  A thorough regulation of building energy systems translates in relevant
energy savings and in a better comfort for the occupants. Algorithms to predict
the thermal state of a building on a certain time horizon with a good
confidence are essential for the implementation of effective control systems.
This work presents a global Transformer architecture for indoor temperature
forecasting in multi-room buildings, aiming at optimizing energy consumption
and reducing greenhouse gas emissions associated with HVAC systems. Recent
advancements in deep learning have enabled the development of more
sophisticated forecasting models compared to traditional feedback control
systems. The proposed global Transformer architecture can be trained on the
entire dataset encompassing all rooms, eliminating the need for multiple
room-specific models, significantly improving predictive performance, and
simplifying deployment and maintenance. Notably, this study is the first to
apply a Transformer architecture for indoor temperature forecasting in
multi-room buildings. The proposed approach provides a novel solution to
enhance the accuracy and efficiency of temperature forecasting, serving as a
valuable tool to optimize energy consumption and decrease greenhouse gas
emissions in the building sector.
