Automated Smart Solar Panel System Fault Detection and Energy Forecasting for Solar Panels Using Convolutional Neural Networks (CNN) and Deep Learning.

Mr.Shital.M.Patil , Prof. Krishna S. Kadam , Prof. S.S. Sangewar

Solar energy, Panel health monitoring, Fault detection, Image classification, Efficiency, Energy loss prediction, Energy consumption forecasting

The growing reliance on solar energy highlights the need for effective monitoring of solar panel health to optimize energy production. Issues like dust, bird droppings, and physical damage can severely impact efficiency. This project proposes an intelligent system utilizing Convolutional Neural Networks (CNN) and Deep Learning for real-time fault detection in solar panels through image classification. Additionally, it predicts energy loss associated with these faults and forecasts future energy consumption. The system comprises two main components: a CNN-based fault detection mechanism that identifies specific panel issues, and a time-series forecasting model that analyzes historical data and environmental factors to project energy output. This integrated approach aims to enhance operational efficiency, ensure timely maintenance, and maximize sustainable energy production.

Published Paper   E-Certificate

"Automated Smart Solar Panel System Fault Detection and Energy Forecasting for Solar Panels Using Convolutional Neural Networks (CNN) and Deep Learning.", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.9, Issue 11, page no.612 - 617, November-2024, Available :https://ijsdr.org/papers/IJSDR2411071.pdf

Volume 9 Issue 11, November-2024

Pages : 612 - 617

Paper Reg. ID: IJSDR_212772

Published Paper Id: IJSDR2411071

Downloads: 000346999

Research Area: Computer Engineering 

Country: sangli, Maharashtra, India

Published Paper PDF: https://ijsdr.org/papers/IJSDR2411071

Published Paper URL: https://ijsdr.org/viewpaperforall?paper=IJSDR2411071

ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJSDR(IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition