What does AI do in a smart grid, and why is it becoming essential?

AI helps grid operators make better decisions by rapidly processing huge volumes of data from sensors, smart meters, weather forecasts and operational systems. In practical terms, it improves load forecasting, spots patterns humans and traditional models can miss, and supports real-time control so the grid can respond to sudden changes in demand or generation. It is becoming essential because the electricity system is changing quickly: renewables add variability, extreme weather is more common, and new large loads such as AI data centres can appear and shift at speed. AI enables operators to move from reacting after the fact to preparing in advance, improving reliability while unlocking efficiency savings.

How does AI improve load forecasting compared with traditional forecasting models?

Traditional forecasting generally relies on mathematical models tuned using historic demand curves and seasonal patterns. That works well on “normal” days, but it can struggle when something unfamiliar happens and there is no relevant historical reference, such as unusual weather or a sudden shift in how people use electricity. AI models learn from complex, multi-source data and can adapt as conditions change, making them stronger at recognising and predicting atypical behaviour. The goal is not to dismiss legacy models, but to reduce the growing gap between forecast and reality on the days when errors are most costly, helping operators run the system more efficiently and avoid serious disruptions.

Can AI help prevent blackouts and improve grid reliability?

Yes, but it does so by strengthening preparedness rather than acting as a magic switch. More accurate, faster forecasting supports better scheduling of generation, storage and flexibility, and helps operators anticipate stress on the network before it becomes critical. AI can also support dynamic grid management by analysing conditions in near real time and recommending actions that keep frequency and supply–demand balance stable. Importantly, utilities treat this carefully because the consequences of mistakes are severe. AI works best as part of a broader resilience toolkit alongside established forecasting, market signals and experienced operational oversight, so decisions remain robust even when the system behaves in unexpected ways.

What can we learn from Hydro-Québec’s use of AI for load forecasting?

Hydro-Québec provides a clear example of how AI is adopted safely and pragmatically. After a proof of concept using a neural network on a single substation, the utility invested in years of research before deploying deep neural networks into production. It now uses AI for short-term forecasting within a 36-hour window, hourly forecasts 10–12 days ahead using meteorologists’ forecasts, and even longer outlooks based on “historic normal” weather values. Crucially, it runs AI alongside legacy models to compare outputs and flag significant discrepancies for human review. During a heatwave, one legacy model needed major corrections while the AI model successfully anticipated an unusual demand pattern, illustrating where AI can add the most value.

How can AI data centres support grid flexibility rather than simply adding demand?

AI data centres can be a difficult load to predict, particularly during model training, which can consume substantial power. However, they can also become a flexible resource if their workloads are orchestrated in response to grid conditions. Trials such as National Grid’s work with Emerald.AI explore “smart mediation”, where software observes data centre workloads, identifies which tasks are non-critical, and adjusts computing activity to shape electricity demand. This can include ramping down by a defined number of megawatts, holding reductions for an agreed period, and ramping back up gradually to avoid “snapback” that can destabilise the grid. Done well, it creates a more symbiotic relationship between digital growth and system reliability.

Power Technology

Daily Newsletter

06 January 2026

Daily Newsletter

Equinor unit sues DoI over stop-work order on Empire Wind

The project company seeks to maintain construction activities on the Empire Wind project while the court reviews its challenge to the federal suspension order.

Equinor subsidiary Empire Offshore Wind has initiated legal proceedings contesting a US Department of the Interior (DoI) directive that ordered a suspension of activity on the Empire Wind project.

A civil suit has been filed by the project company in the US District Court for the District of Columbia.

The first phase of the Empire Wind project, located south of Long Island in New York State, involves the installation of 54 turbines with a generation capacity of 810MW. The second phase may add up to 84 turbines to provide an additional 1.26GW of power.

As part of this lawsuit, Empire Offshore Wind intends to request a preliminary injunction that would permit construction to continue while the case is under judicial review.

The DoI’s Bureau of Ocean Energy Management issued a stop-work notice on 22 December 2025, citing national security concerns.

Empire Offshore Wind has stated that it is complying with the order but plans to seek relief from the court to minimise project disruptions and related commercial effects if the suspension remains in place.

Since finalising its lease in 2017, Empire Offshore Wind has participated in regular national security reviews involving various federal agencies including the Department of War and the US Coast Guard.

Construction activities at Empire Wind include trenching, cable-laying and cable-pulling on the US outer continental shelf, involving various vessels, employees and subcontractors, both domestically and internationally.

In total, nearly 4,000 individuals have worked on the project during its construction phase, which is more than 60% complete, according to company figures as of late September 2025.

Empire Wind is under contract with the New York State Energy Research and Development Authority to supply electricity to New York’s grid, with projections indicating capacity for nearly 500,000 homes once it is operational.