Publications

Dynamic Phenotype Mapping in Evolutionary Algorithms for Energy Hub Scheduling
Poppenborg, R.; Phipps, K.; Beichter, M.; Förderer, K.; Mikut, R.; Hagenmeyer, V.
2025. Energy Informatics – 4th Energy Informatics Academy Conference, EI.A 2024, Kuta, Bali, Indonesia, October 23–25, 2024, Proceedings, Part II. Ed.: B. Jørgensen, 205–223, Springer Nature Switzerland. doi:10.1007/978-3-031-74741-0_14

Synthesizing Distribution Grid Congestion Data Using Multivariate Conditional Time Series Generative Adversarial Networks
Demirel, G.; Hauf, J.; Butt, H.; Förderer, K.; Schäfer, B.; Hagenmeyer, V.
2025. 2024 IEEE Sustainable Power and Energy Conference (iSPEC), 385–390, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/iSPEC59716.2024.10892479

PIDE: Photovoltaic integration dynamics and efficiency for autonomous control on power distribution grids
Demirel, G.; Fernengel, N.; Grafenhorst, S.; Förderer, K.; Hagenmeyer, V.
2025. Energy Informatics, 8 (1), 32. doi:10.1186/s42162-025-00489-6

Impact and Integration of Mini Photovoltaic Systems on Electric Power Distribution Grids
Demirel, G.; Grafenhorst, S.; Förderer, K.; Hagenmeyer, V.
2025. Energy Informatics – 4th Energy Informatics Academy Conference, EI.A 2024, Kuta, Bali, Indonesia, October 23–25, 2024, Proceedings, Part II. Ed.: B. Jørgensen, 247–265, Springer Nature Switzerland. doi:10.1007/978-3-031-74741-0_16

Grid Aware Portfolio Optimization of a Multi-Energy DER
Grafenhorst, S.; Demirel, G.; Förderer, K.; Hagenmeyer, V.
2024. 2024 IEEE Sustainable Power and Energy Conference (iSPEC), Kuching, Sarawak, 24th-27th November 2024, 245–250, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/iSPEC59716.2024.10892444

Automation Level Taxonomy for Time Series Forecasting Services: Guideline for Real-World Smart Grid Applications
Meisenbacher, S.; Galenzowski, J.; Förderer, K.; Suess, W.; Waczowicz, S.; Mikut, R.; Hagenmeyer, V.
2024. Energy Informatics – 4th Energy Informatics Academy Conference, EI.A 2024, Kuta, Bali, Indonesia, October 23–25, 2024, Proceedings, Part I. Ed.: B. Jørgensen, 277–297, Springer Nature Switzerland. doi:10.1007/978-3-031-74738-0_18

A Comparative Analysis of Machine Learning Algorithms for Aggregated Electric Chargepoint Load Forecasting
Li, C.; Zhang, M.; Förderer, K.; Matthes, J.; Hagenmeyer, V.
2024. 2024 9th International Conference on Sustainable and Renewable Energy Engineering (ICSREE 2024), Marseille, May 9th-11th 2024, Ed.: J. Lin, Art.-Nr.: 01004, EDP Sciences. doi:10.1051/e3sconf/202454501004

Impact and Integration of Mini Photovoltaic Systems on Electric Power Distribution Grids
Demirel, G.; Grafenhorst, S.; Förderer, K.; Hagenmeyer, V.
2024. arxiv

Distribution grid monitoring based on feature propagation using smart plugs
Grafenhorst, S.; Förderer, K.; Hagenmeyer, V.
2024. Energy Informatics, 7 (1), Article no: 116. doi:10.1186/s42162-024-00427-y

Heuristic vs. Analytical Energy Hub Optimization: Design, Implementation, and Trade-Offs
Grafenhorst, S.; Ohm, J.; Poppenborg, R.; Förderer, K.; Hagenmeyer, V.
2024. 2024 IEEE 12th International Conference on Smart Energy Grid Engineering (SEGE), Oshawa, ON, Canada, 18-20 August 2024, 145–150, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/SEGE62220.2024.10739589

Gossen’s first law in the modeling for demand side management: a thorough heat pump case study with deep learning based partial time series data generation
Li, C.; Brecher, G.; Kovačević, J.; Çakmak, H. K.; Förderer, K.; Matthes, J.; Hagenmeyer, V.
2024. Energy Informatics, 7, 47. doi:10.1186/s42162-024-00353-z

Improving Reduced-Order Building Modeling: Integration of Occupant Patterns for Reducing Energy Consumption
Kovačević, J.; Li, C.; Förderer, K.; Çakmak, H. K.; Hagenmeyer, V.
2024. Proceedings of the 15th ACM International Conference on Future and Sustainable Energy Systems, 15th ACM International Conference on Future and Sustainable Energy Systems (2024), Singapur, Singapur, 04.06.2024–07.06.2024, 569–579, Association for Computing Machinery (ACM). doi:10.1145/3632775.3662163

Distribution Grid Monitoring Based on Widely Available Smart Plugs
Grafenhorst, S.; Förderer, K.; Hagenmeyer, V.
2024. Energy Informatics – Third Energy Informatics Academy Conference, EI.A 2023, Campinas, Brazil, December 6–8, 2023, Proceedings, Part II. Ed.: B. Jørgensen, 195–212, Springer Nature Switzerland. doi:10.1007/978-3-031-48652-4_13

Gossen’s First Law in the Modeling for Demand Side Management: A First Heat Pump Case Study
Li, C.; Förderer, K.; Moser, T.; Spatafora, L.; Hagenmeyer, V.
2024. Energy Informatics. Hrsg.: B.N. Jørgensen. Pt. 2, 111–125, Springer Nature Switzerland. doi:10.1007/978-3-031-48652-4_8

Transformationsprozess für die kommunale Energiewende sektorengekoppelte Infrastrukturen und Strategien zur Einbindung von lokalen Akteuren (TrafoKommunE) : IKT-Konzept
Grafenhorst, S.; Förderer, K.; Frankenhoff, F.; Müntefering, T.; Benthin, J.; Albus, R.
2023. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000165178

Dynamic Mapping for Evolutionary Algorithm Based Optimization of Energy Hub Gas Scheduling
Poppenborg, R.; Beisswanger, K.; Hotz, C.; Förderer, K.; Kolb, T.; Hagenmeyer, V.
2023. 2023 IEEE 11th International Conference on Smart Energy Grid Engineering (SEGE), 206–211, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/SEGE59172.2023.10274571

Dynamic Mapping for Evolutionary Algorithm Based Optimization of Energy Hub Gas Scheduling
Poppenborg, R.
2023, August 13. 11th IEEE International Conference on Smart Energy Grid Engineering (SEGE 2023), Oshawa, Canada, August 13–15, 2023

Dynamic Chromosome Interpretation in Evolutionary Algorithms for Distributed Energy Resources Scheduling
Poppenborg, R.; Phipps, K.; Khalloof, H.; Förderer, K.; Mikut, R.; Hagenmeyer, V.
2023. GECCO ’23 Companion: Companion Conference on Genetic and Evolutionary Computation Lisbon Portugal July 15 - 19, 2023. Ed.: S. Silva, 755–758, Association for Computing Machinery (ACM). doi:10.1145/3583133.3590666

The Kopernikus ENSURE Co-Demonstration Platform
Mehlmann, G.; Kühnapfel, U.; Wege, F.; Winkens, A.; Scheibe, C.; Vogel, S.; Noglik, P.; Gratza, M.; Richter, J.; Weber, M.; Burlakin, I.; Kuri, A.; Wagner, T.; Hubschneider, S.; Poppenborg, R.; Heins, T.; Förderer, K. M.; Ulbig, A.; Monti, A.; Hagenmeyer, V.; Luther, M.
2023. IEEE Open Journal of Power Electronics, 4, 987–1002. doi:10.1109/OJPEL.2023.3332515

Poster Abstract: A three-phase electricity grid model of a single family house
Grafenhorst, S.; Förderer, K.; Hagenmeyer, V.
2023. 12th DACH+ Conference on Energy Informatics (2023), Vienna, Austria, October 4–6, 2023

Abstracts of the 12th DACH+ Conference on Energy Informatics 2023
Grafenhorst, S.; Förderer, K.; Hagenmeyer, V.
2023. Energy Informatics, 6 (S2). doi:10.1186/s42162-023-00272-5

An adapter-based architecture for evaluating candidate solutions in energy system scheduling
Chlosta, M.; Liu, J.; Poppenborg, R.; Lutz, R.; Förderer, K.; Schlachter, T.; Hagenmeyer, V.
2022. Energy Informatics, 5 (S4), 56. doi:10.1186/s42162-022-00246-z

Fast Grid State Estimation for Power Networks: An Ensemble Machine Learning Approach
Shahoud, S.; Khalloof, H.; Khalouf, R.; Düpmeier, C.; Cakmak, H. K.; Forderer, K.; Hagenmeyer, V.
2022. 2022 IEEE 10th International Conference on Smart Energy Grid Engineering (SEGE), 12–18, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/SEGE55279.2022.9889763

Facilitating the hybridization of parallel evolutionary algorithms in cluster computing environments
Khalloof, H.; Ciftci, S.; Shahoud, S.; Duepmeier, C.; Foerderer, K.; Hagenmeyer, V.
2022. GECCO ’22: Proceedings of the Genetic and Evolutionary Computation Conference Companion. Ed.: J. E. Fieldsend, 2001–2008, Association for Computing Machinery (ACM). doi:10.1145/3520304.3533997

Automated generation of models for demand side flexibility using machine learning – an overview
Förderer, K.; Hagenmeyer, V.; Schmeck, H.
2021. ACM SIGEnergy Energy Informatics Review, 1 (1), 107–120. doi:10.1145/3508467.3508477

Modeling and Communicating Flexibility in Smart Grids Using Artificial Neural Networks as Surrogate Models - Supplementary Materials
Förderer, K.
2021, September 20. doi:10.5445/IR/1000137363

Modeling and Communicating Flexibility in Smart Grids Using Artificial Neural Networks as Surrogate Models. PhD dissertation
Förderer, K. M.
2021, September 1. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000136921

State-of-the-art integration of decentralized energy management systems into the German smart meter gateway infrastructure
Kroener, N.; Förderer, K.; Lösch, M.; Schmeck, H.
2020. Applied Sciences, 10 (11), Article No.3665. doi:10.3390/app10113665

A Concept for Standardized Benchmarks for the Evaluation of Control Strategies for Building Energy Management
Wölfle, D.; Förderer, K.; Schmeck, H.
2019. Energy informatics

State-based load profile generation for modeling energetic flexibility
Förderer, K.; Schmeck, H.
2019. Energy informatics, 2 (S1), 18. doi:10.1186/s42162-019-0077-z

Smart Meter Gateways: Options for a BSI-compliant integration of energy management systems
Förderer, K.; Lösch, M.; Növer, R.; Ronczka, M.; Schmeck, H.
2019. Applied Sciences, 9 (8), Art. Nr.: 1634. doi:10.3390/app9081634

Definition von Flexibilität in einem zellulär geprägten Energiesystem
Lehmann, N.; Kraft, E.; Duepmeier, C.; Mauser, I.; Förderer, K.; Sauer, D.
2019. Zukünftige Stromnetze 2019 : 30./31. Januar 2019 in Berlin, 459–469, Conexio

Modeling flexibility using artificial neural networks
Förderer, K.; Ahrens, M.; Bao, K.; Mauser, I.; Schmeck, H.
2018. Energy Informatics, S1 (21), 73–91. doi:10.1186/s42162-018-0024-4

Evaluation of power flow prognosis methods for congestion management in low voltage grids
Volk, K.; Lakenbrink, C.; Hatje, N.; Stolle, P.; Sivorotka, F.; Förderer, K.
2018. 8th Solar Integration Workshop : International Workshop on Integration of Solar Power into Power Systems : 16-17 October 2018, Stockholm, Sweden : digital proceedings / organizer: Energynautics ; edited by: Uta Betancourt, Thomas Ackermann, Energynautics GmbH

Towards the Modeling of Flexibility Using Artificial Neural Networks in Energy Management and Smart Grids: Note
Förderer, K.; Ahrens, M.; Bao, K.; Mauser, I.; Schmeck, H.
2018. Proceedings of the 9th International Conference on Future Energy Systems (ACM e-Energy 2018), Karlsruhe, 12.-15. Juni 2018, 85–90, Association for Computing Machinery (ACM). doi:10.1145/3208903.3208915

Definition, Modeling, and Communication of Flexibility in Smart Buildings and Smart Grid
Mauser, I.; Müller, J.; Förderer, K.; Schmeck, H.
2017. ETG Congress 2017 – Die Energiewende, Bonn, November 28 – 29, 2017, 605–610, VDE Verlag

Demo abstract : a building energy management system in the context of the smart grid traffic light concept
Förderer, K.; Schmeck, H.
2017. Computer science - research and development, 1–2. doi:10.1007/s00450-017-0376-1