The building sector is one of the largest consumers of final energy, with HVAC systems representing a major share of this demand. Traditional rule-based HVAC controllers often prove inefficient under real operating conditions, as they tend to prioritize guaranteed indoor comfort over energy savings. To address this, recent research has focused on advanced control strategies such as MPC and DRL, which leverage real-time predictive capabilities to improve energy efficiency and flexibility. However, their adoption is limited by high computational requirements, often incompatible with existing BACS and by their "black-box" nature, which reduces stakeholder trust and hinders real-world implementation. This work is positioned within this context, aiming to facilitate the implementation of advanced controllers while improving their transparency and scalability.