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.

翻译：建筑能源系统的精细调控可有效节约能源并提升居住舒适度。以较高置信度预测特定时间范围内建筑热状态的相关算法，是实现高效控制系统的关键。本文提出一种面向多房间建筑的全局Transformer架构，用于预测室内温度，旨在优化建筑能耗并减少暖通空调系统相关的温室气体排放。相较于传统反馈控制系统，近期深度学习领域的进展使得更复杂的预测模型得以实现。所提出的全局Transformer架构可基于涵盖所有房间的完整数据集进行训练，无需为各房间单独建立模型，从而显著提升预测性能，简化部署与维护流程。值得注意的是，本研究首次将Transformer架构应用于多房间建筑的室内温度预测任务。该方法为提升温度预测的准确性与效率提供了创新解决方案，可成为优化建筑领域能耗、降低温室气体排放的有效工具。