Lars Quakernack
12 Publikationen
2024 | Konferenzbeitrag | FH-PUB-ID: 5720
Quakernack, L., Engelmann, T., Haubrock, J., & Vaquet, V. (2024). LSTM Autoencoder Model to Identify Electric Vehicles in Grouped Smart Meter Data. In IEEE (Ed.), 2024 IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE) (pp. 1–5). Dubrovnik, Croatia: IEEE. https://doi.org/10.1109/ISGTEUROPE62998.2024.10863725
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2024 | Konferenzbeitrag | FH-PUB-ID: 5761
Quakernack, L., Gurcke, M., Schulte, K., & Haubrock, J. (2024). Grid-Oriented Control of Vehicle Batteries in a Cellular Grid Setup Based on Fuzzy Logic. In IEEE (Ed.), 2024 IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE) (pp. 1–5). Dubrovnik, Croatia: IEEE. https://doi.org/10.1109/ISGTEUROPE62998.2024.10863623
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2024 | Konferenzbeitrag | FH-PUB-ID: 5557
Schulte, K., Engel, L., Quakernack, L., Liegmann, F., & Haubrock, J. (2024). A comparison of machine learning algorithms for the optimization of a day-ahead photovoltaic power forecast. In N. Holjevac, T. Baškarad, M. Zidar, & I. Kuzle (Eds.), IEEE PES ISGT Europe 2024 Conference book. Dubrovnik, Croatia.
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2023 | Konferenzbeitrag | FH-PUB-ID: 5723
Engelmann, T., Quakernack, L., & Haubrock, J. (2023). Q-learning based control algorithm with dynamic combination of peak shaving and self-consumption optimization for industrial battery storage systems. In IEEE (Ed.), PESS 2023; Power and Energy Student Summit. Bielefeld.
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2023 | Konferenzbeitrag | FH-PUB-ID: 4357
Schulte, K., Engel, L., Quakernack, L., & Haubrock, J. (2023). Optimized photovoltaic power forecast using k-means clustering based error reduction. In 2023 IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE) (pp. 1–5). Grenoble, France: IEEE. https://doi.org/10.1109/ISGTEUROPE56780.2023.10407521
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2023 | Konferenzbeitrag | FH-PUB-ID: 3105
Quakernack, L., Hövelmann, J., Kröger, K., & Haubrock, J. (2023). AI-based heat demand forecasting in industrial buildings for flexible operation of combined heat and power plants. Presented at the International ETG-Congress 2023, Magdeburg, Germany.
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2023 | Konferenzbeitrag | FH-PUB-ID: 3096
Steinhagen, B., Jungh, T., Hesse, M., Rückert, U., Quakernack, L., Kelker, M., & Haubrock, J. (2023). Evaluation of the Usage of Edge Computing and LoRa for the Control of Electric Vehicle Charging in the Low Voltage Grid. In 2023 IEEE PES Conference on Innovative Smart Grid Technologies - Middle East (ISGT Middle East) (pp. 1–5). Abu Dhabi, United Arab Emirates: IEEE. https://doi.org/10.1109/ISGTMiddleEast56437.2023.10078593
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2022 | Konferenzbeitrag | FH-PUB-ID: 3113
Quakernack, L., Kelker, M., & Haubrock, J. (2022). Deep Reinforcement Learning For Autonomous Control Of Low Voltage Grids With Focus On Grid Stability In Future Power Grids. In 2022 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe) (pp. 1–5). Novi Sad, Serbia: IEEE. https://doi.org/10.1109/ISGT-Europe54678.2022.9960416
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2022 | Konferenzbeitrag | FH-PUB-ID: 3112
Kelker, M., Quakernack, L., Haubrock, J., & Westermann, D. (2022). Multi agent double deep Q-network with multiple reward functions for electric vehicle charge control. In 2022 IEEE Power & Energy Society General Meeting (PESGM) (pp. 01–05). Denver, CO, USA: IEEE. https://doi.org/10.1109/PESGM48719.2022.9917038
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| DOI
2022 | Konferenzbeitrag | FH-PUB-ID: 3097 | 
Quakernack, L., Kelker, M., Rückert, U., & Haubrock, J. (2022). Deep Reinforcement Learning als Methode zur autonomen Steuerung von Niederspannungsnetzen mit Fokus auf die Netzstabilität. Presented at the 17. Symposium Energieinnovation, Graz.
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2021 | Konferenzbeitrag | FH-PUB-ID: 3356
Kelker, M., Quakernack, L., & Haubrock, J. (2021). Multi agent deep Q-reinforcement learning for autonomous low voltage grid control. In 2021 IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe) (pp. 1–6). Espoo, Finland: IEEE. https://doi.org/10.1109/ISGTEurope52324.2021.9639897
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| DOI
2018 | Konferenzbeitrag | FH-PUB-ID: 3360
Quakernack, L., Kelker, M., & Haubrock, J. (2018). Simulation of a Smart-Micro-Grid to Analyse an Intelligent Charge Management in Matlab Simulink for a Local Bakery Chain. Presented at the 8. IEEE Germany Student Conference, Magdeburg.
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12 Publikationen
2024 | Konferenzbeitrag | FH-PUB-ID: 5720
Quakernack, L., Engelmann, T., Haubrock, J., & Vaquet, V. (2024). LSTM Autoencoder Model to Identify Electric Vehicles in Grouped Smart Meter Data. In IEEE (Ed.), 2024 IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE) (pp. 1–5). Dubrovnik, Croatia: IEEE. https://doi.org/10.1109/ISGTEUROPE62998.2024.10863725
HSBI-PUB
| DOI
2024 | Konferenzbeitrag | FH-PUB-ID: 5761
Quakernack, L., Gurcke, M., Schulte, K., & Haubrock, J. (2024). Grid-Oriented Control of Vehicle Batteries in a Cellular Grid Setup Based on Fuzzy Logic. In IEEE (Ed.), 2024 IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE) (pp. 1–5). Dubrovnik, Croatia: IEEE. https://doi.org/10.1109/ISGTEUROPE62998.2024.10863623
HSBI-PUB
| DOI
2024 | Konferenzbeitrag | FH-PUB-ID: 5557
Schulte, K., Engel, L., Quakernack, L., Liegmann, F., & Haubrock, J. (2024). A comparison of machine learning algorithms for the optimization of a day-ahead photovoltaic power forecast. In N. Holjevac, T. Baškarad, M. Zidar, & I. Kuzle (Eds.), IEEE PES ISGT Europe 2024 Conference book. Dubrovnik, Croatia.
HSBI-PUB
2023 | Konferenzbeitrag | FH-PUB-ID: 5723
Engelmann, T., Quakernack, L., & Haubrock, J. (2023). Q-learning based control algorithm with dynamic combination of peak shaving and self-consumption optimization for industrial battery storage systems. In IEEE (Ed.), PESS 2023; Power and Energy Student Summit. Bielefeld.
HSBI-PUB
| Download (ext.)
2023 | Konferenzbeitrag | FH-PUB-ID: 4357
Schulte, K., Engel, L., Quakernack, L., & Haubrock, J. (2023). Optimized photovoltaic power forecast using k-means clustering based error reduction. In 2023 IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE) (pp. 1–5). Grenoble, France: IEEE. https://doi.org/10.1109/ISGTEUROPE56780.2023.10407521
HSBI-PUB
| DOI
2023 | Konferenzbeitrag | FH-PUB-ID: 3105
Quakernack, L., Hövelmann, J., Kröger, K., & Haubrock, J. (2023). AI-based heat demand forecasting in industrial buildings for flexible operation of combined heat and power plants. Presented at the International ETG-Congress 2023, Magdeburg, Germany.
HSBI-PUB
2023 | Konferenzbeitrag | FH-PUB-ID: 3096
Steinhagen, B., Jungh, T., Hesse, M., Rückert, U., Quakernack, L., Kelker, M., & Haubrock, J. (2023). Evaluation of the Usage of Edge Computing and LoRa for the Control of Electric Vehicle Charging in the Low Voltage Grid. In 2023 IEEE PES Conference on Innovative Smart Grid Technologies - Middle East (ISGT Middle East) (pp. 1–5). Abu Dhabi, United Arab Emirates: IEEE. https://doi.org/10.1109/ISGTMiddleEast56437.2023.10078593
HSBI-PUB
| DOI
2022 | Konferenzbeitrag | FH-PUB-ID: 3113
Quakernack, L., Kelker, M., & Haubrock, J. (2022). Deep Reinforcement Learning For Autonomous Control Of Low Voltage Grids With Focus On Grid Stability In Future Power Grids. In 2022 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe) (pp. 1–5). Novi Sad, Serbia: IEEE. https://doi.org/10.1109/ISGT-Europe54678.2022.9960416
HSBI-PUB
| DOI
2022 | Konferenzbeitrag | FH-PUB-ID: 3112
Kelker, M., Quakernack, L., Haubrock, J., & Westermann, D. (2022). Multi agent double deep Q-network with multiple reward functions for electric vehicle charge control. In 2022 IEEE Power & Energy Society General Meeting (PESGM) (pp. 01–05). Denver, CO, USA: IEEE. https://doi.org/10.1109/PESGM48719.2022.9917038
HSBI-PUB
| DOI
2022 | Konferenzbeitrag | FH-PUB-ID: 3097 |
Quakernack, L., Kelker, M., Rückert, U., & Haubrock, J. (2022). Deep Reinforcement Learning als Methode zur autonomen Steuerung von Niederspannungsnetzen mit Fokus auf die Netzstabilität. Presented at the 17. Symposium Energieinnovation, Graz.
HSBI-PUB
| Download (ext.)
2021 | Konferenzbeitrag | FH-PUB-ID: 3356
Kelker, M., Quakernack, L., & Haubrock, J. (2021). Multi agent deep Q-reinforcement learning for autonomous low voltage grid control. In 2021 IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe) (pp. 1–6). Espoo, Finland: IEEE. https://doi.org/10.1109/ISGTEurope52324.2021.9639897
HSBI-PUB
| DOI
2018 | Konferenzbeitrag | FH-PUB-ID: 3360
Quakernack, L., Kelker, M., & Haubrock, J. (2018). Simulation of a Smart-Micro-Grid to Analyse an Intelligent Charge Management in Matlab Simulink for a Local Bakery Chain. Presented at the 8. IEEE Germany Student Conference, Magdeburg.
HSBI-PUB