Current building operations can be improved through smart predictive operation based on weather and use patterns in order to save energy with minimal impact on the building fabric and daily use. The existing literature has investigated implementations, and potential savings through combining with variable tariffs, however, this thesis addresses the issue of how different buildings differ in their suitability for such smart control. In this thesis, a digital twin of a school is created and adjusted to test differences in building fabric factors. These are combined with multiple Deep Reinforcement Learning (DRL) agents, which are trained to operate the schools more efficiently by controlling the heating set-point as well as natural ventilation of the buildings in order to save energy while maintaining comfort. The DRL agents vary in their ability to observe future weather as well as their internal network model architecture. The results show a high energy saving compared to a simple baseline, despite the few building controls available to the agents. In addition, some algorithms out-compete even rule-based controllers, which were tested as a stricter baseline. The results also confirm a theory revealed through the literature review, that buildings with higher energy input, storage and control have a larger potential for energy savings. Additionally, the types of DRL models used also greatly influences the agents’ ability to perform well, and generally more advanced models performed better. The findings can be used to access a building’s suitability for and potential benefits from such predictive smart control.