<![CDATA[Modern industrial power meters are often equipped with Ethernet ports, providing web-based interfaces for real-time monitoring. However, automating data extraction from these interfaces for long-term analysis can be challenging without costly proprietary software. This paper presents a systematic design for an energy logging system that utilizes a web scraping approach to systematically retrieve data from power meter web APIs. The system is implemented using a Python-based scraping script deployed on a Raspberry Pi and utilizes a MongoDB database for scalable data storage and subsequent load profile visualization. Experimental results from an installation at the Universitas Multimedia Nusantara campus demonstrate the system’s ability to effectively monitor high-load Mechanical Ventilation and Air Conditioning (MVAC) systems, providing critical insights into peak consumption periods and operational efficiency. This low-cost, automated solution facilitates data-driven energy management and supports institutional energy-saving initiatives.]]>
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