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 6 Issue 6 November-December 2024 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 6 Issue 6
November-December 2024

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

AI-powered Predictive Maintenance for Solar Energy Systems: A Case Study

Author(s) Robin Sarkar
Country United States
Abstract This article presents the implementation of an AI-powered predictive maintenance system at SolarTech
Solutions' 75MW solar installation in Arizona, examining the transformation from traditional reactive
maintenance to an advanced predictive maintenance framework utilizing IoT sensors and artificial
intelligence. The implementation, spanning 18 months, integrated 12,000 distributed sensors with a
sophisticated AI/ML infrastructure, achieving remarkable results across multiple dimensions: 94.3%
accuracy in anomaly detection, 98.2% precision in fault localization, and a 47% reduction in unplanned
downtime, generating annual savings of $425,000. Key operational improvements include a 64% increase
in Mean Time Between Failures (MTBF), reduction in maintenance response time from 72 to 4 hours, and
a 3.2% improvement in panel efficiency. The system's environmental impact is equally significant,
contributing to a reduction of 1,960 metric tons of CO2 emissions annually and conserving 1.2 million
gallons of water per year. The article demonstrates the viability and effectiveness of AI-driven
maintenance solutions in utility-scale solar operations, providing a comprehensive framework for similar
implementations across the renewable energy sector, while addressing critical challenges in deployment,
integration, and optimization of these advanced systems.
Keywords Keywords: Predictive Maintenance, Artificial Intelligence, Solar Energy Systems, IoT Sensors, Machine Learning
Field Computer
Published In Volume 6, Issue 6, November-December 2024
Published On 2024-11-16
Cite This AI-powered Predictive Maintenance for Solar Energy Systems: A Case Study - Robin Sarkar - IJFMR Volume 6, Issue 6, November-December 2024. DOI 10.36948/ijfmr.2024.v06i06.30731
DOI https://doi.org/10.36948/ijfmr.2024.v06i06.30731
Short DOI https://doi.org/g8rdz6
View / Download PDF File

Share this



E-ISSN 2582-2160
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 6 Isu 6Cover Page Vol 6 Isu 5Cover Page Vol 6 Isu 4

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 +91-9898-5948-55 editor@ijfmr.com