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 That works well on “normal” days, but it can struggle when something unfamiliar happens and there is no relevant historical reference, such as unusual weather or a sudden shift in how people use electricity.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

Wind turbines market to reach 934.6GW by 2030, forecasts GlobalData

The wind market is entering its strongest growth phase, driven by accelerating national decarbonisation targets and long‑term industrial strategies.

GlobalData’s latest report, 'Wind Turbines Market Size, Share and Trends Analysis by Technology, Installed Capacity, Generation, Key Players and Forecast, 2024-2030', offers comprehensive information and understanding of the global wind turbine market. The report analyses the wind turbine market value and capacity for the historical (2020–2024) and forecast (2025–2030) periods, as well as country-wise drivers and restraints affecting the market.

The report also provides detailed information about key policies and regulations, the market share for 2024, major upcoming projects and contracts for each country. The analysis is based on GlobalData’s proprietary databases, as well as primary and secondary research, and in-house expertise.

The global wind energy market is entering its strongest growth phase to date, driven by accelerating national decarbonisation targets, energy‑security needs and long‑term industrial strategies. The aggregate installations are expected to reach 934.6GW in 2030.

The Asia-Pacific (APAC) region leads the global wind turbine market, accounting for the largest share of annual installations and possessing the most advanced manufacturing capabilities for turbines, components, and offshore technologies. APAC's dominance is primarily driven by China's extensive onshore and offshore development, India's rapidly growing domestic manufacturing and auction-driven expansion, and the emerging offshore projects in Japan and Australia.

Europe, the Middle East, and Africa (EMEA) represent the second-largest market. Europe serves as the regional anchor, bolstered by binding climate mandates under the EU Green Deal, the revised Renewable Energy Directive III, and a strong offshore wind trajectory led by countries in the North Sea. The Americas ranks as the third-largest market, with the US leading the way, where the Inflation Reduction Act (IRA) has stimulated clean energy manufacturing, repowering activities, and the development of an emerging offshore wind supply chain.

The Middle East and North Africa are advancing utility-scale renewable energy projects through government-backed procurements and decarbonisation initiatives, while certain areas of Sub-Saharan Africa are gradually unlocking wind projects with the help of international financing and regional power pool initiatives.

The Americas ranks as the third-largest market, with the US leading the way, where the Inflation Reduction Act (IRA) has stimulated clean energy manufacturing, repowering activities, and the development of an emerging offshore wind supply chain.

Market share leadership is increasingly concentrated among China's major original equipment manufacturers (OEMs), supported by cost-efficient manufacturing and unmatched domestic deployment volumes. Meanwhile, European and US manufacturers remain competitive through advanced offshore technology, digital optimisation, and robust service portfolios. Current trends such as turbine upscaling, hybrid project integration, and supply chain localisation are transforming how and where turbines are produced and deployed.

The global wind turbine market is on the brink of a new era of accelerated growth, fuelled by increasing clean energy commitments, rapid technological advancements, and more resilient manufacturing ecosystems.

With Chinese OEMs leading global capacity additions and Western manufacturers driving innovation in offshore and digital turbine platforms, the industry is entering its most competitive and transformative phase to date. As nations ramp up wind deployment to meet climate targets and ensure long-term energy independence, the global wind turbine market is expected to grow significantly, reinforcing wind power's role as a cornerstone of the world’s renewable energy future.