Artificial intelligence and the renewable energy transition in MENA

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Kamran Yeganegi
October 14, 2025

Artificial intelligence has the potential to bridge the gap between abundant natural resources and the pressing need for reliable, sustainable power in the Middle East and North Africa. This column outlines the constraints and proposes policies that can address the challenges of variability of renewable resources and stress on power grids, and support the transformation of ‘sunlight’ to ‘smart power’.

In a nutshell

For the MENA region, the integration of artificial intelligence into renewable energy touches directly on questions of economic diversification, energy security and geopolitical positioning.

Countries that successfully deploy AI-driven energy systems could reduce their vulnerability to oil price volatility, attract foreign investment into clean technology and assert themselves as leaders in the global green economy.

The path from ‘solar to smart’ is neither automatic nor guaranteed: it requires deliberate investment, policy foresight and a commitment to building institutions that can balance innovation with accountability.

Across the Middle East and North Africa (MENA), the expansion of solar photovoltaics and wind power has become a strategic priority in energy policy. Yet a fundamental challenge remains: how to manage the variability of renewable generation reliably.

In recent years, artificial intelligence (AI) has emerged as a promising enabler that can enhance forecasting, optimise storage and support smarter grid operations. The pressing policy question is whether the MENA region is prepared to transform ‘sunlight’ into ‘smart power’. This column reviews global and regional evidence, outlines the constraints and proposes actionable pathways for policy-makers in the region.

Evidence and trends

A recent study shows that AI-based forecasting methods can reduce prediction errors in solar and wind output by up to 30%, thereby improving grid balancing (Zhao et al, 2024).
Similarly, a systematic review of more than 400 research papers finds that AI in renewable energy is most often applied to demand prediction, predictive maintenance, hybrid systems and storage integration (Ejiyi et al, 2025).

In the MENA region, the AI-in-energy market was valued at about US$54.48 million in 2024, with projections of a 22.24% compound annual growth rate (CAGR) through to 2033 (Grand View Research, 2024).

One applied case is the SAI-WEFS (sustainable AI-driven wind energy forecasting syste,), tested in a regional wind farm. This uses hybrid machine learning to provide multi-horizon wind speed and power forecasts while evaluating trade-offs in carbon emissions per computational hour (Algarni et al, 2025).

These examples suggest that AI can directly address two core challenges in MENA’s energy transition: variability of renewable resources; and stress on power grids.

Constraints and critical challenges

Data scarcity and quality: many MENA utilities lack the dense sensor networks and high-frequency logging needed for robust AI models. Poor data leads to degraded or biased performance.
Energy and carbon footprint of AI: training large-scale models and running continuous inference consume significant electricity. By 2027, AI operations could account for 0.5% of global electricity demand (Wikipedia, 2025).
Transparency and trust: many AI models are ‘black boxes’. Without explainability, operators and regulators may resist adoption. Research emphasises the importance of transparency, fairness and accountability in AI for energy (Arrieta et al, 2020).
Regulatory and institutional gaps: in most MENA countries, grid codes, liability frameworks and cybersecurity standards are not yet adapted to AI-enabled systems.
Inequality in capacity: wealthier Gulf states are advancing faster, raising the risk of an ‘AI energy divide’ within the region.

Policy pathways

To move from solar to smart, MENA policy-makers should consider:

Pilot projects: launching AI-enabled solar, wind and storage initiatives in high-resource countries such as Morocco, Saudi Arabia and the United Arab Emirates.
Investment in data infrastructure: deploying IoT (internet of things) sensors, weather stations and open-access energy data platforms.
Green AI strategies: encouraging energy-efficient model architectures and renewable-powered data centers to reduce the carbon footprint of AI.
Regulatory reform: developing standards for explainability, accountability, safety and interoperability in AI for energy.
Regional cooperation: creating joint AI-energy labs, share datasets and models and promote cross-border learning.
Procurement incentives: adjusting tendering processes to favour projects with AI-enabled performance guarantees.

Conclusion

When applied thoughtfully, AI has the potential to redefine the trajectory of MENA’s renewable energy transition. It is not merely a technical tool but a strategic enabler that bridges the gap between abundant natural resources and the region’s pressing need for reliable, sustainable power. By enhancing forecasting accuracy, optimising storage and dispatch and enabling predictive maintenance, AI can transform intermittency from a liability into an asset.

Yet the stakes are higher than technical efficiency alone. For MENA, the integration of AI into renewable energy touches directly on questions of economic diversification, energy security and geopolitical positioning. Countries that successfully deploy AI-driven energy systems could reduce their vulnerability to oil price volatility, attract foreign investment into clean technology and assert themselves as leaders in the global green economy.

Conversely, failure to act could entrench dependence on fossil fuels, deepen inequalities between richer and poorer states in the region, and leave MENA behind in a rapidly evolving technological race.

Crucially, AI also presents an opportunity to strengthen governance and cooperation. Regional collaboration in data sharing, joint research and regulatory harmonisation could not only accelerate the learning curve but also build trust across borders in a region often marked by fragmentation. Such initiatives would help to ensure that the benefits of AI are broadly distributed and that the transition does not exacerbate existing divides.

Ultimately, the path from ‘solar to smart’ is neither automatic nor guaranteed. It requires deliberate investment, policy foresight and a commitment to building institutions that can balance innovation with accountability.

If these conditions are met, MENA’s renewable energy transition could serve as a model of how technology and policy, when aligned, can reshape both national resilience and regional leadership in the global energy landscape.

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