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 4 (July-August 2026) Submit your research before last 3 days of August to publish your research paper in the issue of July-August.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 8 Issue 4
July-August 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

Artificial Intelligence in Renewable Energy Management: Techniques, Emerging Applications, and the Road Ahead

Author(s) Mr. Sumit Mor, Dr. Vipin Saini
Country India
Abstract Decarbonising the global electrical industry is no longer a faraway objective, but rather a technical problem. Nobody predicted ten years ago the speed at which renewable energy projects would grow. But the same characteristics that make solar and wind power attractive – their abundance and near-zero marginal costs – also pose operational issues when scaled up. Grids designed for predictable, synchronous generation are under pressure from weather dependency, spatial and temporal unpredictability, and lack of intrinsic inertia. Here, artificial intelligence has grown from an academic curiosity into a crucial practical tool. This study covers more than 80 peer-reviewed articles published between 2015 and 2025 in journals such as Applied Energy, IEEE Transactions on Smart Grid, Nature Energy, and Energy Conversion and Management. It covers several key topics, such as solar irradiance and power forecasting, wind farm output prediction, smart-grid dispatch and demand response, energy storage management, and condition monitoring with problem diagnosis. Rather than cataloguing approaches, the analysis critically considers what has actually enhanced performance, where promises of generality are exaggerated, and which problems require continuous study. Across all of these trials, effectively designed AI systems tend to cut operating costs by 10-25% and curtailment losses by up to 30%. At the scale of entire continents, these gains mean reduced emissions and greater energy security.
Keywords Artificial intelligence; Renewable energy prediction; Deep learning; Smart grid optimization; Reinforcement learning; Predictive maintenance; Energy storage; Demand response
Field Engineering
Published In Volume 8, Issue 3, May-June 2026
Published On 2026-06-12
DOI https://doi.org/10.36948/ijfmr.2026.v08i03.80800
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 Cover Page Vol 8 Isu 4Cover Page Vol 8 Isu 3Cover Page Vol 8 Isu 2

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