AI and ML power sustainable energy policies for developing nations

AI models now predict weather-dependent renewable outputs with higher precision, improving wind turbine placements in India and South Africa and achieving multi-million-dollar annual savings. In Ecuador’s Galapagos Islands, hybrid AI-optimized energy systems have achieved near-total renewable reliance, drastically reducing fossil fuel dependence and carbon emissions.

Artificial intelligence (AI) and machine learning (ML) are poised to reshape global energy systems, particularly in regions struggling with energy poverty, outdated grids, and resource constraints. A new study titled "A Thematic Review of AI and ML in Sustainable Energy Policies for Developing Nations," published in the journal Energies, meticulously outlines how these technologies could unlock more sustainable and equitable energy futures. By thematically categorizing opportunities and challenges, the study offers a roadmap for policymakers, researchers, and industry leaders looking to integrate AI/ML into energy strategies tailored for developing economies.

Conducted by Hassan Qudrat-Ullah at York University, the review synthesizes evidence across energy generation, transmission, consumption, economic and policy frameworks, and interdisciplinary synergies. The comprehensive analysis draws from 301 studies, narrowing down to 42 high-quality articles that capture the transformative potential and barriers of AI/ML integration in sustainable energy development.

How are AI and ML transforming energy generation, transmission, and consumption?

The study highlights AI and ML's pivotal role in optimizing energy generation by enhancing forecasting capabilities, increasing operational efficiency, and managing renewable integration challenges. AI models now predict weather-dependent renewable outputs with higher precision, improving wind turbine placements in India and South Africa and achieving multi-million-dollar annual savings. In Ecuador’s Galapagos Islands, hybrid AI-optimized energy systems have achieved near-total renewable reliance, drastically reducing fossil fuel dependence and carbon emissions.

Transmission and distribution systems also benefit from AI applications. Predictive maintenance solutions, like those implemented in Brazil and Kenya, have led to significant operational cost reductions and improved grid stability. AI-driven load balancing algorithms minimize transmission losses and optimize energy flow even during peak demand, making grids more resilient and efficient.

In energy consumption and demand management, AI-integrated IoT systems have enabled real-time monitoring and optimization of energy use in households and industries. Smart grids in Mexico and Malaysia have demonstrated up to 15% energy savings and substantial economic benefits. These demand-side interventions not only lower costs for consumers but also align behaviors with broader sustainability goals.

Integrated case studies from Kenya and the Galapagos illustrate how AI and ML can bridge infrastructural gaps in remote regions by enhancing renewable energy uptake and boosting grid resilience. Through these examples, the study underscores AI’s dynamic role in tailoring energy solutions to varied socio-economic and environmental contexts.

What are the economic and policy challenges in scaling AI/ML adoption in developing nations?

While AI and ML offer significant benefits, the study stresses that their adoption in developing countries faces considerable economic and policy hurdles. High upfront costs for infrastructure upgrades and AI system deployments, coupled with shortages of skilled labor, remain major barriers. Countries like India, South Africa, and Kenya are grappling with financial constraints that limit the scaling of AI solutions across their energy sectors.

Moreover, the study emphasizes that beyond technological challenges, equitable AI deployment hinges on strong, adaptable policy frameworks. Data-driven policymaking, scenario modeling, and dynamic monitoring, powered by AI tools, enable real-time evaluations and adjustments to energy policies. However, many developing nations lack the regulatory maturity necessary to manage AI’s ethical, legal, and operational complexities, including issues related to data privacy, algorithmic fairness, and cybersecurity.

Economic analysis in the study shows that while predictive analytics and smart maintenance systems can deliver operational cost savings of up to 20% in solar and wind energy, the initial investments are daunting for resource-constrained countries. Policymakers are therefore urged to foster international partnerships, leverage climate funds, and design public–private partnerships that can bridge the financial gaps.

The review also proposes that decision-makers adopt a dynamic feedback-based energy policy framework, incorporating AI to simulate multiple scenarios and optimize policy responses over time. Such adaptability, the study notes, is critical to ensuring sustainable energy transitions in the face of climate change, technological disruption, and socio-economic shifts.

How can future strategies unlock AI/ML’s full potential for sustainable energy in developing nations?

The study outlines several future directions to overcome the existing barriers and fully leverage AI/ML technologies for sustainable energy policies. Firstly, building local capacity is emphasized as critical. Training programs, interdisciplinary curricula, and community-focused initiatives are necessary to equip local populations with the skills to deploy, maintain, and innovate AI-driven energy systems.

Secondly, fostering international collaboration is vital. By sharing technological resources, best practices, and financial support, developed nations can aid in reducing the technological disparity that threatens to widen the energy divide between regions. Collaborative platforms must prioritize open access to AI tools and data to enable localized innovation.

Technological innovations are also key to scaling solutions. The study advocates for the development of lightweight, energy-efficient AI models that can operate on low-power devices, particularly in rural and underserved areas. Integration of AI with blockchain is suggested to enhance transparency and accountability in energy trading and governance.

Furthermore, the study stresses the importance of embedding ethical principles into AI deployment. Fairness, transparency, inclusivity, and cultural sensitivity must be foundational elements of AI-based energy systems. Ethical considerations are particularly important in ensuring that marginalized communities are not left behind in the energy transition.

Comparative analysis with developed nations shows that while the global North leads in advanced AI-driven smart grids and large-scale renewable integration, developing countries have carved out innovative, localized applications that offer lessons in cost-effectiveness and resilience. The study concludes that by bridging local innovations with global best practices, the world can move closer to an equitable, AI-powered sustainable energy future.