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Self-Learning Data-Based Models as Basis of a Universally Applicable Energy Management System

Author

Listed:
  • Malin Lachmann

    (Center for Industrial Mathematics, University of Bremen, Bibliothekstr. 5, 28359 Bremen, Germany
    These authors contributed equally to this work.)

  • Jaime Maldonado

    (Cognitive Neuroinformatics, University of Bremen, Enrique-Schmidt-Straße 5, 28359 Bremen, Germany
    These authors contributed equally to this work.)

  • Wiebke Bergmann

    (Center for Industrial Mathematics, University of Bremen, Bibliothekstr. 5, 28359 Bremen, Germany
    Steinbeis Innovation Center for Optimization and Control, Schmalenbecker Str. 33, 28879 Grasberg, Germany
    These authors contributed equally to this work.)

  • Francesca Jung

    (Center for Industrial Mathematics, University of Bremen, Bibliothekstr. 5, 28359 Bremen, Germany
    These authors contributed equally to this work.)

  • Markus Weber

    (Center for Industrial Mathematics, University of Bremen, Bibliothekstr. 5, 28359 Bremen, Germany)

  • Christof Büskens

    (Center for Industrial Mathematics, University of Bremen, Bibliothekstr. 5, 28359 Bremen, Germany)

Abstract

In the transfer from fossil fuels to renewable energies, grid operators, companies and farms develop an increasing interest in smart energy management systems which can reduce their energy expenses. This requires sufficiently detailed models of the underlying components and forecasts of generation and consumption over future time horizons. In this work, it is investigated via a real-world case study how data-based methods based on regression and clustering can be applied to this task, such that potentially extensive effort for physical modeling can be decreased. Models and automated update mechanisms are derived from measurement data for a photovoltaic plant, a heat pump, a battery storage, and a washing machine. A smart energy system is realized in a real household to exploit the resulting models for minimizing energy expenses via optimization of self-consumption. Experimental data are presented that illustrate the models’ performance in the real-world system. The study concludes that it is possible to build a smart adaptive forecast-based energy management system without expert knowledge of detailed physics of system components, but special care must be taken in several aspects of system design to avoid undesired effects which decrease the overall system performance.

Suggested Citation

  • Malin Lachmann & Jaime Maldonado & Wiebke Bergmann & Francesca Jung & Markus Weber & Christof Büskens, 2020. "Self-Learning Data-Based Models as Basis of a Universally Applicable Energy Management System," Energies, MDPI, vol. 13(8), pages 1-42, April.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:8:p:2084-:d:348569
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    References listed on IDEAS

    as
    1. Jacobsson, Staffan & Lauber, Volkmar, 2006. "The politics and policy of energy system transformation--explaining the German diffusion of renewable energy technology," Energy Policy, Elsevier, vol. 34(3), pages 256-276, February.
    2. Verzijlbergh, R.A. & De Vries, L.J. & Dijkema, G.P.J. & Herder, P.M., 2017. "Institutional challenges caused by the integration of renewable energy sources in the European electricity sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 660-667.
    3. Al-Wakeel, Ali & Wu, Jianzhong & Jenkins, Nick, 2017. "k-means based load estimation of domestic smart meter measurements," Applied Energy, Elsevier, vol. 194(C), pages 333-342.
    4. Karoline A. Mester & Marion Christ & Melanie Degel & Wolf-Dieter Bunke, 2017. "Integrating Social Acceptance of Electricity Grid Expansion into Energy System Modeling: A Methodological Approach for Germany," Progress in IS, in: Volker Wohlgemuth & Frank Fuchs-Kittowski & Jochen Wittmann (ed.), Advances and New Trends in Environmental Informatics, pages 115-129, Springer.
    5. Larson, David P. & Nonnenmacher, Lukas & Coimbra, Carlos F.M., 2016. "Day-ahead forecasting of solar power output from photovoltaic plants in the American Southwest," Renewable Energy, Elsevier, vol. 91(C), pages 11-20.
    6. Wang, Guang Chao & Ratnam, Elizabeth & Haghi, Hamed Valizadeh & Kleissl, Jan, 2019. "Corrective receding horizon EV charge scheduling using short-term solar forecasting," Renewable Energy, Elsevier, vol. 130(C), pages 1146-1158.
    7. McLoughlin, Fintan & Duffy, Aidan & Conlon, Michael, 2015. "A clustering approach to domestic electricity load profile characterisation using smart metering data," Applied Energy, Elsevier, vol. 141(C), pages 190-199.
    8. Beaudin, Marc & Zareipour, Hamidreza, 2015. "Home energy management systems: A review of modelling and complexity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 318-335.
    9. Esen, Hikmet & Inalli, Mustafa & Esen, Yuksel, 2009. "Temperature distributions in boreholes of a vertical ground-coupled heat pump system," Renewable Energy, Elsevier, vol. 34(12), pages 2672-2679.
    10. Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
    11. Boukouvala, Fani & Misener, Ruth & Floudas, Christodoulos A., 2016. "Global optimization advances in Mixed-Integer Nonlinear Programming, MINLP, and Constrained Derivative-Free Optimization, CDFO," European Journal of Operational Research, Elsevier, vol. 252(3), pages 701-727.
    12. Žáčeková, Eva & Váňa, Zdeněk & Cigler, Jiří, 2014. "Towards the real-life implementation of MPC for an office building: Identification issues," Applied Energy, Elsevier, vol. 135(C), pages 53-62.
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