Optimization of an HVAC system with a strength multi-objective particle-swarm algorithm

A data-driven approach for the optimization of a heating, ventilation, and air conditioning (HVAC) system in an office building is presented. A neural network (NN) algorithm is used to build a predictive model since it outperformed five other algorithms investigated in this paper. The NN-derived predictive model is then optimized with a strength multi-objective particle-swarm optimization (S-MOPSO) algorithm. The relationship between energy consumption and thermal comfort measured with temperature and humidity is discussed. The control settings derived from optimization of the model minimize energy consumption while maintaining thermal comfort at an acceptable level. The solutions derived by the S-MOPSO algorithm point to a large number of control alternatives for an HVAC system, representing a range of trade-offs between thermal comfort and energy consumption.

[1]  Andrew Kusiak,et al.  Multi-objective optimization of HVAC system with an evolutionary computation algorithm , 2011 .

[2]  Bourhan Tashtoush,et al.  Dynamic model of an HVAC system for control analysis , 2005 .

[3]  J. Friedman.  Stochastic gradient boosting , 2002 .

[4]  Yung-Chung Chang,et al.  Optimal chilled water temperature calculation of multiple chiller systems using Hopfield neural network for saving energy , 2009 .

[5]  Nabil Nassif,et al.  A cost‐effective operating strategy to reduce energy consumption in a HVAC system , 2008 .

[6]  Berhane H. Gebreslassie,et al.  Design of environmentally conscious absorption cooling systems via multi-objective optimization and life cycle assessment , 2009 .

[7]  Andrew Kusiak,et al.  Reheat optimization of the variable-air-volume box , 2010 .

[8]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[9]  Fariborz Haghighat,et al.  Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and Artificial Neural Network , 2010 .

[10]  Andrew Kusiak,et al.  Modeling and optimization of HVAC energy consumption , 2010 .

[11]  S. Ari,et al.  Fuzzy Logic and Neural Network Approximation to Indoor Comfort and Energy Optimization , 2006, NAFIPS 2006 - 2006 Annual Meeting of the North American Fuzzy Information Processing Society.

[12]  Abdullatif Ben-Nakhi,et al.  Energy conservation in buildings through efficient A/C control using neural networks , 2002 .

[13]  John Wang,et al.  Data Mining: Opportunities and Challenges , 2003 .

[14]  S. M. Lo,et al.  Application of evolutionary neural network method in predicting pollutant levels in downtown area of Hong Kong , 2003, Neurocomputing.

[15]  Kiyotaka Izumi,et al.  A particle-swarm-optimized fuzzy-neural network for voice-controlled robot systems , 2005, IEEE Transactions on Industrial Electronics.

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

[17]  Vojislav Novakovic,et al.  Optimization of energy consumption in buildings with hydronic heating systems considering thermal comfort by use of computer-based tools , 2007 .

[18]  Kamel Ghali,et al.  Optimal control strategy for a multi-zone air conditioning system using a genetic algorithm , 2009 .

[19]  T. Mexia,et al.  Author ' s personal copy , 2009 .

[20]  D. Wunsch,et al.  Time series prediction with recurrent neural networks using a hybrid PSO-EA algorithm , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

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

[22]  Luis Pérez-Lombard,et al.  A review on buildings energy consumption information , 2008 .

[23]  Andrew Kusiak,et al.  A data-driven approach for steam load prediction in buildings , 2010 .

[24]  Robert Sabourin,et al.  Optimization of HVAC Control System Strategy Using Two-Objective Genetic Algorithm , 2005 .

[25]  Yu Li,et al.  Particle swarm optimisation for evolving artificial neural network , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

[26]  Kwok-wing Chau,et al.  Application of a PSO-based neural network in analysis of outcomes of construction claims , 2007 .

[27]  R. Sabourin,et al.  Evolutionary algorithms for multi-objective optimization in HVAC system control strategy , 2004, IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04..