Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2017 (2017), Article ID 3075432, 13 pages
https://doi.org/10.1155/2017/3075432
Research Article

Optimization of Indoor Thermal Comfort Parameters with the Adaptive Network-Based Fuzzy Inference System and Particle Swarm Optimization Algorithm

1School of Energy and Environmental Engineering, University of Science & Technology Beijing, Beijing 100083, China
2Beijing Key Laboratory of Energy Saving and Emission Reduction for Metallurgical Industry, University of Science and Technology Beijing, Beijing 100083, China
3School of Mechanical Engineering, University of Science & Technology Beijing, Beijing 100083, China

Correspondence should be addressed to Shao-Wu Yin; nc.ude.btsu@wsniy

Received 1 December 2016; Revised 21 February 2017; Accepted 26 February 2017; Published 22 March 2017

Academic Editor: Alessandro Lo Schiavo

Copyright © 2017 Jing Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. C. H. Zhu, Study on the Evaluation of the Indoor Environment and the Grey Theory, Hunan University, 2012.
  2. L. Mora, A. J. Gadgil, and E. Wurtz, “Comparing zonal and CFD model predictions of isothermal indoor airflows to experimental data,” Indoor Air, vol. 13, no. 2, pp. 77–85, 2003. View at Publisher · View at Google Scholar · View at Scopus
  3. A. Karimipour, M. H. Esfe, M. R. Safaei, D. T. Semiromi, S. Jafari, and S. N. Kazi, “Mixed convection of copper-water nanofluid in a shallow inclined lid driven cavity using the lattice Boltzmann method,” Physica A: Statistical Mechanics and Its Applications, vol. 402, pp. 150–168, 2014. View at Publisher · View at Google Scholar · View at Scopus
  4. A. Karimipour, A. Hossein Nezhad, A. D'Orazio, M. Hemmat Esfe, M. R. Safaei, and E. Shirani, “Simulation of copper-water nanofluid in a microchannel in slip flow regime using the lattice Boltzmann method,” European Journal of Mechanics, B/Fluids, vol. 49, pp. 89–99, 2015. View at Publisher · View at Google Scholar · View at Scopus
  5. M. R. Safaei, O. Mahian, F. Garoosi et al., “Investigation of micro- and nanosized particle erosion in a 90° pipe bend using a two-phase discrete phase model,” Scientific World Journal, vol. 2014, Article ID 740578, 12 pages, 2014. View at Publisher · View at Google Scholar · View at Scopus
  6. M. H. Esfe, A. A. A. Arani, A. Karimipour, and S. S. M. Esforjani, “Numerical simulation of natural convection around an obstacle placed in an enclosure filled with different types of nanofluids,” Heat Transfer Research, vol. 45, no. 3, pp. 279–292, 2014. View at Publisher · View at Google Scholar · View at Scopus
  7. A. Karimipour, M. Afrand, M. Akbari, and M. R. Safaei, “Simulation of fluid flow and heat transfer in the inclined enclosure,” International Journal of Mechanical and Aerospace Engineering, vol. 6, pp. 86–91, 2012. View at Google Scholar
  8. M. Skovgaard and P. V. Nielsen, Modelling Complex Inlet Geometries in CFD, Aalborg Universitetsforlag, 1991.
  9. P. Riederer, D. Marchio, and J. C. Visier, “Influence of sensor position in building thermal control: criteria for zone models,” Energy and Buildings, vol. 34, no. 8, pp. 785–798, 2002. View at Publisher · View at Google Scholar · View at Scopus
  10. X. Peng and A. H. C. Van Paassen, “A state space model for predicting and controlling the temperature responses of indoor air zones,” Energy and Buildings, vol. 28, no. 2, pp. 197–203, 1998. View at Publisher · View at Google Scholar · View at Scopus
  11. A. C. Megri and F. Haghighat, “Zonal modeling for simulating indoor environment of buildings: review, recent developments, and applications,” HVAC and R Research, vol. 13, no. 6, pp. 887–905, 2007. View at Publisher · View at Google Scholar · View at Scopus
  12. L.-S. Cao and J. Zhu, “Prediction of submarine hydrodynamics using CFD-based calculations and RBF neural network,” Journal of Ship Mechanics, vol. 18, no. 3, pp. 221–230, 2014. View at Publisher · View at Google Scholar · View at Scopus
  13. G. Krauss, J. I. Kindangen, and P. Depecker, “Using artificial neural networks to predict interior velocity coefficients,” Building and Environment, vol. 32, no. 4, pp. 295–303, 1997. View at Publisher · View at Google Scholar · View at Scopus
  14. W. Xu, X. G. Chen, and H. X. Peng, “Optimization of the control variables of indoor thermal comfort based on genetic algorithm and neural network,” Transaction of Beijing Institute of Technology, vol. 30, no. 2, pp. 240–244, 2010. View at Google Scholar
  15. G. M. Stavrakakis, D. P. Karadimou, P. L. Zervas, H. Sarimveis, and N. C. Markatos, “Selection of window sizes for optimizing occupational comfort and hygiene based on computational fluid dynamics and neural networks,” Building and Environment, vol. 46, no. 2, pp. 298–314, 2011. View at Publisher · View at Google Scholar · View at Scopus
  16. M. Buragohain and C. Mahanta, “A novel approach for ANFIS modelling based on full factorial design,” Applied Soft Computing, vol. 8, no. 1, pp. 609–625, 2008. View at Publisher · View at Google Scholar · View at Scopus
  17. B. Sun, N. G. Jiang, K. Han et al., “The optimal design method using CFD combined with genetic algorithm for indoor thermal comfort,” Journal of Chongqing Jianzhu University, S2, pp. 119–122, 2011. View at Google Scholar
  18. Q. L. Luan and H. B. Lu, “Research of intrusion detection based on neural network optimized by adaptive genetic algorithm,” Computer Engineering and Design, 2008.
  19. Q. He, “Research on the development of artificial intelligence technology,” Modern Telecommunication Technology, vol. 2, pp. 18–21, 2016. View at Google Scholar
  20. I. Dis, Moderate Thermal Environments—Determination of the PMV and PPD Indices and Specification of the Conditions for Thermal Comfort, International Standards Organization, Geneva, Switzerland, 1984.
  21. P. O. Fanger, A. K. Melikov, H. Hanzawa, and J. Ring, “Air turbulence and sensation of draught,” Energy and Buildings, vol. 12, no. 1, pp. 21–39, 1988. View at Publisher · View at Google Scholar · View at Scopus
  22. B. Zhao, D. L. Li, X. T. Li et al., “Error pretreatment method for numerical simulation of indoor air flow,” Journal of Tsinghua University (Natural Science Edition), vol. 10, pp. 114–117, 2001. View at Google Scholar
  23. L. Zhou and F. Haghighat, “Optimization of ventilation system design and operation in office environment, part I: methodology,” Building and Environment, vol. 44, no. 4, pp. 651–656, 2009. View at Publisher · View at Google Scholar · View at Scopus
  24. R. Z. Freire, G. H. C. Oliveira, and N. Mendes, “Predictive controllers for thermal comfort optimization and energy savings,” Energy and Buildings, vol. 40, no. 7, pp. 1353–1365, 2008. View at Publisher · View at Google Scholar · View at Scopus
  25. T. Kim, D. Song, S. Kato, and S. Murakami, “Two-step optimal design method using genetic algorithms and CFD-coupled simulation for indoor thermal environments,” Applied Thermal Engineering, vol. 27, no. 1, pp. 3–11, 2007. View at Publisher · View at Google Scholar · View at Scopus
  26. D.-C. Ma, Y.-B. Diao, Y.-Z. Guo et al., “A novel method to predict protein-protein interactions based on the information of protein-protein interaction networks and protein sequence,” Protein and Peptide Letters, vol. 18, no. 9, pp. 906–911, 2011. View at Publisher · View at Google Scholar · View at Scopus
  27. H. H. H. Homeier, “A hierarchically consistent, iterative sequence transformation,” Numerical Algorithms, vol. 8, no. 1, pp. 47–81, 1994. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  28. W. Xu, X.-G. Chen, J. Zhao, and H. Hu, “Thermal comfort in offices: comfort values and optimization of indoor control variables,” Journal of Beijing Institute of Technology, vol. 20, no. 1, pp. 123–128, 2011. View at Google Scholar · View at Scopus
  29. J. H. Huang and H. Zhang, Human and Thermal Environment, Science Press, Beijing, China, 2011.
  30. Y. Z. Ji, K. Gao, X. J. Wang et al., “Study on the influence of air velocity on thermal comfort of human body,” Journal of Lanzhou University, vol. 2, pp. 95–99, 2003. View at Google Scholar
  31. T. Bedford, Basic Principles of Ventilation and Heating, Lewis, London, UK, 1998.
  32. R. Nevins, R. R. Gonzalez, Y. Nishi et al., “Effect of change in ambient temperature and level of humidity on comfort and thermal sensation,” ASHRAE Transactions, vol. 81, no. 2, pp. 64–77, 1975. View at Google Scholar
  33. S. V. Pascal and Z. Z. Zhang, Numerical Calculation of Heat Transfer and Fluid, Science Press, Beijing, China, 1984.
  34. S. Shamshirband, A. Malvandi, A. Karimipour et al., “Performance investigation of micro- and nano-sized particle erosion in a 90° elbow using an ANFIS model,” Powder Technology, vol. 284, pp. 336–343, 2015. View at Publisher · View at Google Scholar · View at Scopus
  35. C. Y. Zhang, Research of Fuzzy Neural Network Based on Improved PSO Algorithm, Harbin University of Science and Technology, 2014.