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<p>As Indonesia navigates a critical energy transition aimed at Net Zero emissions by 2060, the integration of Machine Learning (ML) and the Internet of Things (IoT) has emerged as a primary catalyst for systemic change. This review synthesizes state-of-the-art literature to evaluate the transformative potential of digitalization within Indonesia’s archipelagic landscape. By analyzing over 170 multi-sectoral sources ranging from peer-reviewed technical studies to national policy frameworks, this article identifies key high-impact applications in AI-driven grid stability, predictive maintenance, and forecasting, and employs a comprehensive narrative review approach to synthesize technical and policy barriers. Moving beyond technological optimism to critically examine the "Digitalization Dilemma": the paradox where AI-driven efficiencies are counteracted by the escalating energy demands of data center infrastructure in a coal-heavy grid. The analysis reveals that while technical feasibility is high, implementation is currently throttled by a "Policy-Implementation Gap," infrastructure disparities across the outer islands, and significant cybersecurity vulnerabilities. This paper contributes a novel conceptual framework for a "Green AI" transition and offers strategic recommendations for aligning Indonesia’s digital roadmap with its decarbonization targets.</p>

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Evaluating the role of AI and IoT in advancing sustainable energy transitions within the Indonesian energy sector

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月6日

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  • Nicholas Pranata

摘要

As Indonesia navigates a critical energy transition aimed at Net Zero emissions by 2060, the integration of Machine Learning (ML) and the Internet of Things (IoT) has emerged as a primary catalyst for systemic change. This review synthesizes state-of-the-art literature to evaluate the transformative potential of digitalization within Indonesia’s archipelagic landscape. By analyzing over 170 multi-sectoral sources ranging from peer-reviewed technical studies to national policy frameworks, this article identifies key high-impact applications in AI-driven grid stability, predictive maintenance, and forecasting, and employs a comprehensive narrative review approach to synthesize technical and policy barriers. Moving beyond technological optimism to critically examine the "Digitalization Dilemma": the paradox where AI-driven efficiencies are counteracted by the escalating energy demands of data center infrastructure in a coal-heavy grid. The analysis reveals that while technical feasibility is high, implementation is currently throttled by a "Policy-Implementation Gap," infrastructure disparities across the outer islands, and significant cybersecurity vulnerabilities. This paper contributes a novel conceptual framework for a "Green AI" transition and offers strategic recommendations for aligning Indonesia’s digital roadmap with its decarbonization targets.