Meera Krishnan | June 6, 2025

In the sprawling control rooms of California’s energy grid, software is beginning to think for itself. Algorithms monitor the weather, anticipate spikes in demand, and decide in milliseconds whether to draw power from a solar array, a battery, or a wind farm. The shift is subtle but seismic. As wind and solar power scale up, artificial intelligence (AI) is emerging as the quiet partner in keeping the lights on.

AI’s encroachment into the energy sector is less about sleek futurism and more about pragmatic engineering. Renewable energy, for all its ecological promise, remains unpredictable. The sun does not always shine, and the wind is a fickle business partner. Traditional energy systems, built around controllable fossil-fuel plants, are ill-suited to this variability. AI, by contrast, thrives in complexity.

Forecasting the Inconstant

Start with forecasting. Grid operators have long relied on meteorological models to estimate how much power solar panels or wind turbines might generate. Now, AI tools trained on vast troves of historical data can do the same job with far greater finesse. DeepMind, an AI firm owned by Google’s parent company, claims its machine-learning models improved the predictability of wind output by nearly a fifth. For energy markets that rely on forward contracts and real-time balancing, such improvements are worth millions.

Smarter Grids, Not Just Bigger Ones

Once renewable energy enters the grid, the challenge becomes one of orchestration. AI is proving adept here, too. Modern grids increasingly resemble vast digital marketplaces, where supply and demand fluctuate by the second. AI systems can process streams of data from weather satellites, household batteries, industrial plants, and vehicle chargers—then decide how best to balance them.

This is particularly valuable in jurisdictions with high levels of decentralised generation. In Germany, for example, rooftop solar now accounts for a significant share of total capacity. Without AI to coordinate flows between thousands of micro-generators and consumers, grid stability would be unmanageable.

Storage Plays a Role

Batteries, meanwhile, are evolving from static assets to strategic instruments. AI manages when to charge or discharge, taking into account not only grid conditions but also electricity prices and weather forecasts. Such predictive control improves efficiency and prolongs battery life—a crucial consideration given the capital costs involved.

Firms like Tesla, Fluence, and CATL are already embedding AI into their energy storage platforms. In time, AI may help unlock the full value of seasonal storage—allowing excess solar in summer to be shifted, in part, to winter.

Prevention Over Cure

AI is also proving valuable in maintenance. Rather than waiting for turbines to fail or solar panels to degrade, predictive systems analyse vibrations, heat signatures, or visual anomalies to detect faults before they become serious. Drones equipped with AI-based image recognition can identify micro-cracks on wind blades or dirt accumulation on panels, improving performance and reducing downtime.

For offshore wind farms, where maintenance is costly and sometimes dangerous, such tools are more than convenient—they are economically essential.

From Black Boxes to Public Goods?

Yet the embrace of AI in energy is not without risk. Many models are opaque by design, offering recommendations without clear rationales. In a system as critical as the power grid, the lack of explainability could be troubling. Worse, reliance on AI trained with biased or incomplete data may exacerbate existing inequities—raising electricity prices in poorer neighbourhoods or misallocating grid resources.

Regulators and energy planners will need to keep pace. That may mean mandating greater transparency in AI models, developing common standards, or investing in public data infrastructure to reduce dependence on proprietary systems.

A Quiet Revolution

The integration of AI into renewable energy is unlikely to generate headlines. There are no gleaming new turbines or glistening solar fields to photograph. But beneath the surface, the energy transition is being reshaped not just by technology that captures sunlight or wind—but by systems that can make sense of it.

The revolution may be quiet. But it is very much underway.



This article was published with the help of AI.