Abstract
An increased use of renewable energy could significantly reduce greenhouse gas emissions but is difficult to realize since most renewable energy sources underlie volatile availability. Making use of storage devices and scheduling consumers to times when energy is available allows to increase the amount of renewable energy that can be used. For this purpose, adequate models for forecasting the energy generated and consumed as well as for the behavior of storage devices are essential. Many data-based modeling approaches are computationally costly and therefore difficult to apply in real-world systems. Hence we present a computationally efficient modeling approach using a least-squares regression. Besides, we propose to use a hybrid model approach and evaluate it on real-world data at the examples of modeling the state of charge of a battery storage and the temperature inside a milk cooling tank. The experiments indicate that the hybrid approach leads to better forecasting results, especially for modeling more complicated behavior. Also, it is investigated if the behavior of the models is qualitatively realistic and we find that the battery model fulfills this requirement and is thus suitable for the application in a smart energy management system. Even though forecasts for the hybrid milk cooling model have even lower error values than the ones for the battery storage, further steps need to be taken to avoid undesired effects when using this model in such a sophisticated system.
This research is based on a project funded by the Federal Ministry for Economic Affairs and Energy of Germany (project title SmartFarm, project number 0325927).
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Lachmann, M., Büskens, C. (2021). A Hybrid Approach for Data-Based Models Using a Least-Squares Regression. In: Dorronsoro, B., Amodeo, L., Pavone, M., Ruiz, P. (eds) Optimization and Learning. OLA 2021. Communications in Computer and Information Science, vol 1443. Springer, Cham. https://doi.org/10.1007/978-3-030-85672-4_5
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