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

• URL: http://arxiv.org/abs/2405.14267v2
• Date: Wed, 20 Nov 2024 06:50:50 GMT
• Title: A Gap in Time: The Challenge of Processing Heterogeneous IoT Data in Digitalized Buildings
• Authors: Xiachong Lin, Arian Prabowo, Imran Razzak, Hao Xue, Matthew Amos, Sam Behrens, Flora D. Salim,
• Abstract summary: This study investigates the diverse dimensions of IoT data heterogeneity in both intra-building and inter-building contexts. The results emphasize the critical need for multi-modal data integration, domain-informed modeling, and automated data engineering pipelines.
• Score: 15.525789412274587
• License:
• 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.

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