Implementing intelligent technical systems into smart homes by using model based systems engineering and multi-agent systems

J. Michael, M. Hillebrand, B. Wohlers, C. Henke, R. Dumitrescu, M. Meyer, A. Trächtler



This paper shows a methodologically interdisciplinary approach to develop smart homes by using Model-Based Systems Engineering (MBSE) and a Multi-Agent-System (MAS)
Using the methods and techniques of MBSE/MAS leads to an efficient development of a smart home and intelligent consumption of energy of the involved appliances. At the beginning there is a specification technique used to specify an appliance. This contains analysing the requirements,
evaluating the functionality of the system and choosing solution-elements. Each single system shows an intelligent, self-optimizing behaviour, which has to be up scaled to a socalled global optimum. This is reached by implementing connections between the appliances and a negotiation for available energy. Physical simulation models deliver the required energy for different processes and enable to predict the need of energy in prospective time segments. These physical models again need to be controlled by the controller models, which are also developed exemplarily in this paper. Therefore a structure of the controller is explained, which among others
contains the functionality of negotiation and optimization. This functionality is furthermore used to circumvent a defined energy-supply-bottleneck-situation.

Published in: Renewable Energy & Power Quality Journal (RE&PQJ, Nº. 14)
Pages:359-364 Date of Publication: 2016/5/20
ISSN: 2172-038X Date of Current Version:2016/05/04
REF:320-16 Issue Date: May 2016
DOI:10.24084/repqj14.320 Publisher: EA4EPQ

Authors and affiliations

J. Michael, M. Hillebrand, B. Wohlers, C. Henke. R. Dumitrescu, , M. Meyer, A. Trächtler
Fraunhofer Project Group for Mechatronic System Design, Paderborn. Germany

Key words

Model-Based Systems Engineering, Multi-Agent Systems, Intelligent Technical Systems, Smart Home, Home Appliances


[1] Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety: Action program for climate protection., (02.01.2016)
[2] Federal Ministry for Economic Affairs and Energy: Energiewende direct. Issue 13/2015, infografikwindkraftkapazitaet.html, (05.01.2016)
[3] Idealo Energy Comparison: Overproduction of Energy at Pentecost strains grids and consumer., (02.01.2016)
[4] Fraunhofer Institut for Windenergy and Energy-System Technologies: Strommarkt Flexibilisierung- Hemmnisse und
Lösungskonzepte. Study, Bochum, January 2015, ISBN-13: 978-3-920328-72-0
[5] VDE Verband der Elektrotechnik: VDERoadmap– Die deutsche Normungsroadmap Smart Home + Building. Frankfurt, November 2013
[6] VDI Verein Deutscher Ingenieure: VDI 2206 Design methodology for mechatronic systems. Guideline, Düsseldorf 2004
[7] Kruse, D.;Schweers, C.; Trächtler, A.: Methodology for a partly automated parameter identification fort the validation of multi-domain models. In: ASME International Mechanical Engineering Congress and Exposition, Montreal, 2014
[8] Kruse, D.; Trächtler, A.; Herden, R.: Modellbasierte Entwicklung eines neuartigen Heizverfahrens für Waschautomaten. In: Paderborner Workshop: „Entwurf mechatronischer Systeme“, Paderborn, 2013
[9] Strube, G.: Modeling Motivation and Action Control in Cognitive Systems. Mind Modelling, Berlin, Pabst, 1998
[10] Dumitrescu, R.: Entwicklungssystematik zur Integration kognitiver Funktionen in fortgeschrittene mechatronische Systeme.Dissertation, Paderborn, Januar 2011
[11] Michael, J.; Hillebrand, M.: Modellbasierte Mehrzieloptimierung zur Integration von Hausgeräten in Smart Grids. VDI Mechatronik Dortmund, March 2015
[12] Zambonelli, F.; Jennings, N.; Wooldridge, M.: Developing Multiagent Systems: The Gaia Methodology. ACM Transactions on Software Engineering and Methodology, Vol.12, No. 3, July 2003