TY - THES A1 - Sheryar, Muhammad T1 - Reinforcement learning for building energy system control in multi-family buildings N2 - The demand for heat energy is increasing worldwide and to achieve net zero carbon emissions targets, more innovation is needed for heat production. Heat pumps are considered a potential replacement for boilers and are currently in high demand. The next approach is to optimize the use of heat pumps with household PV production to avoid grid overloading due to running increased demand by the heat pump. In this work, a Reinforcement Learning algorithm is used in the MATLAB RL toolbox with an energy-building model built in MATLAB Simulink Carnot. The energy building model uses a heat pump to charge thermal storage, and a PV system is considered with a typical ON/OFF strategy. This work shows how the RL toolbox has the potential to interact with this energy-building model to optimize the heat pump with a PV system. All suggested agents by the MATLAB RL toolbox are investigated with this building energy model (BEM), and annual simulation is performed with a well-trained agent, which converges during training. Two different models have been developed for heat pump control. The first model is called the RL-based Heat Pump Controller, which is designed to meet thermal targets only. The second model is called the PV- optimized RL-based Heat Pump Controller, which not only meets thermal targets but also considers the operation of the PV system with the heat pump. The simulation results show that using the RL toolbox, the RL-based Heat Pump Controller model has performed excellently. In the PV-optimized RL-based Heat Pump Controller model, there is almost a 4.37% increase in PV self-consumption compared to the typical control strategy, resulting in annual electricity savings of almost 3.52 MWh. Some challenges of using the RL toolbox are also highlighted with future recommendations, which mainly include computational efforts. Y1 - 2024 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-44608 CY - Ingolstadt ER -