. Environ. 50(1), 1–9 (2012)

    Article  Google Scholar 

  46. Abdul, A., Farrokh, J.S.: Review of modeling methods for HVAC systems. Appl. Therm. Eng. 67(1–2), 507–519 (2014)

    Google Scholar 

  47. Metha, D.P., Woods, J.E.: An experimental validation of a rational model of dynamic responses of building. ASHRAE Trans. 86(1), 546–558 (1980)

    Google Scholar 

  48. Borresen, B.A.: Thermal room models for control analysis. ASHRAE Trans. 87(2), 251–260 (1981)

    Google Scholar 

  49. Tashtoush, B., Molhim, M., Al-Rousan, M.: Dynamic model of an HVAC system for control analysis. Energy 30(10), 1729–1745 (2005)

    Article  Google Scholar 

  50. Riederer, P., Marchio, D., Visier, J.C., Husaunndee, A., Lahrech, R.: Room thermal modeling adapted to the test of HVAC control systems. Build. Environ. 37(8–9), 777–790 (2002)

    Article  Google Scholar 

  51. Chen, Q., Peng, X., Paassen, A.H.C.V.: Prediction of room thermal response by CFD technique with conjugate heat transfer and radiation models. ASHRAE Trans. 101(part 1), 50–60 (1995)

    Google Scholar 

  52. Wu, X., Olesen, B., Fang, W., Zhao, L.: A nodal model to predict vertical temperature distribution in a room with floor heating and displacement ventilation. Build. Environ. 59(1), 626–634 (2013)

    Article  Google Scholar 

  53. Yao, Y., Yang, K., Huang, M.: A state-space model for dynamic response of indoor air temperature and relative humidity. Build. Environ. 64(6), 26–37 (2013)

    Article  Google Scholar 

  54. Huang, G., Wang, S., Xu, X.: Robust model predictive control of VAV air-handling units concerning uncertainties and constraints. HVAC R. Res. 16(1), 15–33 (2010)

    Article  MathSciNet  Google Scholar 

  55. Huang, G.: Model predictive control of VAV zone thermal systems concerning bi-linearity and gain nonlinearity. Control Eng. Pract. 19(7), 700–710 (2011)

    Article  Google Scholar 

  56. Wang, S., Zhou, Q., Xiao, F.: A system-level fault detection and diagnosis strategy for HVAC systems involving sensor faults. Energy Build. 42(4), 477–490 (2010)

    Article  Google Scholar 

  57. Hosoz, M., Ertunc, H.M.: Artificial neural network analysis of an automobile air conditioning system. Energy Convers. Manag. 47(11–12), 1574–1587 (2006)

    Article  Google Scholar 

  58. Ertunc, H.M., Hosoz, M.: Comparative analysis of an evaporative condenser using artificial neural network and adaptive neuro-fuzzy inference system. Int. J. Refrig. 31(8), 1426–1436 (2008)

    Article  Google Scholar 

  59. Ding, L., Lv, J., Li, X., Li, L.: Support vector regression and ant colony optimization for HVAC cooling load prediction. In: International Symposium on Computer Communication Control and Automation (3CA), vol. 1 IEEE, Tainan, Taiwan, pp. 537–541 (2010)

    Google Scholar 

  60. Becker, M., Oestreich, D., Hasse, H., Litz, L.: Fuzzy control for temperature and humidity in refrigeration systems. IEEE Trans., FM-4-2: 1607–1611 (1994)

    Google Scholar 

  61. He, M., Cai, W.J., Li, S.Y.: Multiple fuzzy model-based temperature predictive control for HVAC systems. Inf. Sci. 169(1), 155–174 (2005)

    Article  MATH  Google Scholar 

  62. Soyguder, S., Alli, H.: An expert system for the humidity and temperature control in HVAC systems using ANFIS and optimization with fuzzy modeling approach. Energy Build. 41(3), 814–822 (2009)

    Article  Google Scholar 

  63. Homoda, R.Z., Saharia, K.S.M., Almuribb, H.A.F., Nagia, F.H.: Gradient auto-tuned Takagi-Sugeno fuzzy forward control of a HVAC system using predicted mean vote index. Energy Build. 49(6), 254–267 (2012)

    Article  Google Scholar 

  64. Khan, M.W., Choudhry, M.A., Zeeshan, M., Ali, A.: Adaptive fuzzy multivariable controller design based on genetic algorithm for an air handling unit. Energy 81(3), 477–488 (2015)

    Article  Google Scholar 

  65. Mustafaraj, G., Chen, J., Lowry, G.: Development of room temperature and relative humidity linear parametric models for an open office using BMS data. Energy Build. 42(3), 348–356 (2010)

    Article  Google Scholar 

  66. Penya, Y.K., Borges, C.E., Agote, D., Fernandez, I.: Short-term load forecasting in air-conditioned non-residential Buildings. In: 20th IEEE International Symposium on Industrial Electronics (ISIE), Gdansk, Poland, 1359–1364 27–30 June 2011

    Google Scholar 

  67. Wang, Y.W., Cai, W.J., Soh, Y.C., Li, S.J., Lu, L., Xie, L.: A simplified modeling of cooling coils for control and optimization of HVAC systems. Energy Convers. Manag. 45(18–19), 2915–2930 (2004)

    Article  Google Scholar 

  68. Ghiaus, C., Chicinas, A., Inard, C.: Grey-box identification of air-handling unit elements. Control Eng. Pract. 15(4), 421–433 (2007)

    Article  Google Scholar 

  69. Yao, Y., Liu, S.: Transfer function model for dynamic response of wet cooling coils. Energy Convers. Manag. 49(12), 3612–3621 (2008)

    Article  Google Scholar 

  70. Ferkl, L., Siroky, J.: Ceiling radiant cooling: comparison of ARMAX and sub-space identification modelling methods. Build. Environ. 45(2), 205–212 (2010)

    Article  Google Scholar 

  71. Jiang, Y.: State space method for analysis of the thermal behavior of rooms and calculation of air-conditioning load. ASHRAE Trans. 101(1), 122–132 (1995)

    Google Scholar 

  72. He, X.D., Liu, S., Asada, H.H., Itoh, H.: Multivariable control of vapor compression systems. HVAC&R Res. 4(3), 205–230 (1998)

    Article  Google Scholar 

  73. Yao, Y., Huang, M., Yang, K.: State-space model for dynamic behavior of vapor compression liquid chiller. Int. J. Refrig. 36(8), 2128–2147 (2013)

    Article  Google Scholar 

  74. Li, M., Wu, C.L., Zhao, S.Q., Yang, Y.: State-space model for airborne particles in multizone indoor environments. Atmos. Environ. 42(21), 5340–5349 (2008)

    Article  Google Scholar 

  75. Qi, Q., Deng, S.: Multivariable control-oriented modeling of a direct expansion (DX) air conditioning (A/C) system. Int. J. Refrig. 31(5), 841–849 (2008)

    Article  Google Scholar 

  76. Kumar, M., Kar, I.N., Ray, A.: State space based modeling and performance evaluation of an air-conditioning system. HVAC R Res. 14(5), 797–816 (2008)

    Article  Google Scholar 

  77. Moroşan, P.D., Bourdais, R., Dumur, D., Buisson, J.: Building temperature regulation using a distributed model predictive control. Energy Build. 42(9), 1445–1452 (2010)

    Article  Google Scholar 

  78. Prívara, S., Široký, J., Cigler, J.: Model predictive control of a building heating system: the first experience. Energy Build. 43(2–3), 564–572 (2011)

    Article  Google Scholar 

  79. Balakrishnan, V.K.: Graph Theory. Shaum’s Outline Series, New York (1997)

    MATH  Google Scholar 

  80. Garg, R.K., Agrawal, V.P., Gupta, V.K.: Selection of power plants by evaluation and comparison using graph theoretic methodology. Electr. Power Energy Syst. 28(3), 429–435 (2006)

    Article  Google Scholar 

  81. Prabhakaran, R.T.D., Agrawal, V.P.: Structural modeling and analysis of composite product system: a graph theoretic approach. J. Compos. Mater. 40(22), 1987–2007 (2006)

    Article  Google Scholar 

  82. Grekas, D.N., Frangopoulos, C.A.: Automatic synthesis of mathematical models using graph theory for optimisation of thermal energy systems. Energy Convers. Manag. 48(12), 2818–2826 (2007)

    Article  Google Scholar 

  83. Singh, V., Agrawal, V.P.: Structural modelling and integrative analysis of manufacturing systems using graph theoretic approach. J. Manuf. Technol. Manage. 19(7), 844–870 (2008)

    Article  Google Scholar 

  84. Wang, T., Ding, G., Duan, Z., Ren, T., Chen, J., Pu, H.: A distributed-parameter model for LNG spiral wound heat exchanger based on graph theory. Appl. Therm. Eng. 81(1), 102–113 (2015)

    Article  Google Scholar 

  85. Braun, J.E.: Reducing energy costs and peak electrical demands through optimal control of building thermal storage. ASHRAE Trans. 96(part 1), 587–601 (1990)

    Google Scholar 

  86. Kawashima, M., Dorgan, C.E., Mitchell, J.W.: Hourly thermal load prediction for the next 24 hours by ARIMA, EWMA, LR, and an artificial neural network. ASHRAE Trans. 101(part 1), 186–200 (1995)

    Google Scholar 

  87. Hwang, R.C., Huang, H.C., Chen, Y.J., Hsieh, J.G., Chen, H.C., Chuang, C.W.: Selection of influencing factors for power load forecasting by grey relation analysis. In: Second National Conference on Grey Theory and Applications, Taiwan, pp. 109–113 (1997)

    Google Scholar 

  88. Neto, A.H., Fiorelli, F.A.S.: Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption. Energy Build. 40(12), 2169–2176 (2008)

    Article  Google Scholar 

  89. Zhou, Q., Wang, S., Xu, X., Xiao, F.: A grey-box model of next-day building thermal load prediction for energy-efficient control. Int. J. Energy Res. 32(15), 1418–1431 (2008)

    Article  Google Scholar 

  90. Guo, J.J., Wu, J.Y., Wang, R.Z.: A new approach to energy consumption prediction of domestic heat pump water heater based on grey system theory. Energy Build. 43(6), 1273–1279 (2011)

    Article  Google Scholar 

  91. Chen, H.: The Validity of the Theory and Its Application of Combination Forecast Methods. Science Press, Beijing (China) (2008)

    Google Scholar 

  92. Granger, C.W.J., Ramanathan, R.: Improved methods of combining forecasts. J. Forecast. 3(3), 197–204 (1984)

    Article  Google Scholar 

  93. Braun, J.E., Klein, S.A., Mitchell, J.W., Beckman, W.A.: Methodologies for optimal control of chilled water systems without storage. ASHRAE Transact. 95(1), 652–662 (1989)

    Google Scholar 

  94. Olson, R.T., Liebman, S.: Optimization of a chilled water plant using sequential quadratic programming. Eng. Optim. 15(1), 171–191 (1990)

    Article  Google Scholar 

  95. Ahn, B.C., Mitchell, J.W.: Optimal control development for chilled water plants using a quadratic representation. Energy Build. 33(4), 371–378 (2001)

    Article  Google Scholar 

  96. Chang, Y.C.: Application of genetic algorithm to the optimal chilled water supply temperature calculation of air-conditioning systems for saving energy. Energy Res. 31(8), 796–810 (2007)

    Article  Google Scholar 

  97. Engdahl, F., Johansson, D.: Optimal supply air temperature with respect to energy use in a variable air volume system. Energy Build. 36(3), 205–218 (2004)

    Article  Google Scholar 

  98. Xu, X., Wang, S., Sun, Z., Xiao, F.: A model-based optimal ventilation control strategy of multi-zone VAV air-conditioning systems. Appl. Therm. Eng. 29(1), 91–104 (2009)

    Article  Google Scholar 

  99. Mossolly, M., Ghali, K., Ghaddar, N.: Optimal control strategy for a multi-zone air conditioning system using a genetic algorithm. Energy 34(1), 58–66 (2009)

    Article  Google Scholar 

  100. Sun, J., Reddy, A.: Optimal control of building HVAC and R systems using complete simulation-based sequential quadratic programming (CSB-SQP). Build. Environ. 40(5), 657–669 (2005)

    Article  Google Scholar 

  101. Hanby, V.I., Wright, J.A.: HVAC optimization studies: component modeling methodology. Build. Serv. Eng. Res. Technol. 10(1), 35–39 (1989)

    Article  Google Scholar 

  102. Zaheer-Uddin, M., Zheng, G.R.: Optimal control of time-scheduled heating, ventilating and air conditioning processes in buildings. Energy Convers. Manag. 41(1), 49–60 (2000)

    Article  Google Scholar 

  103. Chow, T.T., Zhang, G.Q., Lin, Z., Song, C.L.: Global optimization of absorption chiller system by generic algorithm and neural network. Energy Build. 34(1), 103–109 (2000)

    Article  Google Scholar 

  104. Huang, L.: Using genetic algorithms to optimize controller parameters for HVAC systems. Energy Build. 26(2), 277–282 (1997)

    Article  Google Scholar 

  105. Fong, K.F., Hanby, V.I., Chow, T.T.: System optimization for HVAC energy management using the robust evolutionary algorithm. Appl. Therm. Eng. 29(11–12), 2327–2334 (2009)

    Article  Google Scholar 

  106. Kido, T., Takag, K., Nakanishi, M.: Analysis and comparisons of genetic algorithm, simulated annealing, TABU search, and combination algorithm. Informatics 18(4), 399–410 (1994)

    Google Scholar 

  107. Arturo, M.C., Lorenz, T.B.: A stable elemental decomposition for dynamic process optimization. J. Comput. Appl. Math. 120(1), 41–57 (2000)

    MathSciNet  MATH  Google Scholar 

  108. Thi, H.A.L., Pham, D.T., Thoai, N.V.: Combination between global and local methods for solving an optimization problem over the efficient set. Eur. J. Oper. Res. 142(2), 258–270 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  109. Huang, Y.J., Reklaitis, G.V., Venkatasubramanian, V.: Model decomposition based method for solving general dynamic optimization problems. Comput. Chem. Eng. 26(6), 863–873 (2002)

    Article  Google Scholar 

  110. Wang, Y.J., Ying, L.: Global optimization for special reverse convex programming. Comput. Math Appl. 55(6), 1154–1163 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  111. Yu, D., Li, H., Ni, L., Yu, Y.: An improved virtual calibration of a supply air temperature sensor in rooftop air conditioning units. HVAC & R Res. J. 17(5), 798–812 (2011)

    Google Scholar 

  112. United Nations.: Role of measurement and calibration in the manufacture of products for the global market (2006)

    Google Scholar 

  113. Li, Z., Huang, G.: Preventive approach to determine sensor importance and maintenance requirements. Autom. Constr. 31(3), 307–312 (2013)

    Google Scholar 

  114. Huang, Y., Qian, X., Chen, S.: Multi-sensor calibration through iterative registration and fusion. Comput. Aided Des. 41(2), 240–255 (2009)

    Article  Google Scholar 

  115. Djuric, N., Huang, G., Novakovic, V.: Data fusion heat pump performance estimation. Energy Build. 43(6), 621–630 (2011)

    Article  Google Scholar 

  116. Mitchell, H.B.: Data Fusion: Concepts and Ideas, 2nd ed. Springer (2012)

    Google Scholar 

  117. Yu, D., Li, H., Yu, Y., Xiong, J.: Virtual calibration of a supply air temperature sensor in rooftop air conditioning units. HVAC&R Res. 17(1), 31–50 (2011)

    Article  Google Scholar 

  118. Whitehouse, K., Culler, D.: Calibration as parameter estimation in sensor networks. In: Proceedings of the 1st ACM International Workshop on Wireless Sensor Networks and Applications, pp. 59–67 (2002)

    Google Scholar 

  119. Bychkovskiy, V., Megerian, S., Estrin, D., Potkonjak, M.: A collaborative approach to in-place sensor calibration. Lect. Notes Comput. Sci. 263(2), 301–316 (2003)

    Article  MATH  Google Scholar 

  120. Dulev, V., Ermishin, S., Khoteev, N., Lopatin, A., Shabanov, P., Menshikov, A., Startsev, A.: Automated measuring system designed to calibrate measuring devices using virtual standards technology. In: 2004 IEEE Autotestcon, pp. 158–164

    Google Scholar 

  121. Fisher, J.W., Moses, R.L., Willsky, A.S.: Nonparametric Belief Propagation for Self-calibration in Sensor Networks. IPSN’ 04, Berkeley, California, 26–27 April 2004

    Google Scholar 

  122. Balzano, L., Nowak, R.: Blind calibration of sensor networks. In: Proceedings of IPSN’07. Cambridge, Massachusetts, 25–27 April 2007

    Google Scholar 

  123. Yu, D., Li, H., Yang, M.: A virtual supply airflow rate meter for rooftop air-conditioning units. Build. Environ. 46(6), 1292–1302 (2011)

    Article  Google Scholar 

  124. Tan, R., Xing, G., Liu, X., Yao, J., Yuan, Z.: Adaptive calibration for fusion-based cyber-physical system. ACM Trans. Embed. Comput. Syst. 11(4), 80, (1–25) (2012)

    Google Scholar 

  125. Mosh, B., Rashidi, F.: Self-tuning based fuzzy PID controllers: application to control of nonlinear HVAC systems. In: Proceedings of 5th International conference: Intelligent Data Engineering and Automated Learning. Exeter, UK (2004)

    Google Scholar 

  126. Yu, Y.: Model-based multivariate control of conditioning systems for office buildings (PhD. thesis). Carnegie Mellon University (2012)

    Google Scholar 

  127. Rawlings, J.B., Mayne, D.Q.: Model Predictive Control: Theory and Design. Nob Hill Publishing (2009)

    Google Scholar 

  128. Camacho, E.F., Bordons, C.: Model Predictive Control in the Process Industry. Springer, Berlin (1995)

    Google Scholar 

  129. Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Eng. Pract. 11(7), 733–764 (2003)

    Article  Google Scholar 

  130. Zhang, Y., Hanby, V.I.: Model-based control of renewable energy systems in buildings. HVAC&R Res. 12(3a), 739–760 (2006)

    Article  Google Scholar 

  131. Yuan, S., Perez, R.: Multiple-zone ventilation and temperature control of a single-duct VAV system using model predictive strategy. Energy Build. 38(10), 1248–1261 (2006)

    Article  Google Scholar 

  132. Wang, S., Ma, Z.: Supervisory and optimal control of building HVAC systems: a review. HVAC&R Res. 14(1), 3–32 (2008)

    Article  Google Scholar 

  133. Freire, R.Z., Oliveira, G.H.C., Mendes, N.: Predictive controllers for thermal comfort optimization and energy savings. Energy Build. 40(7), 1352–1365 (2008)

    Article  Google Scholar 

  134. Hazyuk, I., Ghiaus, C., Penhouet, D.: Optimal temperature control of intermittently heated buildings using model predictive control: part II—control algorithm. Build. Environ. 51(5), 388–394 (2012)

    Article  Google Scholar 

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Authors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, China

    Ye Yao

  2. University of Nebraska–Lincoln, Lincoln, USA

    Yuebin Yu

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  1. Ye Yao
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  2. Yuebin Yu
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© 2017 Shanghai Jiao Tong University Press and Springer-Verlag GmbH Germany

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Yao, Y., Yu, Y. (2017). Introduction. In: Modeling and Control in Air-conditioning Systems. Energy and Environment Research in China. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-53313-0_1

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  • DOI: https://doi.org/10.1007/978-3-662-53313-0_1

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