Intelligent Solar Panel Management using VGG16 and Accessing Early Damage Insights DOI

Arpanpreet Kaur,

Kanwarpartap Singh Gill, Nitin Thapliyal

и другие.

Опубликована: Июль 27, 2024

Язык: Английский

Customized large-scale model for human-AI collaborative operation and maintenance management of building energy systems DOI
Siliang Chen, Xinbin Liang, Liu Ying

и другие.

Applied Energy, Год журнала: 2025, Номер 393, С. 126169 - 126169

Опубликована: Май 20, 2025

Язык: Английский

Процитировано

0
Advanced fault detection, diagnosis and prognosis in HVAC systems: Lifecycle insight, key challenges, and promising approaches DOI
Zhanwei Wang, Yi‐Xian Qin, Yifan Kong

и другие.

Renewable and Sustainable Energy Reviews, Год журнала: 2025, Номер 219, С. 115867 - 115867

Опубликована: Май 27, 2025

Язык: Английский

Процитировано

0
Guest Editorial: Special Issue on Artificial Intelligence in Thermal Engineering Systems DOI
Xiao Fu, Fangzhou Guo, Cheng Fan

и другие.

Applied Thermal Engineering, Год журнала: 2023, Номер 236, С. 121894 - 121894

Опубликована: Ноя. 1, 2023

Язык: Английский

Процитировано

1
Innovative Solutions for Solar Panel Maintenance: A VGG16-Based Approach for Early Damage Detection DOI

Khushi Mittal,

Kanwarpartap Singh Gill,

Saumitra Chattopadhyay

и другие.

2022 International Conference on Communication, Computing and Internet of Things (IC3IoT), Год журнала: 2024, Номер unknown, С. 1 - 4

Опубликована: Апрель 17, 2024

This study examines the creation and assessment of a deep learning-based method for early identification surface impurities damage, such as dust, snow, bird droppings, physical electrical on solar panels. Eighty-seven photos total from collection are categorised into six groups: dusty, clean, drop, snow-covered. The how well transfer learning model performs in correctly identifying these by utilising getting access to VGG16 model. Maintaining maximum panel efficiency, cutting social maintenance costs, minimising resource usage all depend discovery problems. Using cutting-edge Deep algorithms, suggested methodology analyses photographs assigns them appropriate categories. results reveal that proposed technique is successful recognising classifying concerns panels, which can lead increased energy output sustainability. collected show high accuracy precision rate 92.7%.

Язык: Английский

Процитировано

0
Trailblazing Strategies for Solar Panel Maintenance: Employing VGG19 for Early Detection of Damage DOI

Khushi Mittal,

Kanwarpartap Singh Gill,

Deepak Upadhyay

и другие.

Опубликована: Май 3, 2024

Язык: Английский

Процитировано

0
An automatic and efficient fault diagnosis strategy for air conditioning units by combining attention mechanisms DOI
Zhen Jia, Jian G. Qin, Qiqi Yang

и другие.

Science and Technology for the Built Environment, Год журнала: 2024, Номер unknown, С. 1 - 11

Опубликована: Окт. 25, 2024

Язык: Английский

Процитировано

0
Efficient feature selection for enhanced chiller fault diagnosis: A multi-source ranking information-driven ensemble approach DOI
Zhanwei Wang,

Penghua Xia,

Jingjing Guo

и другие.

Building Simulation, Год журнала: 2024, Номер unknown

Опубликована: Дек. 21, 2024

Язык: Английский

Процитировано

0
Intelligent Solar Panel Management using VGG16 and Accessing Early Damage Insights DOI

Arpanpreet Kaur,

Kanwarpartap Singh Gill, Nitin Thapliyal

и другие.

Опубликована: Июль 27, 2024

Язык: Английский

Процитировано

0