INTELLIGENT AND ADAPTIVE FUZZY CONTROL SYSTEM FOR ENERGY EFFICIENT HOMES
DOI:
https://doi.org/10.17770/etr2017vol1.2631Keywords:
Energy Efficiency, Fuzzy logic, Sensow networkAbstract
“Smart houses” have widely established their position as a research field during the last decade. Nowadays the technical solutions related to energy resource management are being rapidly developed and integrated into the daily lives of people. The energy resource management systems use sensor networks for receiving and processing information during the realia time. Smart house adaptive and intelligent solutions has advanced towards common environment, which can take care of the inhabitants’ well-being in numerous ways. This paper propose to use a context sensitive and proactive fuzzy control system for controlling the automation processes in smart house environment. The designed monitoring system has adaptive and intelligent options, and it can operate using real time information received from sensors. The system is designed to operate fully in the background and can be installed to any exiting working system. This paper describes a central heating boiler control system implemented using the fuzzy control system designed. Author concentrates on the basic operation of such systems and present findings from the design process and initial tests.Downloads
References
Antonio J. , Juan M. , Baringo L., "Real-Time Demand Response Model", IEEE Transaction on Smart Grid, Vol. 1, No. 3, 2010, pp. 236–242.
Jurenoks A., Dejan J., Sensor Network Information Flow Control Method with Static Coordinator Within Internet of Things in Smart House Environment, Procedia Computer Science vol. 104., pp. 385. - 392. 2017
Holmblad L. P., Ostergaard J. J. (1982) Control of Cement Kiln by Fuzzy Logic, Gupta M. M, Sarchez E, Fuzzy Information and Decision Processes, North Holland, pp. 389-399
Kaila, L., Vainio, A.-M., Vanhala, J., Connecting the smart home, IASTED, Networks and Communication Systems, April 18-20, 2005, pp. 445-450
Kuhn T. R. , "Energizing Efficiency's Potential," The Electricity Journal, vol. 19, pp. 83-87, 2006
LeMay M. , Nelli R. , Gross G., Gunter C. A. , “An integrated architecture for demand response communications and control”, The 41st Annual IEEE Hawaii International Conference on System Sciences (HICSS '08), Waikola, Hawaii, January 2008
Lui T. J. , Stirling W. , Marcy H. O. , “Get Smart”, IEEE Power and Energy Magazine, Volume 8, Issue 3, pp. 66-78, May 2010.
Mäntyjärvi, J., Seppänen, T. Adapting applications in handheld devices using fuzzy context information, Interacting with Computers, vol. 15, issue 4, p. 521-538, August 2003
Sancho J., Sánchez-Soriano J., Chazarra J. A. , Aparicio J. , "Design and implementation of a decision support system for competitive electricity markets," Decision Support Systems, vol. 44, pp. 765-784, 2008
Shahgoshtasbi D., Jamshidi M. , “Energy efficiency in a smart house with an intelligent Neuro-Fuzzy lookup table”, Proc. the 6th IEEE international conference on System of Systems Engineering (SOSE 2011), Albuquerque, NM, USA, 2011
Shahgoshtasbi D., Jamshidi M. , ”Modified Intelligent Energy Management system in a smart house”, Proc. World Automation Congress, Puerto Vallarta, Mexico, 2012.
Zadeh, L.H., 1965. Fuzzy sets. Information and Control, pp: 338-353.