Multi-objective optimization of HVAC system using NSPSO and Kriging algorithms—A case study

In modern building design, engineers are constantly facing challenging to find an optimal design to maintain a high level of thermal comfort and indoor air quality for occupants while minimizing the system energy consumption. Over the past decades, several algorithms have been proposed and developed for optimizing the heating, ventilation and air conditioning (HVAC) system for indoor environment. Nevertheless, majority of these optimization algorithms are focused on single objective optimization procedures and require large training sample for surrogate modelling. For multi-objective HVAC design problems, previous studies introduced an arbitrary weighting factor to combine all design objectives into one single objective function. The near optimal solutions were however sensitive to the chosen value of the weighting factor. Aiming to develop a multi-objective optimization platform with minimal computational cost, this paper presents a nondominated sorting-based particle swarm optimization (NSPSO) algorithm together with the Kriging method to perform optimization for the HVAC system design of a typical office room. In addition, an adaptive sampling procedure is also proposed to enable the optimization platform to adjust the sampling point and resolution in constructing the training sample. Significant computational cost could be reduced without sacrificing the accuracy of the optimal solution. The proposed methods are applied and assessed in a typical HVAC system and the results indicate that comparing to traditional methods, the presented approach can handle multi-objective optimization in ventilation system with up to 46.6% saving of computational time.

[1]  O. Weck,et al.  A COMPARISON OF PARTICLE SWARM OPTIMIZATION AND THE GENETIC ALGORITHM , 2005 .

[2]  Jiyuan Tu,et al.  Computational Fluid Dynamics: A Practical Approach , 2007 .

[3]  John Haymaker,et al.  ThermalOpt: A methodology for automated BIM-based multidisciplinary thermal simulation for use in optimization environments , 2011 .

[4]  Liang Zhou,et al.  Optimization of ventilation system design and operation in office environment , 2009 .

[5]  Xiaodong Li,et al.  Swarm heuristic for identifying preferred solutions in surrogate-based multi-objective engineering design , 2011 .

[6]  Nicola Cardinale,et al.  Thermal performance of a mobile home with light envelope , 2010 .

[7]  Guan-Chun Luh,et al.  Optimal design of truss-structures using particle swarm optimization , 2011 .

[8]  Hongye Su,et al.  Optimization of ventilation system operation in office environment using POD model reduction and genetic algorithm , 2013 .

[9]  P. Depecker,et al.  Using artificial neural networks to predict interior velocity coefficients , 1997 .

[10]  Jan A. Snyman,et al.  A study of the feasibility of using mathematical optimisation to minimise the temperature in a smelter pot room , 2007 .

[11]  Paola Ricciardi,et al.  A field study on thermal comfort in naturally-ventilated buildings located in the equatorial climatic region of Cameroon , 2014 .

[12]  Said Farahat,et al.  Multi-objective optimization of natural convection in a cylindrical annulus mold under magnetic field using particle swarm algorithm , 2015 .

[13]  Andy J. Keane,et al.  Engineering Design via Surrogate Modelling - A Practical Guide , 2008 .

[14]  V. D. Loktionov,et al.  Using the Star CCM+ software system for modeling the thermal state and natural convection in the melt metal layer during severe accidents in VVER reactors , 2015 .

[15]  Bingtao Zhao,et al.  Modeling pressure drop coefficient for cyclone separators: A support vector machine approach , 2009 .

[16]  David E. Claridge,et al.  Influence of reduced VAV flow settings on indoor thermal comfort in an office space , 2016 .

[17]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.

[18]  Haralambos Sarimveis,et al.  Selection of window sizes for optimizing occupational comfort and hygiene based on computational fluid dynamics and neural networks , 2011 .

[19]  Cinzia Buratti,et al.  Adaptive analysis of thermal comfort in university classrooms: Correlation between experimental data and mathematical models , 2009 .

[20]  Cinzia Buratti,et al.  Application of a new 13-value thermal comfort scale to moderate environments , 2016 .

[21]  P. O. Fanger,et al.  Thermal comfort: analysis and applications in environmental engineering, , 1972 .

[22]  Shinsuke Kato,et al.  Thermal simulation: Response factor analysis using three-dimensional CFD in the simulation of air conditioning control , 2010 .

[23]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[24]  Yu Xue,et al.  Prediction of particulate matter at street level using artificial neural networks coupling with chaotic particle swarm optimization algorithm , 2014 .

[25]  Jelle Laverge,et al.  Optimization of design flow rates and component sizing for residential ventilation , 2013 .

[26]  Cinzia Buratti,et al.  Evaluation of thermal comfort in an historical Italian opera theatre by the calculation of the neutral comfort temperature , 2016 .

[27]  Y. Varol,et al.  Prediction of flow fields and temperature distributions due to natural convection in a triangular enclosure using Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) , 2007 .

[28]  Cinzia Buratti,et al.  HVAC systems testing and check: A simplified model to predict thermal comfort conditions in moderate environments , 2013 .

[29]  Y Yan,et al.  Numerical study of passenger thermal effects on the transport characteristics of exhaled droplets in an airliner cabin , 2014 .

[30]  Shawn E. Gano,et al.  Update strategies for kriging models used in variable fidelity optimization , 2006 .

[31]  Hongye Su,et al.  A fast-POD model for simulation and control of indoor thermal environment of buildings , 2013 .

[32]  Fariborz Haghighat,et al.  Optimization of ventilation systems in office environment, Part II: Results and discussions , 2009 .

[33]  Xiaodong Li,et al.  A Non-dominated Sorting Particle Swarm Optimizer for Multiobjective Optimization , 2003, GECCO.

[34]  Yu Xue,et al.  Inverse design methods for indoor ventilation systems using CFD-based multi-objective genetic algorithm , 2014 .

[35]  Cinzia Buratti,et al.  Thermal comfort in the Fraschini theatre (Pavia, Italy): Correlation between data from questionnaires, measurements, and mathematical model , 2015 .

[36]  Paramasivam Ravikumar,et al.  Analysis of thermal comfort in an office room by varying the dimensions of the windows on adjacent walls using CFD: A case study based on numerical simulation , 2009 .