Exploring Capabilities of Time Series Foundation Models in Building Analytics
This work addresses energy efficiency for building managers and stakeholders, but it is incremental as it focuses on benchmarking existing models without introducing new methods.
The study tackled the challenge of accurate energy forecasting in buildings by benchmarking time series foundation models on IoT datasets, finding that single-modal models show promise in handling data variability and physical constraints.
The growing integration of digitized infrastructure with Internet of Things (IoT) networks has transformed the management and optimization of building energy consumption. By leveraging IoT-based monitoring systems, stakeholders such as building managers, energy suppliers, and policymakers can make data-driven decisions to improve energy efficiency. However, accurate energy forecasting and analytics face persistent challenges, primarily due to the inherent physical constraints of buildings and the diverse, heterogeneous nature of IoT-generated data. In this study, we conduct a comprehensive benchmarking of two publicly available IoT datasets, evaluating the performance of time series foundation models in the context of building energy analytics. Our analysis shows that single-modal models demonstrate significant promise in overcoming the complexities of data variability and physical limitations in buildings, with future work focusing on optimizing multi-modal models for sustainable energy management.