Building Simulation

, Volume 9, Issue 4, pp 359–398 | Cite as

Computational intelligence techniques for HVAC systems: A review

  • Muhammad Waseem Ahmad
  • Monjur Mourshed
  • Baris Yuce
  • Yacine Rezgui
Open Access
Review Article Building Systems and Components


Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air-conditioning (HVAC) systems are the major source of energy consumption in buildings and ideal candidates for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems. The analysis of trends reveals that the minimisation of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences. Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE-2, HVACSim+ and ESP-r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on multi-agent systems (MAS), as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions.


heating ventilation and airconditioning (HVAC) optimization computational intelligence energy conservation energy efficiency buildings 


  1. Ahmed SS, Majid MS, Novia H, Rahman HA (2007). Fuzzy logic based energy saving technique for a central air conditioning system. Energy, 32: 1222–1234.CrossRefGoogle Scholar
  2. Alcalá R, Benítez JM, Casillas J, Cordón O, Pérez R (2003). Fuzzy control of HVAC systems optimized by genetic algorithms. Applied Intelligence, 18: 155–177.MATHCrossRefGoogle Scholar
  3. Alcalá R, Alcalá-Fdez J, Gacto M, Herrera F (2006). Fuzzy rule reduction and tuning of fuzzy logic controllers for a HVAC system. In: Kahraman C (ed), Fuzzy Applications in Industrial Engineering, Volume 201 of Studies in Fuzziness and Soft Computing. Berlin: Springer, pp. 89–117.CrossRefGoogle Scholar
  4. Ali IM (2012). Developing of a fuzzy logic controller for air conditioning system. Anbar Journal for Engineering Sciences, 5: 180–187.Google Scholar
  5. Allen W, Rubaai A (2013). Fuzzy-neuro health monitoring system for HVAC system variable-air-volume unit. In: Proceedings of IEEE Industry Applications Society Annual Meeting.Google Scholar
  6. Argiriou A, Bellas-Velidis I, Balaras C (2000). Development of a neural network heating controller for solar buildings. Neural Networks, 13: 811–820.CrossRefGoogle Scholar
  7. Argiriou AA, Bellas-Velidis I, Kummert M, André P (2004). A neural network controller for hydronic heating systems of solar buildings. Neural Networks, 17: 427–440.MATHCrossRefGoogle Scholar
  8. ASHRAE (2009). Handbook of Fundamentals. Atlanta: American Society of Heating Refrigeration and Air-Conditioning Engineers.Google Scholar
  9. Baños R, Manzano-Agugliaro F, Montoya FG, Gil C, Alcayde A, Gómez J (2011). Optimization methods applied to renewable and sustainable energy: A review. Renewable and Sustainable Energy Reviews, 15: 1753–1766.CrossRefGoogle Scholar
  10. Bagley J (1967). The behavior of adaptive systems which employ genetic and correlative algorithms. PhD Thesis, University of Michigan, USA.Google Scholar
  11. Ben-Nakhi AE, Mahmoud MA (2002). Energy conservation in buildings through efficient A/C control using neural networks. Applied Energy, 73: 5–23.CrossRefGoogle Scholar
  12. Bezdek J (1998). Computational intelligence defined - by everyone! In: Kaynak O, Zadeh L, Trken B, Rudas I (eds), Computational Intelligence: Soft Computing and Fuzzy-Neuro Integration with Applications, Volume 162 of NATO ASI Series. Berlin: Springer, pp. 10–37.CrossRefGoogle Scholar
  13. Bichiou Y, Krarti M (2011). Optimization of envelope and HVAC systems selection for residential buildings. Energy and Buildings, 43: 3373–3382.CrossRefGoogle Scholar
  14. Bin S, Guiqing Z, Lin Z, Ming W (2010). Multi-agent system design for room energy saving. In Proceedings of 5th IEEE Conference on Industrial Electronics and Applications (ICIEA), pp. 73–76.Google Scholar
  15. BPIE (2011). Europe’s buildings under the microscope. Brussels: Building Performance Institute Europe.Google Scholar
  16. Brownlee AE, Wright JA, Mourshed MM (2011). A multi-objective window optimisation problem. In: Proceedings of 13th Annual Conference Companion on Genetic and Evolutionary Computation, pp. 89–90.Google Scholar
  17. Calvino F, Gennusa ML, Morale M, Rizzo G, Scaccianoce G (2010). Comparing different control strategies for indoor thermal comfort aimed at the evaluation of the energy cost of quality of building. Applied Thermal Engineering, 30: 2386–2395.CrossRefGoogle Scholar
  18. Carpenter GA, Grossberg S, Reynolds JH (1991). ARTMAP: Supervised real-time learning and classification of nonstationary data by a self-organizing neural network. Neural Networks, 4: 565–588.CrossRefGoogle Scholar
  19. Chen Y, Hao X, Zhang G, Wang S (2006). Flow meter fault isolation in building central chilling systems using wavelet analysis. Energy Conversion and Management, 47: 1700–1710.CrossRefGoogle Scholar
  20. Cho S-H, Yang H-C, Zaheer-uddin M, Ahn B-C (2005). Transient pattern analysis for fault detection and diagnosis of HVAC systems. Energy Conversion and Management, 46: 3103–3116.CrossRefGoogle Scholar
  21. Chow T, Lin Z, Song C, Zhang G (2001). Applying neural network and genetic algorithm in chiller system optimization. In: Proceedings of 7th International IBPSA Building Simulation Conference, pp. 1059–1065.Google Scholar
  22. Chow T, Zhang G, Lin Z, Song C (2002). Global optimization of absorption chiller system by genetic algorithm and neural network. Energy and Buildings, 34: 103–109.CrossRefGoogle Scholar
  23. Chu CM, Jong T-L, Huang Y-W (2005). Thermal comfort control on multi-room fan coil unit system using LEE-based fuzzy logic. Energy Conversion and Management, 46: 1579–1593.CrossRefGoogle Scholar
  24. CIBSE (2006). Guide A: Environmental Design. London: Chartered Institution of Building Services Engineers.Google Scholar
  25. Colorni A, Dorigo M, Maniezzo V (1991). Distributed optimization by ant colonies. In: Proceedings of European Conference of Artificial Life, pp. 134–142.Google Scholar
  26. Costa A, Keane MM, Torrens JI, Corry E (2013). Building operation and energy performance: Monitoring, analysis and optimization toolkit. Applied Energy, 101: 310–316.CrossRefGoogle Scholar
  27. Counsell J, Zaher O, Brindley J, Murphy G (2013). Robust nonlinear HVAC systems control with evolutionary optimization. Engineering Computations, 30: 1147–1169.CrossRefGoogle Scholar
  28. Crawley DB, Lawrie LK, Winkelmann FC, Buhl W, Huang Y, Pedersen CO, Strand RK, Liesen RJ, Fisher DE, Witte MJ, Glazer J (2001). EnergyPlus: Creating a new-generation building energy simulation program. Energy and Buildings, 33: 319–331.CrossRefGoogle Scholar
  29. Curtiss PS, Kreider JF, Brandemuehl MJ (1994). Local and global control of commercial building HVAC systems using artificial neural networks. In: Proceedings of American Control Conference, pp. 3029–3044.Google Scholar
  30. Dehestani D, Su S, Nguyen H, Guo Y (2013). Robust fault tolerant application for HVAC system based on combination of online SVM and ANN black box model. In: Proceedings of European Control Conference (ECC), pp. 2976–2981.Google Scholar
  31. Dexter A, Benouarets M (1997). Model-based fault diagnosis using fuzzy matching. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 27: 673–682.CrossRefGoogle Scholar
  32. Doctor F, Hagras H, Callaghan V (2005). A fuzzy embedded agentbased approach for realizing ambient intelligence in intelligent inhabited environments. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 35: 55–65.CrossRefGoogle Scholar
  33. Dounis AI, Caraiscos C (2009). Advanced control systems engineering for energy and comfort management in a building environment— A review. Renewable and Sustainable Energy Reviews, 13: 1246–1261.CrossRefGoogle Scholar
  34. Du Z, Jin X, Wu L (2007a). Fault detection and diagnosis based on improved PCA with JAA method in VAV systems. Building and Environment, 42: 3221–3232.CrossRefGoogle Scholar
  35. Du Z, Jin X, Wu L (2007b). PCA-FDA-based fault diagnosis for sensors in VAV systems. HVAC&R Research, 13: 349–367.CrossRefGoogle Scholar
  36. Du Z, Jin X, Yang Y (2009). Fault diagnosis for temperature, flow rate and pressure sensors in VAV systems using wavelet neural network. Applied Energy, 86: 1624–1631.CrossRefGoogle Scholar
  37. Du Z, Fan B, Chi J, Jin X (2014a). Sensor fault detection and its efficiency analysis in air handling unit using the combined neural networks. Energy and Buildings, 72: 157–166.CrossRefGoogle Scholar
  38. Du Z, Fan B, Jin X, Chi J (2014b). Fault detection and diagnosis for buildings and HVAC systems using combined neural networks and subtractive clustering analysis. Building and Environment, 73: 1–11.CrossRefGoogle Scholar
  39. EC (2011). A roadmap for moving to a competitive low carbon economy in 2050. COM(2011): 112. Brussels: European Commission.Google Scholar
  40. EIA (2011). Annual Energy Review. Washington, DC: US Energy Information Administration.Google Scholar
  41. Erickson VL, Cerpa AE (2010). Occupancy based demand response HVAC control strategy. In: Proceedings of 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings, pp. 7–12.CrossRefGoogle Scholar
  42. Evins R (2013). A review of computational optimisation methods applied to sustainable building design. Renewable and Sustainable Energy Reviews, 22: 230–245.CrossRefGoogle Scholar
  43. Fan B, Du Z, Jin X, Yang X, Guo Y (2010). A hybrid FDD strategy for local system of AHU based on artificial neural network and wavelet analysis. Building and Environment, 45: 2698–2708.CrossRefGoogle Scholar
  44. Ferber J (1999). Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence. Boston: Addison-Wesley Longman Publishing.Google Scholar
  45. Ferreira P, Ruano A, Silva S, Conceição EZE (2012). Neural networks based predictive control for thermal comfort and energy savings in public buildings. Energy and Buildings, 55: 238–251.CrossRefGoogle Scholar
  46. Fong K, Hanby V, Chow T (2006). HVAC system optimization for energy management by evolutionary programming. Energy and Buildings, 38: 220–231.CrossRefGoogle Scholar
  47. Fong K, Hanby V, Chow T (2009). System optimization for HVAC energy management using the robust evolutionary algorithm. Applied Thermal Engineering, 29: 2327–2334.CrossRefGoogle Scholar
  48. Ghiaus C (2001). Fuzzy model and control of a fan-coil. Energy and Buildings, 33: 545–551.CrossRefGoogle Scholar
  49. Goldberg D (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Reading, MA, USA: Addison-Wesley.MATHGoogle Scholar
  50. Gorsuch R (1988). Exploratory factor analysis. In: Nesselroade JR, Cattell RB (eds), Handbook of Multivariate Experimental Psychology. New York: Springer, pp. 231–258.CrossRefGoogle Scholar
  51. Grözinger J, Boermans T, Wehringer AJF, Seehusen J (2014). Overview of Member States information on NZEBs: Background paper—Final Report. Cologne, Germany: ECOFYS GmbH.Google Scholar
  52. Hadjiski M, Sgurev V, Boishina V (2007). HVAC control via hybrid intelligent systems. Cybernetics and Information Technologies, 7(1): 71–94.Google Scholar
  53. Hagan M, Menhaj MB (1994). Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks, 5: 989–993.CrossRefGoogle Scholar
  54. Hagras H, Packharn I, Vanderstockt Y, McNulty N, Vadher A, Doctor F (2008). An intelligent agent based approach for energy management in commercial buildings. In: Proceddings of IEEE International Conference on Fuzzy Systems, pp. 156–162.Google Scholar
  55. Hamdy M, Hasan A, Siren K (2009). Combination of optimisation algorithms for a multi-objective building design problem. In: Proceedings of 11th International IBPSA Building Simulation Conference, pp. 173–179.Google Scholar
  56. Hanson BG (1995). General Systems Theory: Beginning with Wholes. Abingdon, UK: Taylor & Francis.Google Scholar
  57. Haykin S (1994). Neural Networks: A Comprehensive Foundation. New York: Macmillan.MATHGoogle Scholar
  58. Holland J (1992). Adaptation in Natural and Artificial Systems. Cambridge, MA, USA: MIT Press.Google Scholar
  59. Holmes A, Duman H, Pounds-Cornish A (2002). The iDorm: Gateway to heterogeneous networking environments. In: Proceedings of International ITEA Workshop Virtual Home Environment, Paderborn, Germany.Google Scholar
  60. Homod RZ, Sahari KSM, Almurib HA, Nagi FH (2012). Gradient auto-tuned Takagisugeno fuzzy forward control of a HVAC system using predicted mean vote index. Energy and Buildings, 49: 254–267.CrossRefGoogle Scholar
  61. House JM, Lee WY, Shin DR (1999). Classification techniques for fault detection and diagnosis of an air-handling unit. ASHRAE Transactions, 105(2): 1087–1100.Google Scholar
  62. Hu Y, Chen H, Xie J, Yang X, Zhou C (2012). Chiller sensor fault detection using a self-adaptive principal component analysis method. Energy and Buildings, 54: 252–258.CrossRefGoogle Scholar
  63. Hurtado L, Nguyen P, Kling W, Zeiler W (2013). Building energy management systems—Optimization of comfort and energy use. In: Proceedings of 48th International Universities Power Engineering Conference.Google Scholar
  64. IPCC (2013). Climate Change 2013—The Physical Science Basis: Working Group I, Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. New York: Cambridge University Press.Google Scholar
  65. ISO (2005). Ergonomics of the thermal environment analytical determination and interpretation of thermal comfort using calculation of the PMV and PPD indices and local thermal comfort criteria (ISO 2005: 7730). Geneva: International Organization for Standardization.Google Scholar
  66. Jackson JE (2005). A User’S Guide to Principal Components. New York: John Wiley & Sons.MATHGoogle Scholar
  67. Jahedi G, Ardehali M (2011). Genetic algorithm-based fuzzy-PID control methodologies for enhancement of energy efficiency of a dynamic energy system. Energy Conversion and Management, 52: 725–732.CrossRefGoogle Scholar
  68. Jain AK, Murty MN, Flynn PJ (1999). Data clustering: A review. ACM Computing Surveys, 31: 264–323.CrossRefGoogle Scholar
  69. Jolliffe I (2005). Principal Component Analysis. In: Everitt BS, Howell D (eds), Encyclopedia of Statistics in Behavioral Science. Hoboken, NJ, USA: John Wiley & Sons.Google Scholar
  70. Joumaa H, Ploix S, Abras S, Oliveira GD (2011). A MAS integrated into home automation system, for the resolution of power management problem in smart homes. Energy Procedia, 6: 786–794.CrossRefGoogle Scholar
  71. Kalogirou SA (2009). Artificial neural networks and genetic algorithms in energy applications in buildings. Advances in Building Energy Research, 3: 83–119.CrossRefGoogle Scholar
  72. Kanarachos A, Geramanis K (1998). Multivariable control of single zone hydronic heating systems with neural networks. Energy Conversion and Management, 39: 1317–1336.CrossRefGoogle Scholar
  73. Kastner W, Kofler M, Reinisch R (2010). Using AI to realize energy efficient yet comfortable smart homes. In: Proceedings of 8th IEEE International Workshop on Factory Communication Systems, pp. 169–172.Google Scholar
  74. Katipamula S, Brambley MR (2005). Methods for fault detection, diagnostics, and prognostics for building systems: A review, part II. HVAC&R Research, 11: 169–187.CrossRefGoogle Scholar
  75. Kennedy J, Eberhart R (1995). Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948.CrossRefGoogle Scholar
  76. Khooban MH, Soltanpour MR, Abadi DNM, Esfahani Z (2012). Optimal intelligent control for HVAC systems. Journal of Power Technologies, 92: 192–200.Google Scholar
  77. Klein L, Kwak J-y, Kavulya G, Jazizadeh F, Becerik-Gerber B, Varakantham P, Tambe M (2012). Coordinating occupant behavior for building energy and comfort management using multi-agent systems. Automation in Construction, 22: 525–536.CrossRefGoogle Scholar
  78. Klein S, Duffie J, Beckman W (1976). TRNSYS—A transient simulation and program. ASHRAE Transactions, 82(1): 623–633.Google Scholar
  79. Kohonen T (1998). The self-organizing map. Neurocomputing, 21: 1–6.MATHCrossRefGoogle Scholar
  80. Kolokotsa D (2007). Artificial intelligence in buildings: A review of the application of fuzzy logic. Advances in Building Energy Research, 1: 29–54.CrossRefGoogle Scholar
  81. Kolokotsa D, Niachou K, Geros V, Kalaitzakis K, Stavrakakis G, Santamouris M (2005a). Implementation of an integrated indoor environment and energy management system. Energy and Buildings, 37: 93–99.CrossRefGoogle Scholar
  82. Kolokotsa D, Pouliezos A, Stavrakakis G (2005b). Sensor fault detection in building energy management systems. In: Proceedings of 5th International Conference on Technology and Automation, Thessaloniki, Greece, pp. 282–287.Google Scholar
  83. Krenker A, Bešter J, Kos A (2011). Introduction to the artificial neural networks. In: Suzuki K (ed), Artificial Neural Networks: Methodological Advances and Biomedical Applications. InTech, pp. 1–18.Google Scholar
  84. Kulasekera AL, Gopura RARC, Hemapala KTMU, Perera N (2011). A review on multi-agent systems in microgrid applications. In: Proceedings of IEEE PES Innovative Smart Grid Technologies, pp. 173–177.Google Scholar
  85. Kusiak A, Xu G, Tang F (2011). Optimization of an HVAC system with a strength multi-objective particle-swarm algorithm. Energy, 36: 5935–5943.CrossRefGoogle Scholar
  86. Lavinal E, Weiss G (1999). Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence. Cambridge, MA, USA: MIT Press.Google Scholar
  87. Lee J (2010). Conflict resolution in multi-agent based intelligent environments. Building and Environment, 45: 574–585.CrossRefGoogle Scholar
  88. Lee K-P, Cheng T-A (2012). A simulation–optimization approach for energy efficiency of chilled water system. Energy and Buildings, 54: 290–296.CrossRefGoogle Scholar
  89. Lee W, House JM, Park C, Kelly GE (1996). Fault diagnosis of an air-handling unit using artificial neural networks. ASHRAE Transactions, 102(1): 540–549.Google Scholar
  90. Lee W-S, Chen Y-T, Wu T-H (2009). Optimization for ice-storage air-conditioning system using particle swarm algorithm. Applied Energy, 86: 1589–1595.CrossRefGoogle Scholar
  91. Lee WY, House JM, Shin DR (1997). Fault diagnosis and temperature sensor recovery for an air-handling unit. ASHRAE Transactions, 103(1): 621–633.Google Scholar
  92. Lee W-Y, House JM, Kyong N-H (2004). Subsystem level fault diagnosis of a building’s air-handling unit using general regression neural networks. Applied Energy, 77: 153–170.CrossRefGoogle Scholar
  93. Li S, Wen J (2014). Application of pattern matching method for detecting faults in air handling unit system. Automation in Construction, 43: 49–58.CrossRefGoogle Scholar
  94. Li X, Visier J, Vaezi-Nejad H (1996). Development of a fault diagnosis method for heating systems using neural networks. ASHRAE Transactions, 102(1): 607–614.Google Scholar
  95. Li X, Visier J, Vaezi-Nejad H (1997). A neural network prototype for fault detection and dianosis of heating system. ASHRAE Transactions, 103(1): 634–644.Google Scholar
  96. Liang J, Du R (2005). Thermal comfort control based on neural network for HVAC application. In: Proceedings IEEE Conference on Control Applications, pp. 819–824.Google Scholar
  97. Liu K, Lin C, Qiao B (2008). A multi-agent system for intelligent pervasive spaces. In: Proceedings of IEEE International Conference on Service Operations and Logistics, and Informatics, pp. 1005–1010.Google Scholar
  98. Lixing D, Jinhu L, Xuemei L, Lanlan L (2010). Support vector regression and ant colony optimization for HVAC cooling load prediction. In: Proceedings of International Symposium on Computer Communication Control and Automation (3CA), pp. 537–541.Google Scholar
  99. Lo CH, Chan PT, Wong YK, Rad AB, Cheung KL (2007). Fuzzy-genetic algorithm for automatic fault detection in HVAC systems. Applied Soft Computing, 7: 554–560.CrossRefGoogle Scholar
  100. Lu L, Cai W, Xie L, Li S, Soh YC (2005). HVAC system optimization—in-building section. Energy and Buildings, 37: 11–22.CrossRefGoogle Scholar
  101. Ma Z, Wang S (2011). Supervisory and optimal control of central chiller plants using simplified adaptive models and genetic algorithm. Applied Energy, 88: 198–211.CrossRefGoogle Scholar
  102. Maitrey S, Jha CK, Ranab P (2014). Comparative analysis of pattern matching methodologies. In: Proceedings of International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT), pp. 607–612.Google Scholar
  103. Manfaat D, Duffy AH, Lee B (1996). Review of pattern matching approaches. The Knowledge Engineering Review, 11: 161–189.CrossRefGoogle Scholar
  104. Marvuglia A, Messineo A, Nicolosi G (2014). Coupling a neural network temperature predictor and a fuzzy logic controller to perform thermal comfort regulation in an office building. Building and Environment, 72: 287–299.CrossRefGoogle Scholar
  105. Mathworks (2012). Matlab Program. Mathworks.Google Scholar
  106. McArthur S, Davidson E, Catterson V, Dimeas A, Hatziargyriou N, Ponci F, Funabashi T (2007a). Multi-agent systems for power engineering applications—Part I: Concepts, approaches, and technical challenges. IEEE Transactions on Power Systems, 22: 1743–1752.CrossRefGoogle Scholar
  107. McArthur S, Davidson E, Catterson V, Dimeas A, Hatziargyriou N, Ponci F, Funabashi T (2007b). Multi-agent systems for power engineering applications—Part II: Technologies, standards, and tools for building multi-agent systems. IEEE Transactions on Power Systems, 22: 1753–1759.CrossRefGoogle Scholar
  108. Meireles M, Almeida P, Simoes M (2003). A comprehensive review for industrial applicability of artificial neural networks. IEEE Transactions on Industrial Electronics, 50: 585–601.CrossRefGoogle Scholar
  109. Mitchell M (1996). An Introduction to Genetic Algorithms. Cambridge, MA, USA: MIT Press.MATHGoogle Scholar
  110. Mokhlessi O, Rad H, Mehrshad N (2010). Utilization of 4 types of artificial neural network on the diagnosis of valve-physiological heart disease from heart sounds. In: Proceedings of 17th Iranian Conference of Biomedical Engineering (ICBME).Google Scholar
  111. Mokhtar M, Stables M, Liu X, Howe J (2013). Intelligent multi-agent system for building heat distribution control with combined gas boilers and ground source heat pump. Energy and Buildings, 62: 615–626.CrossRefGoogle Scholar
  112. Mongkolwongrojn M, Sarawit V (2005). Implementation of fuzzy logic control for air conditioning systems. In: Proceedings of 8th International Conference on Control, Automation and Systems, pp. 313–321.Google Scholar
  113. Moon JW, Jung SK, Kim Y, Han S-H (2011). Comparative study of artificial intelligence-based building thermal control methods— Application of fuzzy, adaptive neuro-fuzzy inference system, and artificial neural network. Applied Thermal Engineering, 31: 2422–2429.CrossRefGoogle Scholar
  114. Moon JW, Yoon S-H, Kim S (2013). Development of an artificial neural network model based thermal control logic for double skin envelopes in winter. Building and Environment, 61: 149–159.CrossRefGoogle Scholar
  115. Morisot O, Marchio D (1999). Fault detection and diagnosis on HVAC variable air volume system using artificial neural network. In: Proceedings of International IBPSA Building Simulation Conference, Kyoto, Japan.Google Scholar
  116. Mossolly M, Ghali K, Ghaddar N (2009). Optimal control strategy for a multi-zone air conditioning system using a genetic algorithm. Energy, 34: 58–66.CrossRefGoogle Scholar
  117. Mourshed M (2006). Interoperability-based optimisation of architectural design. PhD Thesis, National University of Ireland, Ireland.Google Scholar
  118. Mourshed M, Kelliher D, Keane M (2003). Integrating building energy simulation in the design process. IBPSA News, 13(1): 21–26.Google Scholar
  119. Mourshed M, Shikder S, Price AD (2011). Phi-array: A novel method for fitness visualization and decision making in evolutionary design optimization. Advanced Engineering Informatics, 25: 676–687.CrossRefGoogle Scholar
  120. Najafi M, Auslander DM, Bartlett PL, Haves P, Sohn MD (2012). Application of machine learning in the fault diagnostics of air handling units. Applied Energy, 96: 347–358.CrossRefGoogle Scholar
  121. Nassif N (2012). Modeling and optimization of HVAC systems using artificial intelligence approaches. ASHRAE Transactions, 118(2): 133–140.MathSciNetGoogle Scholar
  122. Nassif N, Kajl S, Sabourin R (2005). Optimization of HVAC control system strategy using two-objective genetic algorithm. HVAC&R Research, 11: 459–486.CrossRefGoogle Scholar
  123. Navale RL, Nelson RM (2012). Use of genetic algorithms and evolutionary strategies to develop an adaptive fuzzy logic controller for a cooling coil—comparison of the AFLC with a standard PID controller. Energy and Buildings, 45: 169–180.CrossRefGoogle Scholar
  124. Ngo D, Dexter AL (1998). Automatic commissioning of air-conditioning plant. In: Proceedings of the UKACC International Conference on Control, pp. 1694–1699.CrossRefGoogle Scholar
  125. Ning M, Zaheeruddin M (2010). Neuro-optimal operation of a variable air volume HVAC&R system. Applied Thermal Engineering, 30: 385–399.CrossRefGoogle Scholar
  126. Ooka R, Komamura K (2009). Optimal design method for building energy systems using genetic algorithms. Building and Environment, 44: 1538–1544.CrossRefGoogle Scholar
  127. Pal AK, Mudi RK (2008). Self-tuning fuzzy pi controller and its application to HVAC systems. International Journal of Computational Cognition, 6: 25–30.Google Scholar
  128. Parameshwaran R, Karunakaran R, Kumar CVR, Iniyan S (2010). Energy conservative building air conditioning system controlled and optimized using fuzzy-genetic algorithm. Energy and Buildings, 42: 745–762.CrossRefGoogle Scholar
  129. Peitsman HC, Bakker V (1996). Application of black-box models to HVAC systems for fault detection. ASHRAE Transactions, 102(1): 628–640.Google Scholar
  130. Peitsman HC, Soethout L (1997). Arx models and real-time modelbased diagnosis. ASHRAE Transactions, 103(1): 657–671.Google Scholar
  131. Pérez-Lombard L, Ortiz J, Pout C (2008). A review on buildings energy consumption information. Energy and Buildings, 40: 394–398.CrossRefGoogle Scholar
  132. Pourzeynali S, Lavasani H, Modarayi A (2007). Active control of high rise building structures using fuzzy logic and genetic algorithms. Engineering Structures, 29: 346–357.CrossRefGoogle Scholar
  133. Rackes A, Waring MS (2014). Using multiobjective optimizations to discover dynamic building ventilation strategies that can improve indoor air quality and reduce energy use. Energy and Buildings, 75: 272–280.CrossRefGoogle Scholar
  134. Reyes-Sierra M, Coello C (2006). Multi-objective particle swarm optimizers: A survey of the state-of-the-art. International Journal of Computational Intelligence Research, 2: 287–308.MathSciNetGoogle Scholar
  135. Russell EL, Chiang LH, Braatz RD (2012). Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes. London: Springer.Google Scholar
  136. Rutishauser U, Joller J, Douglas R (2005). Control and learning of ambience by an intelligent building. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 35: 121–132.CrossRefGoogle Scholar
  137. Sahu M, Bhattacharjee B, Kaushik SC (2012). Thermal design of airconditioned building for tropical climate using admittance method and genetic algorithm. Energy and Buildings, 53: 1–6.CrossRefGoogle Scholar
  138. Seo J, Ooka R, Kim JT, Nam Y (2014). Optimization of the HVAC system design to minimize primary energy demand. Energy and Buildings, 76: 102–108.CrossRefGoogle Scholar
  139. Shaikh PH, Nor NBM, Nallagownden P, Elamvazuthi I, Ibrahim T (2014). A review on optimized control systems for building energy and comfort management of smart sustainable buildings. Renewable and Sustainable Energy Reviews, 34: 409–429.CrossRefGoogle Scholar
  140. Shepherd AB, Batty WJ (2003). Fuzzy control strategies to provide cost and energy efficient high quality indoor environments in buildings with high occupant densities. Building Services Engineering Research and Technology, 24: 35–45.CrossRefGoogle Scholar
  141. Siddique N, Adeli H (2013). Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing. New York: John Wiley & Sons.CrossRefGoogle Scholar
  142. So ATP, Chan W, Tse W (1997). Self-learning fuzzy air handling system controller. Building Services Engineering Research and Technology, 18: 99–108.CrossRefGoogle Scholar
  143. Song Q, Hu W, Zhao T (2003). Robust neural network controller for variable airflow volume system. In: Proceedings of the IEEE Conference on Control Theory and Applications, 150: 112–118.CrossRefGoogle Scholar
  144. Soyguder S, Alli H (2010). Fuzzy adaptive control for the actuators position control and modeling of an expert system. Expert Systems with Applications, 37: 2072–2080.CrossRefGoogle Scholar
  145. Stanescu M, Kajl S, Lamarche L (2012). Evolutionary algorithm with three different permutation options used for preliminary HVAC system design. In: Proceedings of the building simulation and optimization conference, pp. 386–393.Google Scholar
  146. Symans MD, Kelly SW (1999). Fuzzy logic control of bridge structures using intelligent semi-active seismic isolation systems. Earthquake Engineering and Structural Dynamics, 28: 37–60.CrossRefGoogle Scholar
  147. Tavares Neto RF, Godinho Filho M (2013). Literature review regarding ant colony optimization applied to scheduling problems: Guide lines for implementation and directions for future research. Engineering Applications of Artificial Intelligence, 26: 150–161.CrossRefGoogle Scholar
  148. The World Bank (2014). World Development Indicators 1960–2013. Washington, DC: The World Bank.Google Scholar
  149. Tianyi Z, Jili Z, Dexing S (2011). Experimental study on a duty ratio fuzzy control method for fan-coil units. Building and Environment, 46: 527–534.CrossRefGoogle Scholar
  150. Ursu I, Nastase I, Caluianu S, Iftene A, Toader A (2013). Intelligent control of HVAC systems. Part I: Modeling and synthesis. INCAS Bulletin, 5(1): 103–118.CrossRefGoogle Scholar
  151. Vandaele I, Wouters P (1994). The PASSYS services, Publication No. EUR 15113. Brussels: European Commission.Google Scholar
  152. Congradac V, Kulic F (2012). Recognition of the importance of using artificial neural networks and genetic algorithms to optimize chiller operation. Energy and Buildings, 47: 651–658.CrossRefGoogle Scholar
  153. Velmurugan V (2014). Performance based analysis between k–Means and Fuzzy C-Means clustering algorithms for connection oriented telecommunication data. Applied Soft Computing, 19: 134–146.CrossRefGoogle Scholar
  154. Venkatasubramanian V, Rengaswamy R, Kavuri SN, Yin K (2003). A review of process fault detection and diagnosis: Part III: Process history based methods. Computers & Chemical Engineering, 27: 327–346.CrossRefGoogle Scholar
  155. Wang S, Chen Y (2002). Fault-tolerant control for outdoor ventilation air flow rate in buildings based on neural network. Building and Environment, 37: 691–704.CrossRefGoogle Scholar
  156. Wang S, Cui J (2005). Sensor-fault detection, diagnosis and estimation for centrifugal chiller systems using principal-component analysis method. Applied Energy, 82: 197–213.CrossRefGoogle Scholar
  157. Wang S, Jin X (2000). Model-based optimal control of VAV airconditioning system using genetic algorithm. Building and Environment, 35: 471–487.CrossRefGoogle Scholar
  158. Wang S, Qin J (2005). Sensor fault detection and validation of VAV terminals in air conditioning systems. Energy Conversion and Management, 46: 2482–2500.CrossRefGoogle Scholar
  159. Wang S, Xiao F (2004a). AHU sensor fault diagnosis using principal component analysis method. Energy and Buildings, 36: 147–160.CrossRefGoogle Scholar
  160. Wang S, Xiao F (2004b). Detection and diagnosis of AHU sensor faults using principal component analysis method. Energy Conversion and Management, 45: 2667–2686.CrossRefGoogle Scholar
  161. Wang S, Zhou Q, Xiao F (2010). A system-level fault detection and diagnosis strategy for HVAC systems involving sensor faults. Energy and Buildings, 42: 477–490.CrossRefGoogle Scholar
  162. Wang Z, Yang R, Wang L (2011). Intelligent multi-agent control for integrated building and micro-grid systems. In: Proceedings of IEEE PES Conference on Innovative Smart Grid Technologies (ISGT).Google Scholar
  163. Wang Z, Wang L, Dounis AI, Yang R (2012). Multi-agent control system with information fusion based comfort model for smart buildings. Applied Energy, 99: 247–254.CrossRefGoogle Scholar
  164. Werbos PJ (1974). Beyond regression: New tools for prediction and analysis in the behavioural science. PhD Thesis, Harvard University, USA.Google Scholar
  165. Williamson DM, Almond RG, Mislevy RJ (2000). Model criticism of Bayesian networks with latent variables. In: Proceedings of 16th Conference on Uncertainty in Artificial Intelligence, pp. 634–643.Google Scholar
  166. Wolpert D, Macready W (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1: 67–82.CrossRefGoogle Scholar
  167. Wright JA, Loosemore HA, Farmani R (2002). Optimization of building thermal design and control by multi-criterion genetic algorithm. Energy and Buildings, 34: 959–972.CrossRefGoogle Scholar
  168. Wright JA, Brownlee A, Mourshed MM, Wang M (2014). Multiobjective optimization of cellular fenestration by an evolutionary algorithm. Journal of Building Performance Simulation, 7: 33–51.CrossRefGoogle Scholar
  169. Xiao F, Zhao Y, Wen J, Wang S (2014). Bayesian network based FDD strategy for variable air volume terminals. Automation in Construction, 41: 106–118.CrossRefGoogle Scholar
  170. Xu BG (2012). Intelligent fault inference for rotating flexible rotors using Bayesian belief network. Expert Systems with Applications, 39: 816–822.CrossRefGoogle Scholar
  171. Xu R, Wunsch D (2005). Survey of clustering algorithms. IEEE Transactions on Neural Networks, 16: 645–678.CrossRefGoogle Scholar
  172. Xu Y, Ji K, Lu Y, Yu Y, Liu W (2013). Optimal building energy management using intelligent optimization. In: Proceedings of IEEE International Conference on Automation Science and Engineering (CASE), pp. 95–99.Google Scholar
  173. Yang C, Li H, Rezgui Y, Petri I, Yuce B, Chen B, Jayan B (2014). High throughput computing based distributed genetic algorithm for building energy consumption optimization. Energy and Buildings, 76: 92–101.CrossRefGoogle Scholar
  174. Yang J, Rivard H, Zmeureanu R (2005). On-line building energy prediction using adaptive artificial neural networks. Energy and Buildings, 37: 1250–1259.CrossRefGoogle Scholar
  175. Yang R, Wang L (2011). Energy management of multi-zone buildings based on multi-agent control and particle swarm optimization. In: Proceedings of IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 159–164.Google Scholar
  176. Yang R, Wang L (2012a). Optimal control strategy for HVAC system in building energy management. In: Proceedings of IEEE PES Conference on Transmission and Distribution Conference and Exposition.Google Scholar
  177. Yang R, Wang L (2012b). Multi-objective optimization for decisionmaking of energy and comfort management in building automation and control. Sustainable Cities and Society, 2: 1–7.CrossRefGoogle Scholar
  178. Yang Y, Wang L (2013). Development of multi-agent system for building energy and comfort management based on occupant behaviors. Energy and Buildings, 56: 1–7.CrossRefGoogle Scholar
  179. Yuce B (2012). Novel computational technique for determining depth using the Bees Algorithm and blind image deconvolution. PhD Thesis, Cardiff University, UK.Google Scholar
  180. Yuce B, Packianather MS, Mastrocinque E, Pham DT, Lambiase A (2013). Honey bees inspired optimization method: The bees algorithm. Insects, 4: 646–662.CrossRefGoogle Scholar
  181. Yuce B, Li H, Rezgui Y, Petri I, Jayan B, Yang C (2014). Utilizing artificial neural network to predict energy consumption and thermal comfort level: An indoor swimming pool case study. Energy and Buildings, 80: 45–56.CrossRefGoogle Scholar
  182. Yuwono M, Guo Y, Wall J, Li J, West S, Platt G, Su SW (2015). Unsupervised feature selection using swarm intelligence and consensus clustering for automatic fault detection and diagnosis in heating ventilation and air conditioning systems. Applied Soft Computing, 34: 402–425.CrossRefGoogle Scholar
  183. Zadeh I (1965). Fuzzy sets. Information and Control, 8: 338–353.MathSciNetMATHCrossRefGoogle Scholar
  184. Zhang Y, Wright J, Hanby V (2006). Energy aspects of HVAC system configurations—Problem definition and test cases. HVAC&R Research, 12: 871–888.CrossRefGoogle Scholar
  185. Zhao Z, Suryanarayanan S, Simoes M (2013a). An energy management system for building structures using a multi-agent decisionmaking control methodology. IEEE Transactions on Industry Applications, 49: 322–330.CrossRefGoogle Scholar
  186. Zhao Y, Xiao F, Wang S (2013b). An intelligent chiller fault detection and diagnosis methodology using Bayesian belief network. Energy and Buildings, 57: 278–288.CrossRefGoogle Scholar
  187. Zhao Y, Wen J, Wang S (2015). Diagnostic Bayesian networks for diagnosing air handling units faults. Part II: Faults in coils and sensors. Applied Thermal Engineering, 90: 145–157.Google Scholar
  188. Zheng W, Xu H (2004). Design and application of self-regulating fuzzy controller based on qualitative and quantitative variables. In: Proceedings of 5th World Congress on Intelligent Control and Automation (WCICA), pp. 2472–2475.Google Scholar
  189. Zhou G, Ihm P, Krarti M, Liu S, Henze G (2003). Integration of an internal optimization module within EnergyPlus. In: Proceedings of 8th International IBPSA Building Simulation Conference, pp. 1475–1482.Google Scholar
  190. Zhou L, Haghighat F (2009). Optimization of ventilation system design and operation in office environment, Part I: Methodology. Building and Environment, 44: 651–656.CrossRefGoogle Scholar

Copyright information

© The Author(s) 2016

Authors and Affiliations

  • Muhammad Waseem Ahmad
    • 1
  • Monjur Mourshed
    • 1
  • Baris Yuce
    • 1
  • Yacine Rezgui
    • 1
  1. 1.BRE Centre for Sustainable Engineering, School of EngineeringCardiff UniversityCardiffUK

Personalised recommendations