PV solar anomaly detection using low-cost data logger and
ANN algorithm

PV solar anomaly detection using low-cost data logger and
ANN algorithm

Artificial intelligence
Edge device
Energy management system
Energy monitoring
Predictive maintenance
Renewable energy
Solar energy

This paper presents an innovative edge device architecture that significantly enhances
solar energy management systems. By integrating advanced functionalities
such as generation prediction, maintenance alerts, and solar anomaly detection,
this architecture transforms solar energy management. Through edge
computing, it enables real-time analysis and decision-making at the network
edge. Leveraging machine learning algorithms and accurate predictive models,
these edge devices provide precise energy generation forecasts, facilitating optimal
energy utilization and strategic planning for stakeholders. Additionally, the
architecture incorporates anomaly detection techniques to proactively identify
deviations from normal operation, minimizing downtime, and enabling timely
maintenance. This approach ensures uninterrupted energy generation, enhancing
the reliability and efficiency of the entire monitoring system. The integration
of these features within edge devices improves the performance and reliability
of energy monitoring systems. Implementing this cutting-edge architecture empowers
stakeholders to achieve superior energy management, substantial cost
reductions, and unparalleled system reliability.

Younes Ledmaoui1, Adila El Maghraoui2, Mohamed El Aroussi1, Rachid Saadane1, Abdellah Chehri3,
Ahmed Chebak2

Journal homepage: http://journal.uad.ac.id/index.php/TELKOMNIKA

Nov 26, 2024

PERI IRAWAN

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