Abstract
The German energy transition confronts the operators of low-voltage grids with new challenges. Local energy producers or large consumers, like, e.g., solar panels, heat pumps, and e-mobility lead to unexpected grid behavior. Because current grids are only sparsely monitored, local unmonitored overloads or violations of the voltage range are possible. To overcome these difficulties a smart monitoring and prediction system is needed. The system must handle different data sources fast and efficiently, so the operators can react to local grid problems. This is solved by using a streaming service to aggregate the data efficiently. Then, the implemented data pipeline is used to train AI-based models to interpolate the unmeasured parts of the grid. These models consider both measured data and predictions, like load profiles and photovoltaic forecasts. Since the grid is not fully observed, a data generator that physically simulates detailed grid scenarios is used to generate large sets of training data. Finally, an interactive GUI is implemented to visualize the data monitoring and predictions in the context of the grid and thus strengthen the user’s trust in the system. The presented assistance system is developed in close cooperation with energy experts and grid operators.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Primadianto, A., Lu, C.N.: A review on distribution system state estimation. IEEE Trans. Power Syst. 32(5), 3875–3883 (2017)
Winter, A., et al.: Künstliche Intelligenz in Stromverteilnetzen – KI-basierte Systemanalyse im Normal- und Kurzschlussbetrieb. In: ew - Magazin für die Energiewirtschaft, pp. 32–35. VDE (2021)
FITT - Institut für Technologietransfer an der Hochschule des Saarlandes gGmbH: ATPDesigner Design and Simulation of Electrical Power Networks (2022). http://www.atpdesigner.de/. Accessed 17 Mar 2023
Leuven EMTP Center: Alternative Transients Program (ATP): Rule Book. EMTP (1992)
European EMTP-ATP Users Group e.V. (2022). https://www.eeug.org/. Accessed 17 Mar 2023
Winter, A., Igel, M., Schegner, P.: Application of artificial intelligence in power grid state analysis and-diagnosis. In: NEIS 2020; Conference on Sustainable Energy Supply and Energy Storage Systems, pp. 1–6. VDE (2020)
Deru, M., Ndiaye, A.: Deep Learning mit TensorFlow, Keras und TensorFlow.js, 2nd edn. Rheinwerke Computing, Bonn (2022)
Zamzam, A.S., Sidiropoulos, N.D.: Physics-aware neural networks for distribution system state estimation. IEEE Trans. Power Syst. 35(6), 4347–4356 (2020)
Stüber, M., et al.: Forecast quality of physics-based and data-driven PV performance models for a small-scale PV system. Front. Energy Res. 9 (2021)
Brandherm, B., Deru, M., Ndiaye, A., Kiefer, G.-L., Baus, J., Gampfer, R.: Integration erneuerbarer Energien – KI-basierte Vorhersageverfahren zur Stromerzeugung durch Photovoltaikanlagen. In: Barton, T., Müller, C. (eds.) Data Science anwenden. AW, pp. 147–170. Springer, Wiesbaden (2021). https://doi.org/10.1007/978-3-658-33813-8_9
Khan, S., Brandherm, B., Swamy, A.: Electric vehicle user behavior prediction using learning-based approaches. In: 2020 IEEE Electric Power and Energy Conference (EPEC), pp. 1–5 (2020)
Apache Software Foundation: Documentation Kafka 3.3 (2022). https://kafka.apache.org/documentation/. Accessed 17 Mar 2023
Chikobava, M., et al.: Multimodal interactive system for visualization of energy data in extended reality settings. In: HCI International 2023. Springer, Cham (2023)
Acknowledgements
This work was supported by the Project “GridAnalysis” funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWK) under the grant numbers 03EI6034A and 03EI6034D. The authors kindly thank project partners Stadtwerke Saarlouis GmbH and VSE AG for their valuable support and discussions.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Schmeyer, T.A. et al. (2023). Assistance System for AI-Based Monitoring and Prediction in Smart Grids. In: Stephanidis, C., Antona, M., Ntoa, S., Salvendy, G. (eds) HCI International 2023 Posters. HCII 2023. Communications in Computer and Information Science, vol 1835. Springer, Cham. https://doi.org/10.1007/978-3-031-36001-5_65
Download citation
DOI: https://doi.org/10.1007/978-3-031-36001-5_65
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-36000-8
Online ISBN: 978-3-031-36001-5
eBook Packages: Computer ScienceComputer Science (R0)