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

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 intrabuilding and interbuilding 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 multimodal data integration, domain-informed modeling, and automated data engineering pipelines. In addition, the study advocates collaborative efforts to establish high-quality public datasets, which are essential to advance intelligent and sustainable energy management systems in digitalized buildings.