GREEN ENERGY INFRASTRUCTURE IN RIYADH: ARTIFICIAL INTELLIGENCE-DRIVEN OPTIMIZATION OF RENEWABLE ENERGY SYSTEMS FOR URBAN ENVIRONMENTAL SUSTAINABILITY UNDER VISION 2030
Mohammed Ismail Behlim
🤖 gxceed AI 要約
日本語
リヤドの再生可能エネルギーシステムにAIを適用し、太陽光発電予測精度23〜31%向上、グリッドバランスコスト19%削減、デマンドレスポンス参加率41%向上を実証。適応型エネルギーインテリジェンスアーキテクチャを提案。
English
This paper investigates AI-driven optimization of Riyadh's renewable energy infrastructure, demonstrating 23-31% improvement in solar yield forecasting, 19% reduction in grid balancing costs, and 41% improvement in demand-response participation. It proposes an Adaptive Energy Intelligence Architecture for the city's 50% renewable electricity target by 2030.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本でも再生可能エネルギー大量導入時の系統安定化が課題であり、AIによる需給予測・制御の知見は参考になる。ただしサウジ固有の気候・制度条件が強いため、直接適用には調整が必要。
In the global GX context
This paper provides empirical evidence on how AI can accelerate renewable energy integration in a fossil-fuel-dependent economy, offering lessons for grid modernization and demand-side management globally, particularly for countries with high solar potential.
👥 読者別の含意
🔬研究者:Provides a framework (Adaptive Energy Intelligence Architecture) and empirical performance metrics for AI-driven renewable optimization, useful for comparative studies.
🏢実務担当者:Demonstrates quantifiable benefits of AI in solar forecasting, grid balancing, and demand response, offering implementation insights for utility companies.
🏛政策担当者:Shows how AI can help achieve ambitious renewable energy targets, informing policy design for grid modernization and smart city initiatives.
📄 Abstract（原文）
Saudi Arabia burns roughly 600,000 barrels of crude oil daily just to keep the lights on and the air conditioning running. In a country sitting atop the world’s second-largest proven petroleum reserves, this is not an energy crisis in the conventional sense it is an opportunity cost crisis. Every barrel burned domestically is a barrel not exported at international market prices. Riyadh, consuming approximately 22 percent of the Kingdom’s total electricity output, sits at the center of this paradox. This paper investigates the deployment of artificial intelligence systems across Riyadh’s emerging green energy infrastructure encompassing the 2.6 GW Sudair Solar PV Plant, distributed rooftop photovoltaic networks, green hydrogen pilot facilities, smart grid modernization, and district cooling optimization examining how algorithmic intelligence transforms renewable energy from an intermittent supplement into a reliable backbone for urban sustainability. Through field investigation at seven operational green energy installations, analysis of eighteen months of AI-driven grid management data from Saudi Electricity Company systems, and semi-structured interviews with fifty-one professionals across energy engineering, data science, urban planning, and regulatory governance, we demonstrate that AI-optimized renewable integration achieves 23–31 percent improvement in solar yield forecasting accuracy, 19 percent reduction in grid balancing costs, and 41 percent improvement in demand-response participation rates compared to conventional management approaches. We propose an Adaptive Energy Intelligence Architecture that integrates generation forecasting, demand prediction, storage optimization, and carbon accounting into a unified system targeting Riyadh’s transition to 50 percent renewable electricity by 2030.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.18623/rvd.v23.6578first seen 2026-06-19 05:26:42
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