Customizable Energy Management in Smart Buildings Using Evolutionary Algorithms

  • Florian AllerdingEmail author
  • Ingo Mauser
  • Hartmut Schmeck
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8602)


Various changes in energy production and consumption lead to new challenges for design and control mechanisms of the energy system. In particular, the intermittent nature of power generation from renewables asks for significantly increased load flexibility to support local balancing of energy demand and supply. This paper focuses on a flexible, generic energy management system for Smart Buildings in real-world applications, which is already in use in households and office buildings. The major contribution is the design of a “plug-and-play”-type Evolutionary Algorithm for optimizing distributed generation, storage and consumption using a sub-problem based approach. Relevant power consuming or producing components identify themselves as sub-problems by providing an abstract specification of their genotype, an evaluation function and a back transformation from an optimized genotype to specific control commands. The generic optimization respects technical constraints as well as external signals like variable energy tariffs. The relevance of this approach to energy optimization is evaluated in different scenarios. Results show significant improvements of self-consumption rates and reductions of energy costs.


Energy Management Smart Building Evolutionary Algorithm Combined Heat and Power Plant Household Appliances 


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  1. 1.
    Abras, S., Ploix, S., Pesty, S., Jacomino, M.: A multi-agent home automation system for power management. In: Cetto, J.A., Ferrier, J.-L., Costa dias Pereira, J.M., Filipe, J. (eds.) Informatics in Control Automation and Robotics, vol. 15. LNEE (LNCS), pp. 59–68. Springer, Heidelberg (2008)Google Scholar
  2. 2.
    Allerding, F., Premm, M., Shukla, P.K., Schmeck, H.: Electrical Load Management in Smart Homes Using Evolutionary Algorithms. In: Hao, J.-K., Middendorf, M. (eds.) EvoCOP 2012. LNCS, vol. 7245, pp. 99–110. Springer, Heidelberg (2012)Google Scholar
  3. 3.
    Allerding, F., Schmeck, H.: Organic smart home: architecture for energy management in intelligent buildings. In: Proceedings of the 2011 Workshop on Organic Computing. ACM (2011)Google Scholar
  4. 4.
    Babu, C., Ashok, S.: Peak load management in electrolytic process industries. IEEE Transactions on Power Systems (2008)Google Scholar
  5. 5.
    Crawley, D.B., Hand, J.W., Kummert, M., Griffith, B.T.: Contrasting the capabilities of building energy performance simulation programs. Building and Environment (2008)Google Scholar
  6. 6.
    Di Giorgio, A., Pimpinella, L.: An event driven smart home controller enabling consumer economic saving and automated demand side management. Applied Energy (2012)Google Scholar
  7. 7.
    Federal Ministry of Economics and Technology (BMWi): Germany’s new energy policy - Heading towards 2050 with secure, affordable and environmentally sound energy, Berlin (2012)Google Scholar
  8. 8.
    Ha, D.L., Joumaa, H., Ploix, S., Jacomino, M.: An optimal approach for electrical management problem in dwellings. Energy and Buildings (2012)Google Scholar
  9. 9.
    Mohsenian-Rad, Leon-Garcia, A.: Optimal residential load control with price prediction in real-time electricity pricing environments. Transactions on Smart Grid (2010)Google Scholar
  10. 10.
    Müller-Schloer, C., Schmeck, H., Ungerer, T.: Organic Computing - A Paradigm Shift for Complex Systems. Birkhauser Verlag AG (2011)Google Scholar
  11. 11.
    Sou, K.C., Weimer, J., Sandberg, H., Johansson, K.H.: Scheduling smart home appliances using mixed integer linear programming. In: Decision and Control and European Control Conference (CDC-ECC). IEEE (2011)Google Scholar
  12. 12.
    Zipf, M., Möst, D.: Impacts of volatile and uncertain renewable energy sources on the german electricity system. In: European Energy Market (EEM). IEEE (2013)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Florian Allerding
    • 1
    Email author
  • Ingo Mauser
    • 2
  • Hartmut Schmeck
    • 1
  1. 1.Karlsruhe Institute of Technology – Institute AIFBKarlsruheGermany
  2. 2.FZI Research Center for Information TechnologyKarlsruheGermany

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