International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 8, Issue 6 (November-December 2026) Submit your research before last 3 days of December to publish your research paper in the issue of November-December.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 8 Issue 6
November-December 2026

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

HelioIQ: AI-Powered Solar Panel Fault Detection and Predictive Maintenance

Author(s) Sonha P, Tejashwini S B, Prathibha G, Dr. G Fathima
Country India
Abstract HelioIQ is an intelligent, AI-powered platform designed to detect solar panel faults and support maintenance by combining multiple data sources into a unified system. Solar panels can experience conditions such as dust accumulation, bird droppings, snow cover, shading, overheating, loose connections, cracks, and electrical damage, which may reduce power generation and efficiency. Existing monitoring methods often rely on manual inspection or a single data source, limiting their ability to identify different fault conditions and link diagnostic results to maintenance actions. HelioIQ addresses these limitations through a multimodal approach that uses solar panel images and sensor readings, including voltage, current, power, panel temperature, and light intensity. A Convolutional Neural Network (CNN) developed using TensorFlow and Keras analyses panel images to classify visible conditions, while an image validation component rejects inputs that do not contain solar panels. An Extreme Gradient Boosting (XGBoost) model analyses sensor data to identify abnormal operating conditions. A decision-level fusion mechanism combines the outputs to determine the overall panel status, fault category, severity level, confidence score, and maintenance recommendation. Cases involving conflicting model outputs are flagged for manual review. The system is implemented as a full-stack web application using FastAPI, JWT-based authentication, a MySQL database, and a React-based dashboard. Its modules include Image Diagnosis, Sensor Diagnosis, Combined Analysis, Solar Array Grid, Prediction History, Work Orders, and Reports. By maintaining diagnostic records and linking detected conditions to structured maintenance tasks, HelioIQ provides an integrated approach to solar panel monitoring and maintenance support.
Keywords Solar Panel Fault Detection, Predictive Maintenance, Multimodal Fusion, Convolutional Neural Network, XGBoost, Condition Monitoring, Renewable Energy
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 8, Issue 6, November-December 2026
Published On 2026-11-03
DOI https://doi.org/10.36948/ijfmr.2026.v08i06.91237
View / Download PDF File

Share this



E-ISSN 2582-2160
CrossRef

CrossRef DOI prefix of IJFMR is 10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9687-828-838 editor@ijfmr.com
Powered by Sky Research Publication and Journals