Self-Learning Data-Based Models as Basis of a Universally Applicable Energy Management System
1
Center for Industrial Mathematics, University of Bremen, Bibliothekstr. 5, 28359 Bremen, Germany
2
Cognitive Neuroinformatics, University of Bremen, Enrique-Schmidt-Straße 5, 28359 Bremen, Germany
3
Steinbeis Innovation Center for Optimization and Control, Schmalenbecker Str. 33, 28879 Grasberg, Germany
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Energies 2020, 13(8), 2084; https://doi.org/10.3390/en13082084
Received: 20 March 2020
/
Revised: 15 April 2020
/
Accepted: 16 April 2020
/
Published: 21 April 2020
(This article belongs to the Special Issue Applications of Artificial Intelligence in Renewable Energy)
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.
View Full-Text
Keywords:
data-based modeling; data-driven modeling; least-squares regression; linear regression; clustering; simulated annealing; nonlinear optimization; self-consumption optimization; energy management
▼
Show Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Lachmann, M.; Maldonado, J.; Bergmann, W.; Jung, F.; Weber, M.; Büskens, C. Self-Learning Data-Based Models as Basis of a Universally Applicable Energy Management System. Energies 2020, 13, 2084. https://doi.org/10.3390/en13082084
AMA Style
Lachmann M, Maldonado J, Bergmann W, Jung F, Weber M, Büskens C. Self-Learning Data-Based Models as Basis of a Universally Applicable Energy Management System. Energies. 2020; 13(8):2084. https://doi.org/10.3390/en13082084
Chicago/Turabian StyleLachmann, Malin, Jaime Maldonado, Wiebke Bergmann, Francesca Jung, Markus Weber, and Christof Büskens. 2020. "Self-Learning Data-Based Models as Basis of a Universally Applicable Energy Management System" Energies 13, no. 8: 2084. https://doi.org/10.3390/en13082084
Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
