An energy storage system (ESS) in a park surrounded by trees, solar panels, and an wind turbine

META-aiviCase Study

Energy Storage System Setup Verification Using AR + AI

Customer

The customer is Etica Battery Inc., a Taiwan-based manufacturer of energy storage systems (ESS), handheld device batteries, and LEV (Light Electric Vehicle) batteries.

Case

ESS Setup Inspection

Energy storage systems usually consist of battery cells, modules, and power conversion and management systems. They help with peak shaving, integrating renewable energy, boosting energy efficiency, cutting costs, lowering carbon emissions, and enhancing ESG (Environmental, Social, and Governance) performance.

An energy storage system (ESS) in a park surrounded by trees, solar panels, and an wind turbine

Challenge

Limitations of Manual Inspection

Current ESS setup primarily involves manual verification using paper checklists to detect missing parts or confirm the accuracy of knobs and switches. However, human error is unavoidable, necessitating additional administrative steps for verification, which is more labor and resource-intensive. Managing a large volume of paper checklists presents further challenges, and manual data entry can result in recording errors.

Solution

Optimized Setup Verification with META-aivi SOP Validation

META-aivi, powered by deep learning AI, quickly generates models of setup components for inspection. Using AR glasses, workers can conduct real-time verification to ensure correct wiring, switches, and knobs during setup with newfound ease and efficiency. META-aivi’s AR technology guides workers through the correct setup procedures, preventing errors. The inspection results can be uploaded to a central server, establishing a digital record of all inspections for greater diligence.

Outcome

Increased setup accuracy
Reduced setup inspection time
Eliminated data-recording errors
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