BiTSA is an interactive visualization tool that wraps time-series forecasting models (DLinear, PatchTST, One-Fits-All, etc.) for building energy analytics, with a small offline benchmark on two building datasets.
A Gap in Time: The Challenge of Processing Heterogeneous IoT Data in Digitalized Buildings
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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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BiTSA: Leveraging Time Series Foundation Model for Building Energy Analytics
BiTSA is an interactive visualization tool that wraps time-series forecasting models (DLinear, PatchTST, One-Fits-All, etc.) for building energy analytics, with a small offline benchmark on two building datasets.