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

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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.