Global AI in Renewable Energy Market Size, Share, Statistics Analysis Report By Deployment (On-Premise, Cloud-based), By Application (Robotics, Renewables Management, Demand Forecasting, Safety and Security, Infrastructure), By End-Use Industry (Energy Generation, Energy Transmission, Energy Distribution, Utilities), Region and Companies - Industry Segment Outlook, Market Assessment, Competition Scenario, Trends and Forecast 2025-2034
- Published date: January 2025
- Report ID: 138353
- Number of Pages: 364
- Format:
-
Quick Navigation
- Report Overview
- Key Takeaways
- China AI in Renewable Energy Market
- Deployment Analysis
- Application Analysis
- End-Use Industry Analysis
- Key Market Segments
- Driver
- Restraint
- Opportunity
- Challenge
- Emerging Trends
- Business Benefits
- Key Player Analysis
- Top Opportunities Awaiting for Players
- Recent Developments
- Report Scope
Report Overview
The Global AI in Renewable Energy Market size is expected to be worth around USD 78.2 Billion By 2034, from USD 8 Billion in 2024, growing at a CAGR of 25.60% during the forecast period from 2025 to 2034. In 2024, the Asia-Pacific region is expected to lead the AI in Renewable Energy market, accounting for over 35% of the market share and generating revenues of USD 2.8 billion.
The AI in renewable energy market is expanding rapidly as energy providers and businesses seek innovative solutions to meet global energy demands sustainably. This market includes a range of AI-powered products and services designed to enhance the operational efficiency of renewable energy systems and to facilitate real-time decision making and forecasting.
The growth in this market is propelled by the increasing adoption of smart grids and the need for efficient energy management systems in both developed and developing regions. Several key factors drive the growth of AI in the renewable energy sector. The urgent need for sustainable energy solutions has led to governmental support and favorable policies promoting renewable energy.
Additionally, technological advancements in AI and machine learning are enabling more accurate predictions and efficient energy management. The decreasing cost of renewable energy technologies, coupled with the increasing availability of big data and IoT devices, also significantly contributes to the sector’s expansion.
The AI in renewable energy market is characterized by several emerging trends. There is a noticeable shift towards the decentralization of energy systems, where AI assists in managing distributed energy resources and microgrids effectively. The use of AI in predictive maintenance to reduce downtime and extend the life of renewable energy assets is becoming increasingly common.
Moreover, the integration of AI with battery storage technology is enhancing the storage and release of renewable energy, aligning supply with demand patterns more effectively. Stakeholders in the AI in renewable energy market, including energy producers, investors, and technology providers, benefit from numerous advantages.
Recent estimates from the Indigo Advisory Group highlight that artificial intelligence (AI) already has over 50 potential applications in the energy sector, particularly in the transition to renewables. From grid maintenance to load forecasting, AI is shaping the future of energy management. The market for AI technologies in this sector is forecasted to hit a valuation of up to $13 billion, driven by innovations that make energy systems smarter and more efficient.
AI’s impact on solar power forecasting. By leveraging advanced AI-driven models, forecasting errors can be reduced by 30% compared to traditional methods, according to “Advances in Solar Photovoltaic Energy Systems.” Similarly, Google’s collaboration with DeepMind resulted in a network model that predicts wind power output 36 hours in advance, boosting its financial value by 20%.
AI-driven solutions enhance operational efficiencies and reduce maintenance costs, leading to higher profitability. They also enable stakeholders to make informed decisions based on real-time data analytics. Furthermore, by investing in AI technologies, stakeholders can significantly contribute to environmental sustainability, enhancing their brand reputation and compliance with regulatory standards for green energy.
Key Takeaways
- The Global AI in Renewable Energy Market size is projected to reach USD 78.2 Billion by 2034, growing from USD 8 Billion in 2024, with a compound annual growth rate (CAGR) of 25.60% during the forecast period from 2025 to 2034.
- In 2024, the Cloud-Based segment led the AI in renewable energy market with over 56% share, leveraging scalability and flexibility to manage vast data from renewable systems.
- The Demand Forecasting segment held a dominant position in the market in 2024, capturing more than 30% share.
- The Energy Generation segment also held a dominant position in the market in 2024, securing more than 35% share.
- The Asia-Pacific region was the largest market for AI in renewable energy in 2024, capturing over 35% share, with revenues totaling USD 2.8 billion.
- The market for AI in renewable energy in China, projected to be worth USD 1.12 billion in 2024, is expected to grow at a CAGR of 27.8%.
China AI in Renewable Energy Market
The market for AI in renewable energy in China, which is projected to be worth US$ 1.12 billion in 2024, is anticipated to expand at a compound annual growth rate (CAGR) of 27.8%. This growth highlights the rising integration of AI in renewable energy to optimize production, improve efficiency, and cut costs.
AI adoption is key to advancing China’s energy goals, enhancing the reliability and efficiency of solar, wind, and hydro power through machine learning, predictive maintenance, and smart grids. AI’s ability to analyze data and predict energy outputs helps manage renewable energy’s intermittency.
The government’s supportive policies and investments in renewable energy are set to drive market growth. Efforts to reduce carbon emissions and promote sustainable practices will spur innovation in AI applications, creating substantial opportunities for stakeholders and investors as China transitions to renewable energy.
In 2024, the Asia-Pacific region held a dominant position in the AI in Renewable Energy market, capturing more than a 35% share with revenues amounting to USD 2.8 billion. This leading stance can be attributed to several factors that uniquely position Asia-Pacific at the forefront of this technological integration into renewable energy.
The rapid economic growth in China, India, and Japan has driven energy demand, prompting the adoption of innovative technologies for sustainable development. AI’s role in optimizing energy production, forecasting, and distribution has become vital, with the region’s focus on reducing carbon footprints and improving energy efficiency accelerating AI deployment in renewable energy sectors.
Governments in the region, including China and India, have supported growth with favorable policies and investments in renewable energy. China’s ambitious goals and India’s 175 GW target by 2022, along with investments in AI R&D, are driving innovation tailored to renewable energy needs.
Deployment Analysis
In 2024, the Cloud-Based segment led the AI in renewable energy market with over 56% share, leveraging scalability and flexibility to manage vast data from renewable systems. Cloud platforms optimize energy production and distribution by efficiently processing real-time data from sources like weather stations and IoT devices.
The leadership of the Cloud-Based segment can also be attributed to the lower upfront costs associated with cloud deployment. Unlike on-premise solutions, cloud-based AI does not require extensive capital investment in hardware.
Moreover, the Cloud-Based segment benefits from rapid deployment capabilities. Cloud solutions can be rolled out much faster than on-premise setups, which often require significant time for installation and configuration. This agility allows energy companies to quickly adapt to changing market dynamics and technological advancements, maintaining a competitive edge in the fast-evolving renewable energy sector.
Cloud-based AI solutions enhance collaboration in renewable energy operations across multiple locations, enabling seamless data sharing and communication among plant managers, maintenance teams, and decision-makers. This ensures alignment and efficient operation throughout the entire process.
Application Analysis
In 2024, the Demand Forecasting segment held a dominant position in the AI in renewable energy market, capturing more than a 30% share. This segment leads primarily because of its crucial role in optimizing energy distribution and utilization.
Demand Forecasting utilizes AI to analyze vast amounts of data from various sources, including weather patterns, consumer usage trends, and grid capabilities. This analysis helps predict energy demand with high accuracy, ensuring that energy production is aligned with consumption needs, thus reducing waste and increasing efficiency.
The Robotics segment plays a crucial role in the AI in renewable energy market, with AI-enhanced robots and drones handling maintenance and inspections of wind turbines and solar panels. This automation reduces downtime, increasing equipment longevity and productivity.
Renewables Management is a key AI application that optimizes the integration and management of renewable energy sources within the grid, improving energy output, distribution, grid stability, and conservation. However, it holds a smaller market share than Demand Forecasting due to the complexity and scale of the solutions required.
End-Use Industry Analysis
In 2024, the Energy Generation segment held a dominant position in the AI in Renewable Energy market, capturing more than a 35% share. This segment leads due to its critical role in integrating advanced AI technologies to optimize energy production from renewable sources such as solar, wind, and hydro.
AI applications in this segment improve energy generation efficiency and reliability through predictive maintenance, smart grid integration, and real-time data analysis. These technologies boost operational efficiency, reduce costs, and minimize environmental impact, making it a key area for investment.
The Energy Transmission segment also plays a vital role, although it commands a smaller share compared to Energy Generation. In this segment, AI is primarily used to improve the efficiency and reliability of transmitting energy from power plants to substations. This includes the use of AI for predictive maintenance to anticipate and prevent potential failures, and for optimizing route efficiency to reduce energy loss.
The Energy Distribution segment uses AI for demand response management and grid balancing, crucial for maintaining stability as renewable energy adoption grows. AI innovations improve control, distribution, customer satisfaction, and operational efficiency. However, its market share is limited by regulatory challenges and the need for large investments in smart grid technology.
Key Market Segments
By Deployment
- On-Premise
- Cloud-based
By Application
- Robotics
- Renewables Management
- Demand Forecasting
- Safety and Security
- Infrastructure
By End-Use Industry
- Energy Generation
- Energy Transmission
- Energy Distribution
- Utilities
Key Regions and Countries
- North America
- US
- Canada
- Europe
- Germany
- France
- The UK
- Spain
- Italy
- Rest of Europe
- Asia Pacific
- China
- Japan
- South Korea
- India
- Australia
- Singapore
- Rest of Asia Pacific
- Latin America
- Brazil
- Mexico
- Rest of Latin America
- Middle East & Africa
- South Africa
- Saudi Arabia
- UAE
- Rest of MEA
Driver
Enhancing Efficiency in Renewable Energy Systems
Artificial Intelligence (AI) is significantly improving the efficiency of renewable energy systems. By analyzing vast amounts of data from sources like weather forecasts and energy consumption patterns, AI enables more accurate predictions of energy generation and demand.
This predictive capability allows for better integration of renewable sources into the power grid, optimizing energy distribution and reducing waste. AI algorithms can forecast solar and wind energy outputs, facilitating efficient energy storage and distribution strategies. This optimization not only maximizes the utilization of renewable resources but also enhances the stability and reliability of the energy supply.
Restraint
High Energy Consumption of AI Technologies
While AI offers numerous benefits, its deployment comes with significant energy demands. Training AI models, especially large-scale ones, requires substantial computational power, leading to increased energy consumption. This high energy usage can offset some of the environmental benefits gained from renewable energy sources.
The energy required for extensive data processing and storage can be considerable, potentially leading to a higher carbon footprint if not managed sustainably. Therefore, it’s crucial to balance the advantages of AI in optimizing renewable energy systems with the need to minimize the energy consumption of the AI technologies themselves.
Opportunity
Accelerating Renewable Energy Integration
AI presents a significant opportunity to expedite the integration of renewable energy sources into existing power grids. By utilizing AI to streamline the interconnection application process, the time required to connect new renewable energy projects to the grid can be significantly reduced.
By improving the accuracy of predictions and enhancing the flexibility of grid operations, AI can significantly reduce waste and improve the integration of renewable energy into existing infrastructure, leading to a more sustainable, resilient energy system. This opportunity not only helps optimize current systems but also accelerates the transition toward a cleaner energy future.
Challenge
Ensuring Cybersecurity in AI-Integrated Energy Systems
Integrating AI into renewable energy systems introduces cybersecurity challenges that must be addressed to maintain system integrity. As energy infrastructures become more digitized and interconnected, they become potential targets for cyberattacks.
Protecting sensitive data and ensuring the security of AI algorithms are paramount to prevent disruptions in energy supply and maintain trust in these advanced systems. Implementing robust cybersecurity measures is essential to safeguard against potential threats and vulnerabilities associated with AI integration in energy systems.
Emerging Trends
One significant trend is the use of AI to predict energy production from renewable sources like wind and solar. By analyzing weather patterns and historical data, AI can forecast how much energy these sources will generate. This helps grid operators balance supply and demand more effectively, ensuring a steady flow of electricity.
Another emerging trend is the optimization of energy storage systems. Renewable energy can be unpredictable, but AI helps manage this by determining the best times to store energy and when to distribute it. This ensures that excess energy isn’t wasted and is available when needed, enhancing the reliability of renewable power.
AI is enhancing renewable energy maintenance by predicting equipment failures and scheduling proactive maintenance. This reduces downtime, extends equipment lifespan, and boosts efficiency and cost-effectiveness.
Business Benefits
One major advantage is cost savings. By accurately predicting energy production and demand, businesses can optimize their operations to reduce waste and lower energy costs. For instance, AI can help determine the most cost-effective times to store or sell energy, maximizing profits.
AI also enhances decision-making by providing detailed insights into energy usage patterns. Businesses can analyze this data to identify areas where energy efficiency can be improved, leading to further cost reductions. Additionally, AI can assist in developing strategies for energy procurement and usage, ensuring that businesses remain competitive in the evolving energy market.
Improved reliability is another key benefit. AI’s predictive maintenance capabilities mean that potential issues can be addressed before they lead to equipment failure, reducing downtime and associated costs. This reliability is crucial for businesses that depend on consistent energy supply to maintain their operations.
Key Player Analysis
Google DeepMind, IBM, and Siemens are using AI to improve grid management, energy storage, and renewable power generation, driving sustainability and enhancing renewable energy systems globally.
- Google DeepMind is leading the charge in AI applications for renewable energy, particularly in improving energy efficiency at scale. This technology has expanded to renewable energy, where DeepMind’s machine learning models predict wind energy generation.
- IBM Corporation is another major player in the intersection of AI and renewable energy. The company uses AI to optimize energy usage, reduce carbon footprints, and predict renewable energy generation patterns.IBM’s AI-driven Energy Management System helps utilities optimize grid stability, forecast energy demand, and integrate renewables like solar and wind, making them a key player in renewable energy sustainability.
- Siemens combines AI with advanced automation to enhance the performance of renewable energy projects. The company uses AI in its digital grid technology, smart infrastructure, and energy storage systems to optimize the generation and distribution of renewable energy.
Top Key Players in the Market
- Google DeepMind
- IBM Corporation
- Siemens
- GE Renewable Energy
- Schneider Electric
- Microsoft
- Vestas
- Atos SE
- Origami
- Omdena Inc.
- Enel Group
- Alpiq Holding Ltd.
- Other Key Players
Top Opportunities Awaiting for Players
AI is creating numerous opportunities for players in the renewable energy market, enabling advancements that enhance efficiency, reduce costs, and improve sustainability.
- Optimization of Energy Production and Distribution: AI technologies are increasingly used to enhance the efficiency of energy production from renewable sources such as wind and solar. By analyzing historical and real-time data, AI can optimize the output of renewable energy plants, reducing waste and improving grid stability.
- Predictive Maintenance and Operations: AI-driven predictive maintenance can foresee and prevent equipment failures in renewable energy infrastructures, such as wind turbines and solar panels. This minimizes downtime and maintenance costs, ensuring higher efficiency and longer asset life.
- Enhanced Energy Storage and Grid Management: Smart grids, enhanced by AI, can better manage the variable output from renewable sources. AI helps integrate energy storage solutions, balancing supply and demand effectively. This not only improves reliability but also aids utilities in managing peak loads without resorting to non-renewable sources.
- Advanced Data Analytics and Visualization: With the growing complexity of the energy grid, especially with the introduction of more renewable sources, AI’s role in advanced data analytics becomes crucial. These tools help energy companies visualize and understand energy patterns, predict demands, and optimize responses.
- Market Analysis and Energy Trading: AI can also revolutionize the way energy trading markets operate by providing more accurate demand forecasts and optimizing trading strategies based on predictive analytics. This can help in maximizing profits and minimizing costs while supporting the adoption of renewable energy on a larger scale.
Recent Developments
- June 2024: Microsoft highlighted its partnerships leveraging AI to drive sustainability in the energy sector. Collaborations with companies like Kongsberg focus on using AI for industrial applications, aiming to improve decision-making and operational efficiency in energy companies.
- October 2024: IBM acquired Prescinto, a provider of asset performance management software for renewables. This acquisition aims to enhance IBM’s capabilities in monitoring and optimizing renewable energy operations using AI.
- November 2024: In collaboration with Sustainable Energy for All, IBM launched AI-powered solutions to support sustainable urban development. These tools assist policymakers in mapping urbanization and identifying energy infrastructure needs in developing regions.
Report Scope
Report Features Description Market Value (2024) USD 8 Bn Forecast Revenue (2034) USD 78.2 Bn CAGR (2025-2034) 25.60% Base Year for Estimation 2024 Historic Period 2020-2023 Forecast Period 2025-2034 Report Coverage Revenue Forecast, Market Dynamics, COVID-19 Impact, Competitive Landscape, Recent Developments Segments Covered By Deployment (On-Premise, Cloud-based), By Application (Robotics, Renewables Management, Demand Forecasting, Safety and Security, Infrastructure), By End-Use Industry (Energy Generation, Energy Transmission, Energy Distribution, Utilities) Regional Analysis North America – US, Canada; Europe – Germany, France, The UK, Spain, Italy, Russia, Netherlands, Rest of Europe; Asia Pacific – China, Japan, South Korea, India, New Zealand, Singapore, Thailand, Vietnam, Rest of APAC; Latin America – Brazil, Mexico, Rest of Latin America; Middle East & Africa – South Africa, Saudi Arabia, UAE, Rest of MEA Competitive Landscape Google DeepMind, IBM Corporation, Siemens, GE Renewable Energy, Schneider Electric, Microsoft, Vestas, Atos SE, Origami, Omdena Inc., Enel Group, Alpiq Holding Ltd., Other Key Players Customization Scope Customization for segments, region/country-level will be provided. Moreover, additional customization can be done based on the requirements. Purchase Options We have three license to opt for: Single User License, Multi-User License (Up to 5 Users), Corporate Use License (Unlimited User and Printable PDF) AI in Renewable Energy MarketPublished date: January 2025add_shopping_cartBuy Now get_appDownload Sample -
-
- Google DeepMind
- IBM Corporation
- Siemens Aktiengesellschaft Company Profile
- GE Renewable Energy
- Schneider Electric SE. Company Profile
- Microsoft Corporation Company Profile
- Vestas
- Atos SE
- Origami
- Omdena Inc.
- Enel Group
- Alpiq Holding Ltd.
- Other Key Players
- settingsSettings
Our Clients
Kickstart 2025 with Exclusive Savings
Single User
$6,000
$3,999
USD / per unit
save 24%
|
Multi User
$8,000
$5,999
USD / per unit
save 28%
|
Corporate User
$10,000
$6,999
USD / per unit
save 32%
|
|
---|---|---|---|
e-Access | |||
Report Library Access | |||
Data Set (Excel) | |||
Company Profile Library Access | |||
Interactive Dashboard | |||
Free Custumization | No | up to 10 hrs work | up to 30 hrs work |
Accessibility | 1 User | 2-5 User | Unlimited |
Analyst Support | up to 20 hrs | up to 40 hrs | up to 50 hrs |
Benefit | Up to 20% off on next purchase | Up to 25% off on next purchase | Up to 30% off on next purchase |
Buy Now ($ 3,999) | Buy Now ($ 5,999) | Buy Now ($ 6,999) |