Leveraging artificial intelligence for optimizing renewable energy systems and enhancing climate sustainability
再生可能エネルギーシステムの最適化と気候持続可能性向上のための人工知能活用 (AI 翻訳)
Abdullahi Umar Nasiru, Binibor Mary-Ann Ekomerenren, Abdul Salam Abdul Fattah, Mayowa Okunade, Clara Mesoma Nonyelum
🤖 gxceed AI 要約
日本語
本論文は、ナイジェリアにおける再生可能エネルギーシステムの最適化と気候持続可能性向上における人工知能の役割を検討。機械学習や予測分析がエネルギー需要予測や系統安定性を改善する一方、データ基盤や政策面の課題も指摘。AI活用による低炭素移行の可能性を提示。
English
This study explores AI applications for optimizing renewable energy systems in Nigeria. Machine learning and predictive analytics improve demand forecasting, grid stability, and maintenance efficiency, reducing carbon emissions. Challenges include limited data infrastructure and weak policy frameworks. AI offers a transformative pathway for climate resilience.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
In the global GX context
This paper provides a case study of AI-driven renewable energy optimization in a developing economy, highlighting barriers and opportunities relevant to global south energy transitions and sustainable development goals.
👥 読者別の含意
🔬研究者:Highlights AI methods (machine learning, neural networks) for renewable energy optimization in data-scarce settings.
🏢実務担当者:Identifies critical challenges (data infrastructure, technical capacity) for AI deployment in emerging markets.
🏛政策担当者:Recommends institutional support, digital infrastructure investment, and collaborative research for integrating AI into energy governance.
📄 Abstract（原文）
The transition toward sustainable energy systems has become an urgent global priority, particularly in developing economies facing persistent energy and climate challenges. This study explores how Artificial Intelligence (AI) can be leveraged to optimize renewable energy systems and promote climate sustainability in Nigeria. Using a mixed-methods approach that combined secondary data analysis with an extensive review of empirical literature, the research examined the extent to which AI enhances the efficiency, reliability, and environmental performance of renewable energy technologies. Findings reveal that AI applications such as machine learning, neural networks, and predictive analytics significantly improve energy demand forecasting, grid stability, and maintenance efficiency. These technologies contribute to reduced system losses and lower carbon emissions, aligning with Nigeria’s commitment to the Sustainable Development Goals (SDGs) and the Paris Climate Agreement. However, the study also identifies critical challenges, including limited data infrastructure, inadequate technical capacity, and weak policy frameworks that hinder large-scale AI deployment in Nigeria’s energy sector. The study concludes that AI represents a transformative pathway for enhancing renewable energy performance and achieving climate resilience. To maximize its potential, Nigeria must strengthen institutional support, invest in digital infrastructure, and promote collaborative research between academia, industry, and government. By integrating AI into energy governance, Nigeria can accelerate its transition toward a low-carbon, efficient, and sustainable energy future.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.30574/wjaets.2026.19.2.0256first seen 2026-06-02 04:42:28 · last seen 2026-06-03 04:45:04
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