Leveraging Artificial Intelligence for optimizing renewable energy systems: A pathway to environmental sustainability

<p>Artificial intelligence (AI) has emerged as a key enabler in optimizing renewable energy systems, significantly contributing to global efforts toward environmental sustainability. This review explores the application of AI technologies in enhancing the efficiency, reliability, and integration of renewable energy sources such as solar, wind, and hydropower. It focuses on how machine learning (ML), deep learning (DL), and other AI-driven algorithms improve energy forecasting, grid management, and storage optimization. Survey data and case studies demonstrate the potential of AI to minimize energy waste, reduce costs, and lower greenhouse gas emissions, reinforcing its role in transitioning to a sustainable energy future. The review concludes with a discussion of challenges and future research directions.</p>

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-09-30
DOI
https://doi.org/10.5281/zenodo.14970435
Citations
3
Primary Topic
Energy Load and Power Forecasting
Type
article
Field-Weighted Citation Impact
0.63
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Leveraging Artificial Intelligence for optimizing renewable energy systems: A pathway to environmental sustainability

3 citations
Zenodo (CERN European Organization for Nuclear Research)
Energy Load and Power Forecasting
0.63
article

Leveraging Artificial Intelligence for optimizing renewable energy systems: A pathway to environmental sustainability

article en
3 citations

Abstract

<p>Artificial intelligence (AI) has emerged as a key enabler in optimizing renewable energy systems, significantly contributing to global efforts toward environmental sustainability. This review explores the application of AI technologies in enhancing the efficiency, reliability, and integration of renewable energy sources such as solar, wind, and hydropower. It focuses on how machine learning (ML), deep learning (DL), and other AI-driven algorithms improve energy forecasting, grid management, and storage optimization. Survey data and case studies demonstrate the potential of AI to minimize energy waste, reduce costs, and lower greenhouse gas emissions, reinforcing its role in transitioning to a sustainable energy future. The review concludes with a discussion of challenges and future research directions.</p>

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 30%
Energy Load and Power Forecasting
0.63
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.