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BALANCING THE SURGES: HOW AI SOLVES THE RENEWABLE INTERMITTENCY PROBLEM FOR UZBEKISTAN's GRID

急増する再生可能エネルギーを調整する：AIがウズベキスタンの電力系統の間欠性問題を解決する方法 (AI 翻訳)

Nuraliyeva, Komila Sanaqulovna

Zenodoプレプリント2026-06-08#再生可能エネルギー経営インパクト: コスト削減対象セクター: power
DOI: 10.5281/zenodo.20600687
原典: https://zenodo.org/records/20600687
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🤖 gxceed AI 要約

日本語

本論文は、ウズベキスタンの電力系統における再生可能エネルギーの間欠性問題に対するAIの役割を分析する。LSTMニューラルネットワークや深層Q学習などのAI技術により、予測誤差を5%未満に低減できることを示し、ドイツ、中国などの国際事例と比較しながら政策提言を行う。

English

This paper analyzes the role of AI in addressing renewable energy intermittency in Uzbekistan's power grid. It shows that AI techniques like LSTM and deep Q-learning can reduce forecasting errors to below 5%, drawing on international experiences from Germany, China, UAE, South Korea, and the EU, and proposes policy recommendations.

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の大量導入に伴う系統安定化が課題となっており、特にAIによる予測技術はSSBJや有報でのリスク開示にも関連する点で示唆に富む。

In the global GX context

This study contributes to the global discourse on AI-driven grid management for renewable integration, offering a case study from Uzbekistan that complements experiences in Germany and China, and is relevant for TCFD/ISSB climate risk disclosures.

👥 読者別の含意

🔬研究者:AI for energy forecasting and grid optimization researchers can gain insights from LSTM and reinforcement learning applications in a developing country context.

🏢実務担当者:Grid operators and renewable energy developers can learn about AI-based tools for load balancing and storage management to improve reliability.

🏛政策担当者:Energy regulators can consider AI-enabled digital infrastructure as part of their strategy to integrate high shares of renewables while maintaining grid stability.

📄 Abstract（原文）

This article examines the role of artificial intelligence (AI) in addressing the intermittency challenge of renewable energy sources (RES) within Uzbekistan’s national power system. As Uzbekistan advances toward its strategic target of achieving 25 GW of installed renewable energy capacity by 2030, the stochastic generation characteristics of solar photovoltaic and wind power systems pose significant risks to grid stability, frequency regulation, and dispatch efficiency. The study analyses AI-based forecasting systems, real-time load balancing algorithms, smart grid technologies, battery energy storage systems (BESS), and digital energy management infrastructure. A comparative assessment is conducted using international experiences from Germany, China, the UAE, South Korea, and the European Union. The findings demonstrate that Long Short-Term Memory (LSTM) neural networks, Deep Q-Learning algorithms, and AI-driven digital twin technologies can reduce renewable energy forecasting errors to below 5% under Uzbekistan’s climatic and operational conditions. The article further proposes practical recommendations, institutional reform measures, and policy directions aimed at accelerating the transition toward an AI-optimised and digitally managed energy system.

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