A Hybrid Machine Learning Approach: Analyzing Energy Potential and Designing Solar Fault Detection for an AIoT-Based Solar–Hydrogen System in a University Setting

A Hybrid Machine Learning Approach: Analyzing Energy Potential and Designing Solar Fault Detection for an AIoT-Based Solar–Hydrogen System in a University Setting

This research aims to optimize the solar–hydrogen energy system at Kangwon National University’s Samcheok campus by leveraging the integration of artificial intelligence (AI), the Internet of Things (IoT), and machine learning. The primary objective is to enhance the efficiency and reliability of th...

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Bibliographic Details
Main Authors: Salaki Reynaldo Joshua, An Na Yeon, Sanguk Park, Kihyeon Kwon
Format: Article
Language:English
Published: MDPI AG 2024-09-01
Series:Applied Sciences
Subjects:
solar–hydrogen storage system
artificial intelligence (AI)
Internet of Things (IoT)
machine learning
fault detection
Online Access:https://www.mdpi.com/2076-3417/14/18/8573

Internet

https://www.mdpi.com/2076-3417/14/18/8573

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