Virtual Graph Modeling for Smart Building Energy Analysis from Sequential IoT Data

M Febriansyah, Riadi Marta Dinata

Abstract


This study proposes and validates a cost-effective sequential energy mapping methodology to address the challenges of implementing expensive energy monitoring systems in smart buildings. By utilizing a single Internet of Things (IoT) sensor deployed sequentially across five strategic locations, a comprehensive Energy Profile Repository was successfully developed. Data analysis across various scenarios accurately identified major energy hotspots, with the computer laboratory recording the highest consumption at 2.80 kWh/hour. The study also quantified potential energy waste from “vampire loads,” estimated at 2,190 kWh/year at a single location. Furthermore, the analysis revealed that the heating, ventilation, and air conditioning (HVAC) system is the dominant contributor (65%) to peak load demand, rather than computer units. The individual energy profiles were subsequently synthesized to construct a virtual energy graph that models the characteristics and structural relationships of the building’s energy network. This methodology is proven to be an effective and cost-efficient foundational approach for generating actionable, data-driven insights, while also providing a valid basis for the future development of more advanced real-time energy management systems.

Keywords


energy management; graph analysis; Internet of Things; load profiling; smart building

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References


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DOI: https://doi.org/10.32520/stmsi.v15i3.5714

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