Decarbonisation and efforts to reduce living expenses are driving interest in multicarrier energy systems (MCES) that integrate electricity, heat and e-mobility. These integrated networks require sophisticated energy management strategies to address uncertainties in energy supply, demand, and weather conditions.

This thesis seeks to develop a reinforcement learning (RL)-based energy management system (EMS) for a multicarrier residential building at TU Delft's Green Village. The case study household integrates photovoltaic and solar thermal systems, an electric vehicle (EV), lithium-ion battery, heat pump, and thermal storage. Current management uses white-box model predictive control (MPC) which, while effective, demands significant computational resources. In addition, its implementation requires expertise in optimal control and physics-based modelling.

The developed RL-based EMS provides a computationally efficient alternative, leveraging data-driven methods to learn system dynamics. The RL approach was benchmarked against the existing Expert, a day-ahead MPC planner. Performance was evaluated in terms of operational safety, grid energy exchange costs, and satisfaction of EV state of charge (SoC) demands.

The RL agent achieved performance comparable to that of the Expert in managing the MCES, while improving accessibility for developers lacking control theory expertise. The RL agent displayed consistent and near-optimal performance, resulting in only a 4% increase in grid exchange costs while improving both EV charging compliance and safety constraint adherence.

A literature review of RL applications for residential EMS is presented alongside an investigation of advanced policy update algorithms, deep neural network architectures, temporal feature engineering, and reward shaping strategies to analyse their impact on EMS performance.

The implemented RL-based control solution has been shown to manage electrical and thermal subsystems while maintaining safety and minimising costs. The computational efficiency and reduced modelling requirements of an RL agent highlight its suitability for small-scale MCES applications, such as residential or office buildings, where MPC may not be practical.