The University of Queensland has developed Solarisᴬᴵ, a software with a novel approach to solar panel maintenance. It harnesses machine learning algorithms to analyze data from solar panels, with the goal of identifying faults and underperforming units. Notably, Solarisᴬᴵ achieves this without necessitating additional on-site hardware. Serving as a software-as-a-service system for solar farms, it addresses the challenge of monitoring panel health. Trials have demonstrated its effectiveness in detecting issues such as soiling and wiring faults, offering potential cost savings and efficiency enhancements for solar energy operations, according to the university.
University of Queensland develops software for enhanced solar panel maintenance
The University of Queensland has developed Solarisᴬᴵ software for efficient solar panel maintenance without additional hardware. (Image Credit: The University of Queensland)
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