Exploring Capabilities of Time Series Foundation Models in Building Analytics

TL;DR

This paper benchmarks the performance of time series foundation models on IoT-based building energy data, highlighting the potential of single-modal models to address data variability and physical constraints in building energy analytics.

Contribution

It provides a comprehensive comparison of foundation models on IoT datasets for building energy analytics, emphasizing the promise of single-modal models in this domain.

Findings

Single-modal models outperform in handling data variability.

Benchmarking reveals strengths and limitations of current foundation models.

Future work aims to develop multi-modal models for better energy management.

Abstract

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.