As of August 2024, International Journal "Notes on Intuitionistic Fuzzy Sets" is being indexed in Scopus.
Please check our Instructions to Authors and send your manuscripts to nifs.journal@gmail.com. Next issue: September/October 2024.

Open Call for Papers: International Workshop on Intuitionistic Fuzzy Sets • 13 December 2024 • Banska Bystrica, Slovakia/ online (hybrid mode).
Deadline for submissions: 16 November 2024.

Issue:Multi-objective optimisation in air-conditioning systems: comfort/discomfort definition by IF sets: Difference between revisions

From Ifigenia, the wiki for intuitionistic fuzzy sets and generalized nets
Jump to navigation Jump to search
m Borislav: shortcut nifs/7/1/10-21
{{Special:WhatLinksHere/Issue:{{PAGENAME}}|namespace=102|hidetrans=1|hideredirs=1}}
Line 60: Line 60:
# H.-J. Zimmermann, Fuzzy Sets - Theory and its Application (Kluwer, Dordrecht, 1985)
# H.-J. Zimmermann, Fuzzy Sets - Theory and its Application (Kluwer, Dordrecht, 1985)
  | citations      =  
  | citations      =  
{{Special:WhatLinksHere/Issue:{{PAGENAME}}|namespace=102|hidetrans=1|hideredirs=1}}
  | see-also        =  
  | see-also        =  
}}
}}

Revision as of 16:03, 30 May 2016

shortcut
http://ifigenia.org/wiki/issue:nifs/7/1/10-21
Title of paper: Multi-objective optimisation in air-conditioning systems: comfort/discomfort definition by IF sets
Author(s):
Plamen Angelov
Building Services Engineering Research Group Department of Civil and Building Engineering, Loughborough University, Loughborough LEl l 3TU, Leicestershire, UK
P.P.Angelov@Lboro.ac.UK
Published in: "Notes on IFS", Volume 7 (2001), Number 1, pages 10-21
Download:  PDF (1040  Kb, File info)
Abstract: The problem of multi-objective optimisation of air-conditioning (AC) systems is treated in the paper in the framework of intuitionistic fuzzy set theory. The nature of the problem is multi-objective one with requirements for minimal costs (generally, life cycle costs; more specifically, energy costs) and maximal occupants' comfort (minimal discomfort). Moreover, its definition by conventional means is bounded to a number of restrictions and assumptions, which are often far from the real-life situations. Attempts have been made to formulate and solve this problem by means of the fuzzy optimisation. The present paper makes further step by exploring the innovative concept of intuitionistic fuzzy sets into definition of the trickiest issue: comfort and discomfort definition. The new approach allows to formulate more precisely the problem which compromises energy saving and thermal comfort satisfaction under given constraints. The resulting IF optimisation problem could be solved numerically or, under some assumptions, analytically. An example illustrates the viability of the proposed approach. A solution which significantly (with 35%) improves comfort is found which is more energetically expensive with only 0.6%. This illustrates the possibility to use the approach for trade-off analysis in multi-objective optimisation of AC systems.
Keywords: Intuitionistic fuzzy sets, IF optimisation, multi-objective optimisation, air-conditioning systems, comfort/discomfort
References:
  1. Angelov P., Optimization in an Intuitionistic Fuzzy Environment, Fuzzy Sets and Systems, 86, 299-306.1997
  2. Angelov P., Intuitionistic fuzzy optimization, "Notes on Intuitionistic Fuzzy Sets", Volume 1, 1995, Number 2, pages 27-33
  3. Angelov P., Crispification: Defuzzification over Intuitionistic Fuzzy Sets, BUSEFAL, 64, 51-55, 1995
  4. Angelov P., A Fuzzy Approach to Multi-Objective Optimization of Building Thermal Systems, Proc. of the 8th IFSA World Congress, Taiwan, Taipei, 17-19 August 1999, 1,429-434
  5. Angelov P., Evolving Fuzzy Rule-based Models, Journal of CIIE, special issue on Industrial Application of Soft Computing, 17, 2459-468, 2000, ISSN 1017-0669
  6. Atanassov K., Intuitionistic fuzzy sets, Fuzzy Sets and Systems, 20, 87-96, 1986
  7. Atanassov K., Ideas for intuitionistic fuzzy equations, Notes on Intuitionistic Fuzzy Sets, Volume l, 1995, Number 1, pages l7-24
  8. Bustince H., Handling multicriteria fuzzy decision making problems based on intuitionistic fuzzy sets, Notes on Intuitionistic Fuzzy Sets, Volume l, 1995, Number 1, 42-47
  9. Wrieht J.A., HVAC Optimisation Studies: Sizing by GA, Building Systems Engineering Research and Technology,17 (1) (1996) 7-14
  10. Ren M.J., Optimal Predictive Control of Thermal Storage in Hollow Core Ventilated Slab Systems, P h.D.Thesis, Loughborough University (1 997)
  11. Kajl S., P.Malinowski, E.Czogala, M.Blazinski, Fuzzy logic and NN approach to thermal description of buildings, Proc. of the 3th European Congress on Fuzzy and Intelligent Techniques EUFIT'9S, Aachen, Germany v.l (1995) 299-303
  12. Skrjanc I. et.al., Flzzy Modeling of Thermal Behavior in Building, Proc. of the 6th European Congress on Intelligent Techniques EUFIT'98, Aachen, Germany, 2 (1998) 878-882
  13. Loveday D.L., G.S. Virk, Artificial Intelligence for Buildings, Applied Energy,4l, 1992,201-221
  14. Culp C.et al.,The Impact of AI Technology within the FIVAC Industry ASHRAE Journal, l2, 1990,12-22
  15. Hagras H., V. Callaghan, M. Colley, G. Clarke, A Hierarchical Fuzzy Genetic Multi-agent Architecture for Intelligent Buildings Sensing and Control, Conference on Advances in Soft Computing, Leicester, June 29-30 2000
  16. Sharples S., V. Callaghan, G. Clarke, A Multiagent Architecture for Intelligent Building Sensing and Control, Sensor Review,19 (2) 135-140, 1999
  17. Callaghan V., G.Clarke, M.Colley, H.Hagras, A Soft-Computing DAI Architecture for Intelligent Buildings, Journal of Studies in Fuzziness and Soft Computing Agents, Springer Verlag, July 2000
  18. Coen M.H., Building Brains for Rooms: Designing Distributed Software Agents, Proc. 9th Innovative Applications ofAI Conference, AAAI Press, 1997
  19. Mozer M., The Neural Network House: An Environment that Adapts to Its Inhabitants, Proc. AAAI Spring Symposium on Intelligent Environments, 1998, AAAI Press, 110-114
  20. Garg V., N.K. Bansal, Smart Occupancy Sensors to Reduce Energy Consumption, Energy and Buildings, 32, 2000, 81-87
  21. Tayloa Francis, Moderate Thermal Environments - Determination of the PMV and PPD indices and specification of the condition for thermal comfort, BS EH ISO 7730, (1995), EO
  22. Roulet C.-A., Indoor Environment Quality in Buildings and its Impact on Outdoor Environment, Energy and Buildings, 33, 2001, 183 - 191
  23. Raja I.A., J.F. Nicol, K.J. McCartney, M.A. Humphreys, Thermal Comfort: use of Controls in Naturally Ventilated Buildings, Energy and Buildings, 33, 2001, 235-244
  24. P. Angelov, A Generalized Approach to Fuzzy Optimization, International Journal of Intelligent Systems 9 (4) (1994)261-268.
  25. P. Angelov, An Analytical Method for Solving a Type of Fuzzy Optimization Problems, Control and Cybernetics 3 (1995) (to appear)
  26. P. Angelov, Approximate Reasoning Based Optimization, Yugoslav Journal on Operations Research 4 (1) (1994) ll-17.
  27. R. Bellman and L. Zadeh, Decision Making in a Fuzzy Environment, Management Science 17 (1970) 141-160.
  28. D. Filev and P. Angelov, Fuzzy Optimal Control, Fuzzy Sets and Systems 47 (1992) 151-156.
  29. J. Kacprzyk, A Generalization of Fuzzy Multistage Decision Making and Control via Linguistic Quantifiers, lnternational Journal ofControl3S (1983) 1249-1270.
  30. Luhandjula M., Fvzzy Optimization: An Appraisal, Fuzzy Sets and Systems 30 (1988) 257-282.
  31. M.R. Maleki and M.Mashinchi, An Algorithm for Solving a Fuzzy Linear Programming, in: Proc. of V IFSA World Congress (Seoul, Korea, 4-9 July 1993) 1, 641-643.
  32. H. Rommelfanger, Inequality Relations in Fv4r Constraints and its use in Linear Fuzzy Optimization, in: Verdegay J.L., Delgado M. Eds: The Interface Between Artificial Intelligence and Operational Research in Fuzzy Environment (Verlag TUV, Rheinland, Koln, 1989) 195-211.
  33. M. Sakawa and H. Yano, An Interactive Fuzzy Satisficing Method for Multiobjective Non Linear Programming Problems with Fuzzy Parameters, Fuzzy Sets and Systems 30 (1989) 221-238.
  34. H. Tanaka and K. Asai, Fuzzy Linear Programming Problems with Fuzzy Numbers, Fuzzy Sets and Systems 13 (1984) l-10.
  35. H.-J. Zimmermann, Fuzzy Mathematical Programmin g, Computers and Operations Research 10 (1983) 291-298
  36. H.-J. Zimmermann, Fuzzy Sets - Theory and its Application (Kluwer, Dordrecht, 1985)
Citations:

The list of publications, citing this article may be empty or incomplete. If you can provide relevant data, please, write on the talk page.