Intelligent solar panel monitoring system and shading detection using artificial neural networks

• Published in: ENERGY REPORTS
• Main Authors: Abdallah, Fahad Saleh M.; Abdullah, M. N.; Musirin, Ismail; Elshamy, Ahmed M.
• Format: Article; Proceedings Paper
• Language: English
• Published: ELSEVIER 2023
• Subjects: Energy & Fuels
• Online Access: https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001124237300036

Detecting shading in Photovoltaic panels (PV) is crucial for ensuring optimal energy generation. This paper proposes a novel monitoring system that uses Artificial Neural Network (ANN) technology to detect shading and other faults in PV panels. The system is also supervised using an Internet of Things (IoT) monitoring platform, which provides real-time data analysis and alerts. The proposed system's main contribution is its ability to detect shading, which can significantly impact energy generation. The ANN technology accurately detects shading and other faults, while the IoT platform enables remote monitoring and data analysis. Overall, this paper presents a valuable contribution to the field of PV monitoring systems by proposing a novel system that detects shading using ANN technology and is supervised using an IoT monitoring platform. The system's ability to accurately detect shading and other faults can significantly improve energy generation efficiency and reduce maintenance costs. (c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under theCCBYlicense (http://creativecommons.org/licenses/by/4.0/).