  The growing need for sustainable energy solutions has driven the integration
of digitalized buildings into the power grid, utilizing Internet-of-Things
technology to optimize building performance and energy efficiency. However,
incorporating IoT point data within deep-learning frameworks for energy
management presents a complex challenge, predominantly due to the inherent data
heterogeneity. This paper comprehensively analyzes the multifaceted
heterogeneity present in real-world building IoT data streams. We meticulously
dissect the heterogeneity across multiple dimensions, encompassing ontology,
etiology, temporal irregularity, spatial diversity, and their combined effects
on the IoT point data distribution. In addition, experiments using
state-of-the-art forecasting models are conducted to evaluate their impacts on
the performance of deep-learning models for forecasting tasks. By charting the
diversity along these dimensions, we illustrate the challenges and delineate
pathways for future research to leverage this heterogeneity as a resource
rather than a roadblock. This exploration sets the stage for advancing the
predictive abilities of deep-learning algorithms and catalyzing the evolution
of intelligent energy-efficient buildings.
