Latent space learning of energy consumption and indoor environmental quality data in context of building technology and construction informatics

建筑技术和建筑信息学背景下能源消耗和室内环境质量数据的潜在空间学习

• 批准号: 510733583
• 负责人: Professor Dr.-Ing. Christoph van Treeck
• 金额: --
• 依托单位: Lehrstuhl für Energieeffizientes Bauen
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/510733583?language=en
• 关键词: Latent; space; learning; energy; consumption

In recent years, the availability of sensor data related to the indoor environmental quality (IEQ), occupant behavior (OB), and energy consumption in buildings increased significantly. This type of data is gaining more importance within occupant-centric healthy building operation or in the deployment of advanced digital technologies within the building system. Related to the constantly increasing volume of data associated with buildings, the data-driven modeling is commonly applied to process these data. Here, machine learning (ML) is widely adopted for processing large amount of data. Typical objectives of data processing are fault detection and predictive ML modeling of stochastic or complex effects such as OB, as well as data-driven control strategies including model predictive control (MPC). In sum, all these technologies demonstrated empirically better performance than the conventional rule-based or statistical models used to achieve the modeling objectives. In addition to the empirically shown higher modeling accuracy, these modeling paradigms do not require explicit encoding of domain knowledge related to the target building. This independence from explicit encoding of domain-specific information offers a hypothetical opportunity to develop generic models and frameworks, that could be used for highly variant modeling.Following this approach in the context of energy consumption in buildings, a single model could then be used to reconstruct the time-series of indoor air temperatures and OB data, and could furthermore be applied to significantly different buildings as well as energy consumption time-series at district or even urban scale granularity. In general, these highly variant data streams might be modeled using similar approaches. However, the applicability of identical models is not warranted due to the differences in the latent space of each target domain. In order to make a scientific progress towards more generic modeling using data related to buildings, IEQ, and energy consumption modeling, this project aims to fulfill the following objectives: 1) create a semantic space to map the similarities and comparing the data streams related to different target variables and different buildings; 2) identify approaches that allow models developed for a particular objective to accommodate for domain specific data when applying them for new modeling objectives (domain adaptation), and 3) use the knowledge gained about the latent space of these data sources to generate synthetic IEQ, OB, and energy consumption data sets.The methodological focus of the proposed project lies on machine learning methods for latent space learning and on methodologies for ensuring the models’ applicability in different target domains. As domain adaptation techniques, we consider transfer learning, latent space learning using autoencoder neural networks (AENN), adversarial learning, and lastly physics-informed learning in this project.

近年来，与建筑物的室内环境质量（IEQ），居住者行为（OB）和能源消耗相关的传感器数据的可用性显着增加。这种类型的数据在以居住者为中心的健康建筑运营中或在建筑系统内部署先进的数字技术中越来越重要。与建筑物相关的数据量不断增加，数据驱动建模通常用于处理这些数据。在这里，机器学习（ML）被广泛用于处理大量数据。数据处理的典型目标是故障检测和随机或复杂效应（如OB）的预测ML建模，以及包括模型预测控制（MPC）在内的数据驱动控制策略。总之，所有这些技术都证明了比用于实现建模目标的传统基于规则或统计模型更好的性能。除了经验显示的更高的建模精度，这些建模范例不需要明确的编码领域知识的目标建筑物。这种独立于特定领域信息的显式编码提供了一个假设的机会来开发通用模型和框架，可以用于高度可变的建模。在建筑物能耗的背景下，遵循这种方法，单个模型可以用于重建室内空气温度和OB数据的时间序列，并且还可以应用于显著不同的建筑物以及在地区或甚至城市尺度粒度上的能量消耗时间序列。通常，这些高度变化的数据流可以使用类似的方法来建模。然而，由于每个目标域的潜在空间的差异，相同的模型的适用性是没有保证的。为了利用与建筑物、IEQ和能耗建模相关的数据朝着更通用的建模方向取得科学进展，本项目旨在实现以下目标：1）创建一个语义空间来映射相似性，并比较与不同目标变量和不同建筑物相关的数据流; 2）确定方法，允许为特定目标开发的模型在将其应用于新的建模目标时适应特定领域的数据（域适应），以及3）使用获得的关于这些数据源的潜在空间的知识来生成合成IEQ，OB，拟议项目的方法重点在于潜在空间学习的机器学习方法和确保模型适用于不同目标的方法。域.作为领域自适应技术，我们在这个项目中考虑了迁移学习、使用自编码器神经网络（AENN）的潜在空间学习、对抗学习以及最后的物理信息学习。