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arXiv · 2405.14267

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

Xiachong Lin·Arian Prabowo·Imran Razzak·Hao Xue·Matthew Amos·Sam Behrens·Flora D. Salim
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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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Xiachong Lin, Arian Prabowo, Imran Razzak, Hao Xue, Matthew Amos, Sam Behrens, Flora D. Salim. 2024-05-23. A Gap in Time: The Challenge of Processing Heterogeneous IoT Data in Digitalized Buildings. https://arxiv.org/abs/2405.14267

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