@article{loeser2022vision,
  abstract = {Due to the ongoing trend towards a decarbonisation of energy use, the power system is expected to become the backbone of all energy sectors and thus the fundamental critical infrastructure. High penetration with distributed energy resources demands the coordination of a large number of prosumers, partly controlled by home energy management systems (HEMS), to be designed in such a way that the power system’s operational limits are not violated. On the grid level, distribution management systems (DMS) seek to keep the power system in the normal operational state. On the prosumer level, distributed HEMS optimise the internal power flows by setpoint specification of batteries, photovoltaic generators, or flexible loads. The vision of the ODiS (Organic Distribution System) initiative is to develop an architecture to operate a distribution grid reliably, with high resiliency, and fully autonomously by developing “organic” HEMS and DMS which possess multiple self-x capabilities, collectively referred to as self-management. Thus, ODiS seeks answers to the following question: How can we create the most appropriate models, techniques, and algorithms to develop novel kinds of self-configuring, self-organising, self-healing, and self-optimising DMS that are integrally coupled with the distributed HEMS? In this concept paper, the vision of ODiS is presented in detail based on a thorough review of the state of the art.},
  author = {Loeser, Inga and Braun, Martin and Gruhl, Christian and Menke, Jan-Hendrik and Sick, Bernhard and Tomforde, Sven},
  doi = {10.3390/en15030881},
  interhash = {e50a289dec013d0734c525b6b1202f7c},
  intrahash = {59ad7fc64fe334ebd891c97911a9b687},
  journal = {Energies},
  number = 3,
  pages = 881,
  publisher = {MDPI},
  title = {The Vision of Self-Management in Cognitive Organic Power Distribution Systems},
  url = {https://www.mdpi.com/1996-1073/15/3/881},
  volume = 15,
  year = 2022
}

@inproceedings{rauch2023active,
  abstract = {We propose a shift towards end-to-end learning in bird sound monitoring by combining self-supervised (SSL) and deep active learning (DAL). Leveraging transformer models, we aim to bypass traditional spectrogram conversions, enabling direct raw audio processing. ACTIVE BIRD2VEC is set to generate high-quality bird sound representations through SSL, potentially accelerating the assessment of environmental changes and decision-making processes for wind farms. Additionally, we seek to utilize the wide variety of bird vocalizations through DAL, reducing the reliance on extensively labeled datasets by human experts. We plan to curate a comprehensive set of tasks through Huggingface Datasets, enhancing future comparability and reproducibility of bioacoustic research. A comparative analysis between various transformer models will be conducted to evaluate their proficiency in bird sound recognition tasks. We aim to accelerate the progression of avian bioacoustic research and contribute to more effective conservation strategies.},
  author = {Rauch, Lukas and Schwinger, Raphael and Wirth, Moritz and Sick, Bernhard and Tomforde, Sven and Scholz, Christoph},
  booktitle = {Workshop on Artificial Intelligence for Sustainability (AI4S), ECAI},
  interhash = {fa669d70ebe82d3d149ddc85adc736a9},
  intrahash = {642c133c4f7cca48aa218da212f69f64},
  pages = {1--6},
  title = {Active Bird2Vec: Towards End-To-End Bird Sound Monitoring with Transformers},
  url = {https://arxiv.org/abs/2308.07121},
  year = 2023
}

@article{lehna2023managing,
  abstract = {The operation of electricity grids has become increasingly complex due to the current upheaval and the increase in renewable energy production. As a consequence, active grid management is reaching its limits with conventional approaches. In the context of the Learning to Run a Power Network (L2RPN) challenge, it has been shown that Reinforcement Learning (RL) is an efficient and reliable approach with considerable potential for automatic grid operation. In this article, we analyse the submitted agent from Binbinchen and provide novel strategies to improve the agent, both for the RL and the rule-based approach. The main improvement is a N-1 strategy, where we consider topology actions that keep the grid stable, even if one line is disconnected. More, we also propose a topology reversion to the original grid, which proved to be beneficial. The improvements are tested against reference approaches on the challenge test sets and are able to increase the performance of the rule-based agent by 27%. In direct comparison between rule-based and RL agent we find similar performance. However, the RL agent has a clear computational advantage. We also analyse the behaviour in an exemplary case in more detail to provide additional insights. Here, we observe that through the N-1 strategy, the actions of both the rule-based and the RL agent become more diversified.},
  author = {Lehna, Malte and Viebahn, Jan and Marot, Antoine and Tomforde, Sven and Scholz, Christoph},
  codeurl = {https://github.com/FraunhoferIEE/CurriculumAgent},
  doi = {10.1016/j.egyai.2023.100276},
  interhash = {1eb2a95c3cc50ac7aa87f74f078f7a28},
  intrahash = {e8949e7700da1eb974fe51c94877666e},
  journal = {Energy and AI},
  pages = 100276,
  publisher = {Energy and AI},
  title = {Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agents},
  url = {https://www.sciencedirect.com/science/article/pii/S2666546823000484?via%3Dihub},
  volume = 14,
  year = 2023
}

@inproceedings{gruhl2022self,
  abstract = {Besides infrastructure-based solutions, small-scale
microsystems are increasingly connected and interoperating with
others in changing environments. Following the same motivation
as for self-adaptive systems, this demands novel attempts to
counter complexity and provide autonomous decision freedom
that paves the path towards resilient and flexible behaviour.
In this article, we outline a vision for research on self-aware
microsystems (SAM) that aims at providing technical solutions
that are especially relevant for initiatives such as self-improving
system integration.},
  author = {Gruhl, Christian and Tomforde, Sven and Sick, Bernhard},
  booktitle = {Workshop on Self-Improving System Integration (SISSY), ACSOS},
  doi = {10.1109/ACSOSC56246.2022.00045},
  interhash = {69b2a4df1c13ba8feec866d9e41a51bc},
  intrahash = {02014fbae89c1394970a5b3f32ff1bb6},
  pages = {126--127},
  publisher = {IEEE},
  title = {Self-Aware Microsystems},
  url = {https://ieeexplore.ieee.org/abstract/document/9934876/},
  year = 2022
}

@article{krupitzer2022proactive,
  abstract = { Context:

Smart and adaptive Systems, such as self-adaptive and self-organising (SASO) systems, typically consist of a large set of highly autonomous and heterogeneous subsystems that are able to adapt their behaviour to the requirements of ever-changing, dynamic environments. Their successful operation is based on appropriate modelling of the internal and external conditions.
Objective:

The control problem for establishing a near-to-optimal coordinated behaviour of systems with multiple, potentially conflicting objectives is either approached in a distributed (i.e., fully autonomous by the autonomous subsystems) or in a centralised way (i.e. one instance controlling the optimisation and planning process). In the distributed approach, selfish behaviour and being limited to local knowledge may lead to sub-optimal system behaviour, while the centralised approach ignores the autonomy and the coordination efforts of parts of the system.
Method:

In this article, we present a concept for a hybrid (i.e., integrating a central optimisation with a distributed decision-making process) system management that combines local reinforcement learning and self-awareness mechanisms of fully autonomous subsystems with external system-wide planning and optimisation of adaptation freedom that steers the behaviour dynamically by issuing plans and guidelines augmented with incentivisation schemes.
Results:

This work addresses the inherent uncertainty of the dynamic system behaviour, the local autonomous and context-aware learning of subsystems, and proactive control based on adaptiveness. We provide the ‘swarm-fleet infrastructure’—a self-organised taxi service established by autonomous, privately-owned cars—as a testbed for structured comparison of systems.
Conclusion:

The ‘swarm-fleet infrastructure’ supports the advantages of a proactive hybrid self-adaptive and self-organising system operation. Further, we provide a system model to combine the system-wide optimisation while ensuring local decision-making through reinforcement learning for individualised configurations.},
  author = {Krupitzer, Christian and Gruhl, Christian and Sick, Bernhard and Tomforde, Sven},
  doi = {10.1016/j.infsof.2022.106826},
  interhash = {36854b5fe7e895d3a4ccc4fe11a3fc84},
  intrahash = {b34f9014cc2a9f791aef2e40a6f43dd8},
  journal = {Information and Software Technology},
  pages = 106826,
  publisher = {Elsevier},
  title = {Proactive hybrid learning and optimisation in self-adaptive systems: The swarm-fleet infrastructure scenario},
  url = {https://www.sciencedirect.com/science/article/abs/pii/S0950584922000052},
  volume = 145,
  year = 2022
}

@inproceedings{GT21,
  author = {Gruhl, Christian and Tomforde, Sven},
  booktitle = {Workshop on Self-Improving System Integration (SISSY), ACSOS},
  interhash = {d467f23181a913b8d53d37f5e97a7e3f},
  intrahash = {74d1f769c1a0b9e48130c449de32c38e},
  note = {(accepted)},
  title = {OHODIN -- Online Anomaly Detection for Data Streams},
  year = 2021
}

@inproceedings{AGT21,
  author = {Al-Falouji, Ghassan and Gruhl, Christian and Tomforde, Sven},
  booktitle = {Workshop on Self-Improving System Integration (SISSY), ACSOS},
  interhash = {b8f913ba2cc28e4f102233d1a7dee95c},
  intrahash = {490f5c439a2529e1a776d7703ff49393},
  note = {(accepted)},
  title = {Digital Shadows in SISSY Systems: A Concept Using Generative Modelling},
  year = 2021
}