This thesis explores the use of advanced control strategies for district-level building energy management systems across a diverse range of European climates. The study presents a comprehensive benchmarking of 7 control methods: Rule-Based Control (RBC), Offline Deep Reinforcement Learning (Offline DRL), Online DRL, Transfer Learning (TL), Model Predictive Control (MPC), Inverse Reinforcement Learning (IRL), and Behavioral Cloning (BC) for control of mass flow rate and temperature control in 40 target buildings located in Turin, Paris, Helsinki, and Palermo. The study is implmeneded on a co-simulation coupling environment that links a Modelica-based HVAC and EnergyPlus physical building model, and Python-based controller algorithms using Functional Mock-up Interface (FMI).