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

Malin Lachmann, Jaime Maldonado, Wiebke Bergmann, Francesca Jung, Markus Weber and Christof Büskens
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Malin Lachmann: Center for Industrial Mathematics, University of Bremen, Bibliothekstr. 5, 28359 Bremen, Germany
Jaime Maldonado: Cognitive Neuroinformatics, University of Bremen, Enrique-Schmidt-Straße 5, 28359 Bremen, Germany
Wiebke Bergmann: Center for Industrial Mathematics, University of Bremen, Bibliothekstr. 5, 28359 Bremen, Germany
Francesca Jung: Center for Industrial Mathematics, University of Bremen, Bibliothekstr. 5, 28359 Bremen, Germany
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

Energies, 2020, vol. 13, issue 8, 1-42

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.

Keywords: data-based modeling; data-driven modeling; least-squares regression; linear regression; clustering; simulated annealing; nonlinear optimization; self-consumption optimization; energy management (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
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