Modeling the building energy management system of a building using a revised approach of fuzzy cognitive maps

V. Mpelogianni, P. P. Groumpos


Fuzzy Cognitive Maps (FCMs) are a very simple, useful and powerful tool for modeling and analyz-ing dynamic complex systems. FCMs can structure virtual worlds that dynamically change with time. Mathe-matical models of FCMs are reviewed and a number of problems which emerged with them are briefly ana-lyzed. In order to address some of these drawbacks a revised approach is proposed. This approach is being used to analyze the behavior and control the Building Energy Management System of a building. Simulation results of the new method are presented and discussed.

Ключевые слова

Building energy management system; energy efficiency; fuzzy cognitive maps

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Wang S. W. Intelligent Buildings and Building Automa-tion, Spon Press, Taylor & Francis,2009.

Axelrode R. The analysis of cognitive maps”, Structureof decision, pp. 55-73, 1976.

Kosko B. Fuzzy cognitive maps”, International Journal of ManMachine Studies, vol. 24, no. 1, pp. 65-75, 1986.

Kosko B. Fuzzy Thinking: The New Science of Fuzzy Logic, New York: Hyperion, 1993.

Wong, J. K. W., Heng Li, and S. W. Wang Intelligentbuilding research: a review, Automation in construction, 14.1. 2005, pp 143-159.

Anninou A.P., Groumpos P.P., Polychronopoulos P.Modeling health diseases using competitive fuzzy cognitive maps, Artificial Intelligence Applications and Innovations, Springer Berlin Heidelberg, 2013, pp 88-95.

Papageorgiou E.I, Stylios C. D., and Groumpos P. P.Active Hebbian learning algorithm to train fuzzy cognitive maps, International journal of approximate reasoning, 37.3.2004, pp. 219-249.

Papageorgiou E.I., Stylios C. D. Fuzzy cognitive maps,Handbook of Granular Computing, John Wiley & Son Ltd, Pub-lication Atrium, Chichester, England, 2008.

Papageorgiou E.I., Stylios C. D., Groumpos P. P. FuzzyCognitive Map Learning based on Non-Linear Hebbian Rule, Advances in Artificial Intelligence Lecture Notes in Computer Science, Spinger, 2003, Vol. 2903, pp. 256-268.

Papageorgiou E.I., Stylios C. D., Groumpos P. P. “Un-supervised Learning Techniques For Fine-Tuning Fuzzy Cogni-tive Map Causal Links.” International Journal of Human-Computer Studies, Vol. 64 Issue 8, pp. 727-743, 2008.

Kannappan A., Tamilarasi A., Papageorgiou E.I.Analyzing the performance of fuzzy cognitive maps with non-linear hebbian learning algorithm in predicting autistic disor-der, Journal of Expert Systems with Applications: An Interna-tional Journal, Pergamon Press Inc, 3.3.2011, Vol 38 I. 3, pp. 1282-1292.

Belogiannis G. D., Mpelogianni V.G., KalamatianouA. G. Modeling gender participation in the Greek university education: 16th Conference of the Applied Stochastic Models and Data Analysis International Society and Demographics, Piraeus, Greece, 2015.

Doukas H., Patlitzianas K., Iatropoulos K., Psarras J.Intelligent building energy management system using rule sets, Building and Environment, 2007, Vol.42, pp.3562–3569.

Auvil R J. HVAC Control Systems, American TechnicalPublishers, 2003.

Haines R.W., Hittle D.C. Control Systems for Heating,Ventilating, and Air Conditioning, Springer, January 2006.

Levermore G.J. Building Energy Management Sys-tems: An Application to Heating, Natural Ventilation, Lighting and Occupant Satisfaction, E & FN Spon, August 2000.

Groumpos P. P. Fuzzy Cognitive Maps: Basic Theories and their Application to Complex Systems, Fuzzy Cognitive Maps Studies in Fuzziness and Soft Computing,2011 Vol. 247, pp. 1- 22.

Runkler T.A. Selection of appropriate defuzzιfication methods using application specific properties, IEEE Transac-tion on Fuzzy Systems, 1997, Vol.5(1), pp. 72-79.


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(c) 2019 V. Mpelogianni, P. P. Groumpos