TY - JOUR A1 - Bachseitz, Michael A1 - Sheryar, Muhammad A1 - Schmitt, David A1 - Summ, Thorsten A1 - Trinkl, Christoph A1 - Zörner, Wilfried T1 - PV-Optimized Heat Pump Control in Multi-Family Buildings Using a Reinforcement Learning Approach JF - Energies N2 - 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. 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. UR - https://doi.org/10.3390/en17081908 KW - reinforcement learning KW - PV-optimization KW - heat pump KW - multi-family building KW - energy-management system Y1 - 2024 UR - https://doi.org/10.3390/en17081908 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-47361 SN - 1996-1073 VL - 17 IS - 8 PB - MDPI CY - Basel ER -