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

Energies

Wiebke Bergmann¹  Christof Büskens¹  Markus Weber¹  Malin Lachmann¹  Francesca Jung¹  Jaime Maldonado² 

[1] Center for Industrial Mathematics, University of Bremen, Bibliothekstr. 5, 28359 Bremen, Germany;Cognitive Neuroinformatics, University of Bremen, Enrique-Schmidt-Straße 5, 28359 Bremen, Germany;

关键词: data-based modeling;    data-driven modeling;    least-squares regression;    linear regression;    clustering;    simulated annealing;   

DOI  :  10.3390/en13082084

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown