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PV-Optimized Heat Pump Control in Multi-Family Buildings Using a Reinforcement Learning Approach

  • For the energy transition in the residential sector, heat pumps are a core technology for decarbonizing thermal energy production for space heating and domestic hot water. Electricity generation from on-site photovoltaic (PV) systems can also contribute to a carbon-neutral building stock. However, both will increase the stress on the electricity grid. This can be reduced by using appropriate control strategies to match electricity consumption and production. In recent years, artificial intelligence-based approaches such as reinforcement learning (RL) have become increasingly popular for energy-system management. However, the literature shows a lack of investigation of RL-based controllers for multi-family building energy systems, including an air source heat pump, thermal storage, and a PV system, although this is a common system configuration. Therefore, in this study, a model of such an energy system and RL-based controllers were developed and simulated with physical models and compared with conventional rule-based approaches. FourFor the energy transition in the residential sector, heat pumps are a core technology for decarbonizing thermal energy production for space heating and domestic hot water. Electricity generation from on-site photovoltaic (PV) systems can also contribute to a carbon-neutral building stock. However, both will increase the stress on the electricity grid. This can be reduced by using appropriate control strategies to match electricity consumption and production. In recent years, artificial intelligence-based approaches such as reinforcement learning (RL) have become increasingly popular for energy-system management. However, the literature shows a lack of investigation of RL-based controllers for multi-family building energy systems, including an air source heat pump, thermal storage, and a PV system, although this is a common system configuration. Therefore, in this study, a model of such an energy system and RL-based controllers were developed and simulated with physical models and compared with conventional rule-based approaches. Four RL algorithms were investigated for two objectives, and finally, the soft actor–critic algorithm was selected for the annual simulations. The first objective, to maintain only the required temperatures in the thermal storage, could be achieved by the developed RL agent. However, the second objective, to additionally improve the PV self-consumption, was better achieved by the rule-based controller. Therefore, further research on the reward function, hyperparameters, and advanced methods, including long short-term memory layers, as well as a training for longer time periods than six days are suggested.show moreshow less

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Metadaten
Author:Michael BachseitzORCiD, Muhammad SheryarORCiD, David SchmittORCiD, Thorsten SummORCiD, Christoph TrinklORCiD, Wilfried Zörner
Language:English
Document Type:Article
Year of first Publication:2024
published in (English):Energies
Publisher:MDPI
Place of publication:Basel
ISSN:1996-1073
Volume:17
Issue:8
Pages:16
Article Number:1908
Review:peer-review
Open Access:ja
Version:published
Tag:PV-optimization; energy-management system; heat pump; multi-family building; reinforcement learning
URN:urn:nbn:de:bvb:573-47361
Related Identifier:https://doi.org/10.3390/en17081908
Faculties / Institutes / Organizations:Fakultät Maschinenbau
Institut für neue Energie-Systeme (InES)
Licence (German):License Logo Creative Commons BY 4.0
Release Date:2024/05/08