A Gap in Time: The Challenge of Processing Heterogeneous IoT Data in Digitalized Buildings
This addresses the problem of energy management in digitalized buildings for sustainability efforts, but it is incremental as it benchmarks existing methods without introducing new solutions.
This study tackled the challenge of processing heterogeneous IoT data in digitalized buildings for energy efficiency, finding that state-of-the-art time series models face significant performance issues due to data heterogeneity, highlighting the need for multi-modal integration and domain-informed modeling.
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.