Integrating Deep Learning and Energy Management Standards for Enhanced Solar–Hydrogen Systems: A Study Using MobileNetV2, InceptionV3, and ISO 50001:2018

Abstract

This study addresses the growing need for effective energy management solutions in university settings, with particular emphasis on solar–hydrogen systems. The study’s purpose is to explore the integration of deep learning models, specifically MobileNetV2 and InceptionV3, in enhancing fault detection capabilities in AIoT-based environments, while also customizing ISO 50001:2018 standards to align with the unique energy management needs of academic institutions. Our research employs comparative analysis of the two deep learning models in terms of their performance in detecting solar panel defects and assessing accuracy, loss values, and computational efficiency. The findings reveal that MobileNetV2 achieves 80% accuracy, making it suitable for resource-constrained environments, while InceptionV3 demonstrates superior accuracy of 90% but requires more computational resources. The study concludes that both models offer distinct advantages based on application scenarios, emphasizing the importance of balancing accuracy and efficiency when selecting appropriate models for solar–hydrogen system management. This research highlights the critical role of continuous improvement and leadership commitment in the successful implementation of energy management standards in universities.