Nowadays, data-driven approaches are a good way to estimate very efficient black box models for different engineering systems. This class of model is well recognized by its outstanding performances to describe the overall behavior of one system based on its input-output relationships without any physical knowledge. In the context of building modeling, this approach is particularly well suited to predict future temperatures or energy consumption in a building. This paper presents an innovative method that uses input-output data to establish reliable and suitable thermal behavior models for residential buildings, especially for existing buildings where only measurements are available and no numerical models are at the disposal of the facility managers. The main paper contributions consist in the design of a new methodology based on the adaptation of a switched model estimation technique and in its validation to model accurately building thermal behaviors. The paper describes different stages needed to reproduce faithfully complex behaviors: data collection, PieceWise affine Auto-Regressive eXogenous (PWARX) identification technique, sensitivity analysis … It also explains how the procedure and the data-driven estimation algorithm are efficient in extracting sub-model parameters and sequence that give an outstanding ability to reproduce thermal dynamics of buildings, requiring the only collection of available data. The effectiveness of our methodology is discussed through experiments on different buildings located in the North of France. Indeed, through a comparative study between the piecewise ARX model and other existing models such as nonlinear ARX, indexed ARX and ARX models, the PWARX model gives good results in terms of indoor temperature estimation with $78.48\text{\%}$ accuracy.

如今，数据驱动的方法是一种估算不同工程系统非常有效的黑匣子模型的好方法。这种类型的模型以其出色的性能而得到公认，该模型可以基于系统的输入-输出关系来描述一个系统的整体行为，而无需任何物理知识。在建筑物建模的背景下，此方法特别适合预测建筑物中的未来温度或能耗。本文提出了一种创新的方法，该方法使用输入-输出数据为住宅建筑，特别是对于仅提供测量数据而没有数值模型可供设施管理人员使用的现有建筑，建立可靠且合适的热行为模型。论文的主要贡献包括基于转换模型估计技术的适应性的新方法的设计及其对建筑物的热行为进行精确建模的验证。本文描述了忠实地复制复杂行为所需的不同阶段：数据收集，PieceWise仿射自回归外生（PWARX）识别技术，敏感性分析…还解释了该过程和数据驱动的估计算法如何有效地提取子模型。这些参数和序列具有出色的再现建筑物热动力的能力，只需要收集可用数据即可。通过在法国北部的不同建筑物上进行实验，讨论了我们方法论的有效性。确实，$78.48\text{％}$ 准确性。