An Intelligent Approach of Regulating Electric-Fan Adapting to Temperature and Relative Humidity

Автор: Ali Newaz Bahar, Mrinal Kanti Baowaly, Abhijit Chakraborty

Журнал: International Journal of Intelligent Systems and Applications(IJISA) @ijisa

Статья в выпуске: 7 vol.4, 2012 года.

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In our daily lives, we enjoy the service of thousands of devices and systems that have made our lives easier and more comfortable. Electric fan is one of the most popular and used systems in developing countries like Bangladesh for its cost effectiveness and low power consumption. In the era of twenty-first century we expect all of our living and working systems will be intelligent when it will provide the service. We have developed a fuzzy inference system that effectively and intelligently controls the rotating speed of an electric fan according to the temperature of environment and its relative humidity. We used experimental data and verified the experimental data with different mathematical procedure to ensure that our result is well enough. We designed a simulation system to test the result but it can be easily implemented on hardware level, since fuzzy logic toolbox provides such facility.

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Fuzzy logic, controller, inference system, fuzzy rules, curve fitting tools, relative humidity

Короткий адрес: https://sciup.org/15010281

IDR: 15010281

Список литературы An Intelligent Approach of Regulating Electric-Fan Adapting to Temperature and Relative Humidity

  • M. Hamdi, and G. A Lachiver, “Fuzzy Control System Based on the Human Sensation of Thermal Comfort," IEEE, 1998, pp.487- 492.
  • Ari, S., Cosden, I. A., Khalifa, H. E., Dannenhoffer, J. F., Wilcoxen, P. and Isik, C. , 2007: “Individual Thermal Comfort and Energy Optimization”, Proceedings of Clima Wellbeing Indoors.
  • Dr. Hassan Moghbelli, Sinan Sabih, Fatima Ali “Investigation and Design of Solar Cell System for Households in Gulf Cooperation Council (GCC)”
  • http://dealnews.com/features/Get-Some-Deals-to-Keep-Your-House-Cool/461807.html
  • Kuntze, H.-B, Bernard, Th, “A new fuzzy-based supervisory control concept for the demand-responsive optimization of HVAC control systems” Decision and Control, Proceedings of the 37th IEEE Conference on, vol. 4, pp. 4258-4263, 1998.
  • De SILVA, C. , “Knowledge Base Decoupling in Fuzzy Logic Control Systems”, Proceedings of the American Control Conference, San Francisco, USA, vol. 1, 760–764, 1993.
  • Defuzzification: criteria and classification, from the journal Fuzzy Sets and Systems, Van Leekwijck and Kerre, Vol. 108 (1999), pp. 159-178
  • C.M. Chu, T.L. Jong, and Y.W. Huang, " Thermal Comfort Control on Multi-Room Fan Coil Unit System Using LEE-Based Fuzzy Logic," Energy Conversion and Management, Vol. 46, Issues 9-10, June 2005, pp. 1579-1593. 10.
  • Jantzen, J., September, 1998: „Tuning of Fuzzy PID Controllers‟, Technical University of Denmark, report 98-H 871.
  • “Effects of Air Velocity on Thermal Comfort in Hot and Humid Climates” by W. Srivajana, Thammasat Int. J. Sc.Tech.Vol. 8, No. 2, April-June 2003).
  • MATLAB: Curve Fitting Toolbox http://www.mathworks.com/products/curvefitting
  • MATLAB: Fuzzy Logic Toolbox User’s Guide, www.mathworks.com, 2008.
  • Jones, B. W., 2002, “Capabilities and limitations of thermal models for use in thermal comfort standards”, Energy and Buildings, Vol. 1, No. 34, pp. 653–659.
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