Open Energy Services -- Forecasting and Optimization as a Service for Energy Management Applications at Scale
<|reference_start|>Open Energy Services -- Forecasting and Optimization as a Service for Energy Management Applications at Scale: Energy management, in sense of computing optimized operation schedules for devices, will likely play a vital role in future carbon neutral energy systems, as it allows unlocking energy efficiency and flexibility potentials. However, energy management systems need to be applied at large scales to realize the desired effect, which clearly requires minimization of costs for setup and operation of the individual applications. In order to push the latter forward, we promote an approach to split the complex optimization algorithms employed by energy management systems into standardized components, which can be provided as a service with marginal costs at scale. This work is centered around the systematic design of a framework supporting the efficient implementation and operation of such forecasting and optimization services. Furthermore, it describes the implementation of the design concept which we release under the name \emph{Energy Service Generics} as a free and open source repository. Finally, this paper marks the starting point of the \emph{Open Energy Services} community, our effort to continuously push the development and operation of services for energy management applications at scale, for which we invite researchers and practitioners to participate.<|reference_end|>
@article{wölfle2024open,
    title={Open Energy Services -- Forecasting and Optimization as a Service for
  Energy Management Applications at Scale},
    author={David W"olfle, Kevin F"orderer, Tobias Riedel, Lukas Landwich, Ralf
  Mikut, Veit Hagenmeyer, Hartmut Schmeck},
    journal={arXiv preprint arXiv:2402.15230},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.15230},
    primaryClass={cs.SE}
}