标题： 深度迁移学习在智能建筑热力学建模中的应用 显示英文标题

标题： Deep Transfer Learning for Thermal Dynamics Modeling in Smart Buildings

摘要： 热力学建模在建筑供暖、通风和空调（HVAC）系统中是一个关键问题，这会显著影响控制和维护策略。 由于每个具体建筑的独特性，依赖于物理知识的传统热力学建模方法难以很好地泛化。 本研究提出了一种深度监督领域自适应（DSDA）方法，用于建筑室内温度演变和能耗的热力学建模。 基于从建筑中收集的大量数据，预训练了一个基于长短期记忆网络的序列到序列方案，然后通过应用模型微调将其适应到另一个数据量有限的建筑。 我们使用了四个公开可用的数据集：SML 和 AHU 用于温度演变，来自两个不同商业建筑的长期数据集，称为 Building 1 和 Building 2 用于能耗。 我们证明了深度监督领域自适应能够有效地将预训练模型从一个建筑适应到另一个建筑，并且比仅使用有限数据从头学习具有更好的预测性能。 显示英文摘要

摘要： Thermal dynamics modeling has been a critical issue in building heating, ventilation, and air-conditioning (HVAC) systems, which can significantly affect the control and maintenance strategies. Due to the uniqueness of each specific building, traditional thermal dynamics modeling approaches heavily depending on physics knowledge cannot generalize well. This study proposes a deep supervised domain adaptation (DSDA) method for thermal dynamics modeling of building indoor temperature evolution and energy consumption. A long short term memory network based Sequence to Sequence scheme is pre-trained based on a large amount of data collected from a building and then adapted to another building which has a limited amount of data by applying the model fine-tuning. We use four publicly available datasets: SML and AHU for temperature evolution, long-term datasets from two different commercial buildings, termed as Building 1 and Building 2 for energy consumption. We show that the deep supervised domain adaptation is effective to adapt the pre-trained model from one building to another building and has better predictive performance than learning from scratch with only a limited amount of data.