A Gap in Time: The Challenge of Processing Heterogeneous IoT Data in Digitalized Buildings

TL;DR

This paper explores the challenges of processing heterogeneous IoT data in digitalized buildings, emphasizing the importance of multi-modal data integration and automated pipelines for improving energy management.

Contribution

It provides a comprehensive analysis of IoT data heterogeneity in buildings and benchmarks time series models, highlighting the need for domain-informed approaches and collaborative dataset development.

Findings

Heterogeneity significantly impacts predictive modeling accuracy.

Multi-modal data integration improves energy management predictions.

Benchmark results show current models need enhancements for complex IoT data.

Abstract

The increasing demand for sustainable energy solutions has driven the integration of digitalized buildings into the power grid, leveraging Internet-of-Things (IoT) technologies to enhance energy efficiency and operational performance. Despite their potential, effectively utilizing IoT point data within deep-learning frameworks presents significant challenges, primarily due to its inherent heterogeneity. This study investigates the diverse dimensions of IoT data heterogeneity in both intra-building and inter-building contexts, examining their implications for predictive modeling. A benchmarking analysis of state-of-the-art time series models highlights their performance on this complex dataset. The results emphasize the critical need for multi-modal data integration, domain-informed modeling, and automated data engineering pipelines. Additionally, the study advocates for collaborative efforts to establish high-quality public datasets, which are essential for advancing intelligent and sustainable energy management systems in digitalized buildings.