Edge AI for real-time anomaly classification in solar photovoltaic systems
This study details the development and deployment of a real-time anomaly classification system on edge AI devices for solar systems. We used a neural network model, fine-tuned using the keras-tuner library, resulting in an average accuracy of 97.95%. Our optimal model demonstrated a robust performan...
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| Tipo de recurso: | tesis de maestría |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2023 |
| País: | Colombia |
| Institución: | Universidad de los Andes |
| Repositorio: | Séneca: repositorio Uniandes |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.uniandes.edu.co:1992/69649 |
| Acceso en línea: | http://hdl.handle.net/1992/69649 |
| Access Level: | acceso abierto |
| Palabra clave: | Edge AI PV solar system Anomaly classification Neural networks Ingeniería |
| Sumario: | This study details the development and deployment of a real-time anomaly classification system on edge AI devices for solar systems. We used a neural network model, fine-tuned using the keras-tuner library, resulting in an average accuracy of 97.95%. Our optimal model demonstrated a robust performance with an accuracy of 97.84% and a small size (31.531 kB). We applied quantization as a model reduction technique, substantially decreasing the model size to 7.455 kB while maintaining similar accuracy. The reduced model was successfully implemented on various edge AI platforms, with STM32F767 Nucleo-144 proving to be the most cost-effective and energy-efficient. The study suggests further research on different solar systems and a comprehensive cost-effectiveness analysis for large-scale deployment. |
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