Daniel Bayer - Lehrstuhl für Informatik III

Telefon	(0931) 31-89632
E-Mail	daniel.bayer@uni-wuerzburg.de
Raum	01.019
Anschrift	Lehrstuhl für Informatik III Am Hubland D-97074 Würzburg

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Forschungsinteressen

Daniel Bayer arbeitet als wissenschaftlicher Mitarbeiter bei der Professur für Informatik (Modellierung und Simulation). Er studierte zunächst Mathematik mit dem Abschluss Bachelor of Science, danach Informatik an der Friedrich-Alexander-Universität Erlangen-Nürnberg.
Seine Tätigkeit fokussiert sich auf die Bearbeitung des Forschungsprojektes von DigiSWM. Hierbei ist eines der zentralen Ziele, dass umfangreiche Energiedaten mit KI-Verfahren kombiniert werden, um neue Energiedienstleistungen zu simulieren und zu evaluieren.

Die Forschungsinteressen fokussieren sich ins Besondere auf folgende Themen:

Datengetriebene oder agentenbasierte Simulationen, vor allem im Energie- & Gebäudesektor
Digitale Zwillinge lokaler Energiesysteme
Datenanalyse von Zeitreihen
Multi-Agent Reinforcement Learning
Sustainable Computing

Forschungsprojekte

Neuste Veröffentlichungen

2023[ to top ]

A digital twin of a local energy system based on real smart meter data Bayer, Daniel; Pruckner, Marco in Energy Informatics (2023). 6(1)
- [ BibTeX ]
- [ EndNote ]
@article{2023_Bayer_DT_LocalEnergySystem, author = {Bayer, Daniel and Pruckner, Marco}, journal = {Energy Informatics}, keywords = {pruckner}, month = {mar}, number = 1, publisher = {Springer Science and Business Media {LLC}}, title = {A digital twin of a local energy system based on real smart meter data}, volume = 6, year = 2023 }

%0 Journal Article %1 2023_Bayer_DT_LocalEnergySystem %A Bayer, Daniel %A Pruckner, Marco %D 2023 %I Springer Science and Business Media {LLC} %J Energy Informatics %N 1 %R 10.1186/s42162-023-00263-6 %T A digital twin of a local energy system based on real smart meter data %V 6

2022[ to top ]

Enhancing the Performance of Multi-Agent Reinforcement Learning for Controlling HVAC Systems Bayer, Daniel; Pruckner, Marco in 2022 IEEE Conference on Technologies for Sustainability (SusTech) (2022). 187–194.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
- [ URL ]
Systems for heating, ventilation and air-conditioning (HVAC) of buildings are traditionally controlled by a rule-based approach. In order to reduce the energy consumption and the environmental impact of HVAC systems more advanced control methods such as reinforcement learning are promising. Reinforcement learning (RL) strategies offer a good alternative, as user feedback can be integrated more easily and presence can also be incorporated. Moreover, multi-agent RL approaches scale well and can be generalized. In this paper, we propose a multi-agent RL framework based on existing work that learns reducing on one hand energy consumption by optimizing HVAC control and on the other hand user feedback by occupants about uncomfortable room temperatures. Second, we show how to reduce training time required for proper RL-agent-training by using parameter sharing between the multiple agents and apply different pretraining techniques. Results show that our framework is capable of reducing the energy by around 6% when controlling a complete building or 8% for a single room zone. The occupants complaints are acceptable or even better compared to a rule-based baseline. Additionally, our performance analysis show that the training time can be drastically reduced by using parameter sharing.

@inproceedings{9794179, abstract = {Systems for heating, ventilation and air-conditioning (HVAC) of buildings are traditionally controlled by a rule-based approach. In order to reduce the energy consumption and the environmental impact of HVAC systems more advanced control methods such as reinforcement learning are promising. Reinforcement learning (RL) strategies offer a good alternative, as user feedback can be integrated more easily and presence can also be incorporated. Moreover, multi-agent RL approaches scale well and can be generalized. In this paper, we propose a multi-agent RL framework based on existing work that learns reducing on one hand energy consumption by optimizing HVAC control and on the other hand user feedback by occupants about uncomfortable room temperatures. Second, we show how to reduce training time required for proper RL-agent-training by using parameter sharing between the multiple agents and apply different pretraining techniques. Results show that our framework is capable of reducing the energy by around 6% when controlling a complete building or 8% for a single room zone. The occupants complaints are acceptable or even better compared to a rule-based baseline. Additionally, our performance analysis show that the training time can be drastically reduced by using parameter sharing.}, author = {Bayer, Daniel and Pruckner, Marco}, booktitle = {2022 IEEE Conference on Technologies for Sustainability (SusTech)}, keywords = {pruckner}, month = {April}, pages = {187-194}, title = {Enhancing the Performance of Multi-Agent Reinforcement Learning for Controlling HVAC Systems}, year = 2022 }

%0 Conference Paper %1 9794179 %A Bayer, Daniel %A Pruckner, Marco %B 2022 IEEE Conference on Technologies for Sustainability (SusTech) %D 2022 %P 187-194 %R 10.1109/SusTech53338.2022.9794179 %T Enhancing the Performance of Multi-Agent Reinforcement Learning for Controlling HVAC Systems %U https://ieeexplore.ieee.org/document/9794179/ %X Systems for heating, ventilation and air-conditioning (HVAC) of buildings are traditionally controlled by a rule-based approach. In order to reduce the energy consumption and the environmental impact of HVAC systems more advanced control methods such as reinforcement learning are promising. Reinforcement learning (RL) strategies offer a good alternative, as user feedback can be integrated more easily and presence can also be incorporated. Moreover, multi-agent RL approaches scale well and can be generalized. In this paper, we propose a multi-agent RL framework based on existing work that learns reducing on one hand energy consumption by optimizing HVAC control and on the other hand user feedback by occupants about uncomfortable room temperatures. Second, we show how to reduce training time required for proper RL-agent-training by using parameter sharing between the multiple agents and apply different pretraining techniques. Results show that our framework is capable of reducing the energy by around 6% when controlling a complete building or 8% for a single room zone. The occupants complaints are acceptable or even better compared to a rule-based baseline. Additionally, our performance analysis show that the training time can be drastically reduced by using parameter sharing.

Daniel Bayer M. Sc.

Forschungsinteressen

Forschungsprojekte

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