Artificial Intelligence in Renewable Energy

Global Artificial Intelligence in Renewable Energy Market to Reach US$3.0 Billion by 2030

The global market for Artificial Intelligence in Renewable Energy estimated at US$935.8 Million in the year 2024, is expected to reach US$3.0 Billion by 2030, growing at a CAGR of 21.2% over the analysis period 2024-2030. Solutions, one of the segments analyzed in the report, is expected to record a 19.0% CAGR and reach US$1.7 Billion by the end of the analysis period. Growth in the Services segment is estimated at 24.9% CAGR over the analysis period.

The U.S. Market is Estimated at US$246.0 Million While China is Forecast to Grow at 20.2% CAGR

The Artificial Intelligence in Renewable Energy market in the U.S. is estimated at US$246.0 Million in the year 2024. China, the world`s second largest economy, is forecast to reach a projected market size of US$456.8 Million by the year 2030 trailing a CAGR of 20.2% over the analysis period 2024-2030. Among the other noteworthy geographic markets are Japan and Canada, each forecast to grow at a CAGR of 19.0% and 18.5% respectively over the analysis period. Within Europe, Germany is forecast to grow at approximately 14.8% CAGR.

Global Artificial Intelligence in Renewable Energy Market – Key Trends & Drivers Summarized

Why Is Artificial Intelligence Reshaping Renewable Energy Generation and Grid Efficiency?
Artificial Intelligence (AI) is increasingly pivotal in transforming the renewable energy sector by enhancing system efficiency, optimizing energy generation, and enabling intelligent grid operations. As the global energy mix shifts toward solar, wind, hydro, and other variable renewable sources, the inherent intermittency and unpredictability of these resources present significant operational challenges. AI addresses these challenges by leveraging real-time data analytics, machine learning models, and predictive algorithms to forecast energy generation, monitor equipment health, and manage load distribution dynamically across grids. This intelligence-driven approach allows renewable assets to operate more efficiently, reduces downtime, and minimizes energy wastage.

Moreover, AI supports the integration of distributed energy resources (DERs) such as rooftop solar panels, battery storage systems, and electric vehicles into smart grids. By orchestrating decentralized energy flows, predicting demand patterns, and optimizing energy storage and dispatch, AI enables grid stability while reducing reliance on fossil fuel-based peaking plants. As renewable capacity scales globally, AI is becoming essential for utilities, grid operators, and independent power producers seeking to maintain reliability, reduce operational costs, and support the transition to a decarbonized energy future.

How Are AI Technologies Enhancing Forecasting, Asset Management, and Energy Trading?
AI-powered forecasting tools are significantly improving the accuracy of solar irradiance, wind speed, and weather-based generation models, enabling better scheduling and dispatch of renewable energy. Machine learning algorithms analyze historical weather data, satellite imagery, and real-time meteorological inputs to deliver short- and long-term generation forecasts. These insights are vital for balancing supply and demand, reducing curtailment, and participating in energy markets with greater confidence. In wind energy, for example, AI models trained on turbine sensor data can predict performance anomalies and adjust blade angles to optimize output even in suboptimal wind conditions.

In asset management, AI is being deployed for predictive maintenance of solar panels, wind turbines, and hydroelectric equipment. Computer vision and deep learning algorithms identify early signs of mechanical stress, corrosion, or degradation from image and sensor data, allowing operators to schedule repairs before costly failures occur. Drones equipped with AI-based image recognition are increasingly used for automated inspection of large-scale installations. In energy trading, AI algorithms are facilitating real-time pricing decisions, arbitrage strategies, and hedging based on complex datasets—such as market signals, weather forecasts, and demand fluctuations—improving profitability and risk management for renewable energy traders and utilities.

Where Is Demand for AI in Renewable Energy Growing and Which Applications Are Leading?
Adoption of AI in renewable energy is gaining momentum in North America, Europe, and Asia-Pacific, where renewable penetration is highest and digital infrastructure is more advanced. The U.S. and Canada are investing heavily in AI-enabled smart grids, solar analytics platforms, and wind farm optimization software. Europe, driven by strong climate goals and regulatory frameworks, is deploying AI in offshore wind operations, cross-border energy trading, and regional energy balancing. Germany, the U.K., and the Nordic countries are particularly active in integrating AI across transmission and distribution networks.

In Asia-Pacific, China is using AI to support grid modernization and renewable dispatch management as it scales the world’s largest solar and wind capacity. India, Japan, and South Korea are deploying AI in utility-scale solar and wind projects to enhance grid reliability and reduce energy losses. Key application areas leading adoption include solar PV output forecasting, turbine performance analytics, battery energy storage optimization, and smart demand response programs. In urban areas, AI is also powering smart microgrids and energy management systems (EMS) for commercial and industrial users, reinforcing local resilience and decarbonization.

What Is Driving the Global Growth of AI in the Renewable Energy Sector?
The growth in artificial intelligence in the renewable energy market is driven by several converging forces, including the rapid expansion of renewables, grid decentralization, and the imperative for predictive, real-time decision-making. A major driver is the global shift toward clean energy, which requires intelligent orchestration of variable resources and flexible grid operations. AI enables utilities and energy providers to manage this complexity by turning massive streams of data into actionable insights that improve energy efficiency, asset longevity, and market responsiveness.

Falling costs of AI technologies, edge computing, and IoT sensors are making intelligent energy systems more accessible to utilities, independent power producers, and commercial users. Supportive regulatory policies, carbon neutrality targets, and government funding initiatives—especially in Europe and Asia—are further accelerating the integration of AI into national energy strategies. Additionally, advancements in cloud-based platforms, digital twins, and federated learning models are enhancing collaboration and scalability across stakeholders. As AI becomes foundational to the global energy transition, a critical question emerges: Can artificial intelligence drive the next leap in renewable energy optimization by enabling fully autonomous, self-balancing grids and maximizing sustainability across the energy value chain?

SCOPE OF STUDY:

The report analyzes the Artificial Intelligence in Renewable Energy market in terms of units by the following Segments, and Geographic Regions/Countries:

Segments:
Component (Solutions, Services); Deployment (On-Premise, Cloud); End-Use (Energy Generation, Energy Transmission, Energy Distribution, Utilities)

Geographic Regions/Countries:
World; United States; Canada; Japan; China; Europe (France; Germany; Italy; United Kingdom; and Rest of Europe); Asia-Pacific; Rest of World.

Select Competitors (Total 34 Featured) -

• ABB Ltd
• Accenture
• Amazon Web Services
• Autarq GmbH
• Belectric GmbH
• BrainBox AI
• Clobotics
• DeepMind
• Duke Energy
• E.ON SE
• EDF (Électricité de France)
• Enel Group
• Enphase Energy
• Exowatt
• GE Vernova
• GridBeyond
• Heliogen
• Iberdrola
• Microsoft Corporation
• National Grid plc

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