Global AI in Renewable Energy Market Size was valued at USD 863.9 Mn in 2024 and is predicted to reach USD 5896.9 Mn by 2034 at a 21.3% CAGR during the forecast period for 2025-2034.
In the renewable energy sector, artificial intelligence can analyze weather forecasts, anticipate rainfall patterns, and manage dam functions to maximize energy output while ensuring flood control. This assists in striking a balance between environmental issues and energy production. AI is essential to the ecosystem of smart grids. It enables the strength grid to be monitored and controlled in real-time, increasing its resilience and responsiveness to changes in supply and demand.
In addition to predicting grid congestion and load distribution stability, AI algorithms can identify and respond to cybersecurity threats. Building administration systems powered by AI helps to adapt energy use in industrial, commercial, and residential contexts. These systems can adjust lighting, heating, and cooling depending entirely on occupancy and outside factors, which results in significant energy savings.
The AI in the renewable energy market is segmented by end-use industry, deployment, and component type. Based on deployment, the market is segmented into on-premises and cloud. By end-use industry, the market is segmented into energy generation, energy transmission, energy distribution, and utilities. By component type, the market is segmented into solution and service.
When it comes to implementing AI for a variety of purposes, including grid optimization, customer service, predictive maintenance, and load forecasting, utilities are leading the way. This broad range of uses demonstrates how adaptable AI is and how it can change conventional utility operations into more intelligent, responsive, and customer-focused services. In addition, the utilities sector is leading because of the rising demands of renewable energy sources, regulations, and climate change. By streamlining energy flow, improving the integration of renewable energy sources, and offering data-driven insights for improved decision-making, artificial intelligence (AI) empowers utilities to take on these problems head-on.
The cloud segment is expected to rise at a rapid rate in AI in the renewable energy market. The segment's cost-effectiveness, scalability, and flexibility—all important characteristics for energy businesses negotiating the challenges of digital transformation—are primarily responsible for its supremacy. Energy companies may quickly implement AI solutions across a range of operations with the flexibility provided by the cloud deployment model, all without having to make a sizable upfront investment in IT infrastructure.
Throughout the forecast period, North America is anticipated to grow at the highest rate. The increasing use and acceptance of AI technologies and solutions throughout the energy industry is fueling rise of artificial intelligence in the renewable energy market in the North American region. Digitalization of energy sector is another element driving the expansion of artificial intelligence in the renewable energy sector in the region. Artificial intelligence is also being used to create smart home solutions. This is creating opportunities for artificial intelligence (AI) to grow in North America's renewable energy business.
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Report Attribute |
Specifications |
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Market Size Value In 2024 |
USD 863.9 Mn |
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Revenue Forecast In 2034 |
USD 5896.9 Mn |
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Growth Rate CAGR |
CAGR of 21.3% from 2025 to 2034 |
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Quantitative Units |
Representation of revenue in US$ Bn and CAGR from 2025 to 2034 |
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Historic Year |
2021 to 2024 |
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Forecast Year |
2025-2034 |
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Report Coverage |
The forecast of revenue, the position of the company, the competitive market structure, growth prospects, and trends |
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Segments Covered |
By Deployment, By Component Type, By End-Use Industry and By Region |
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Regional Scope |
North America; Europe; Asia Pacific; Latin America; Middle East & Africa |
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Country Scope |
U.S.; Canada; U.K.; Germany; China; India; Japan; Brazil; Mexico; France; Italy; Spain; South East Asia; South Korea |
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Competitive Landscape |
Flex Ltd., Enel Spa, Alpiq Holding Ltd., General Electric, Enphase Energy, Siemens AG, Origami, Vestas, Atos SE, App Orchid, and Other Prominent Players. |
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Customization Scope |
Free customization report with the procurement of the report and modifications to the regional and segment scope. Particular Geographic competitive landscape. |
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Pricing And Available Payment Methods |
Explore pricing alternatives that are customized to your particular study requirements. |
Chapter 1. Methodology and Scope
1.1. Research Methodology
1.2. Research Scope & Assumptions
Chapter 2. Executive Summary
Chapter 3. Global Artificial Intelligence in Renewable Energy Market Snapshot
Chapter 4. Global Artificial Intelligence in Renewable Energy Market Variables, Trends & Scope
4.1. Market Segmentation & Scope
4.2. Drivers
4.3. Challenges
4.4. Trends
4.5. Investment and Funding Analysis
4.6. Industry Analysis – Porter’s Five Forces Analysis
4.7. Competitive Landscape & Market Share Analysis
4.8. Impact of Covid-19 Analysis
Chapter 5. Market Segmentation 1: by Deployment Type Estimates & Trend Analysis
5.1. by Deployment Type & Market Share, 2024 & 2034
5.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2021 to 2034 for the following by Deployment Type:
5.2.1. On-premises
5.2.2. Cloud
Chapter 6. Market Segmentation 2: by End-Use Industry Estimates & Trend Analysis
6.1. by End-Use Industry & Market Share, 2024 & 2034
6.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2021 to 2034 for the following by End-Use Industry:
6.2.1. Energy Generation
6.2.2. Energy Transmission
6.2.3. Energy Distribution
6.2.4. Utilities
Chapter 7. Market Segmentation 3: by Component Type Estimates & Trend Analysis
7.1. by Component Type & Market Share, 2024 & 2034
7.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2021 to 2034 for the following by Component Type:
7.2.1. Solution
7.2.2. Service
Chapter 8. Artificial Intelligence in Renewable Energy Market Segmentation 4: Regional Estimates & Trend Analysis
8.1. North America
8.1.1. North America Artificial Intelligence in Renewable Energy Market Revenue (US$ Million) Estimates and Forecasts by Deployment Type, 2021-2034
8.1.2. North America Artificial Intelligence in Renewable Energy Market Revenue (US$ Million) Estimates and Forecasts by End-Use Industry, 2021-2034
8.1.3. North America Artificial Intelligence in Renewable Energy Market Revenue (US$ Million) Estimates and Forecasts by Component Type, 2021-2034
8.1.4. North America Artificial Intelligence in Renewable Energy Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
8.2. Europe
8.2.1. Europe Artificial Intelligence in Renewable Energy Market Revenue (US$ Million) Estimates and Forecasts by Deployment Type, 2021-2034
8.2.2. Europe Artificial Intelligence in Renewable Energy Market Revenue (US$ Million) Estimates and Forecasts by End-Use Industry, 2021-2034
8.2.3. Europe Artificial Intelligence in Renewable Energy Market Revenue (US$ Million) Estimates and Forecasts by Component Type, 2021-2034
8.2.4. Europe Artificial Intelligence in Renewable Energy Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
8.3. Asia Pacific
8.3.1. Asia Pacific Artificial Intelligence in Renewable Energy Market Revenue (US$ Million) Estimates and Forecasts by Deployment Type, 2021-2034
8.3.2. Asia Pacific Artificial Intelligence in Renewable Energy Market Revenue (US$ Million) Estimates and Forecasts by End-Use Industry, 2021-2034
8.3.3. Asia-Pacific Artificial Intelligence in Renewable Energy Market Revenue (US$ Million) Estimates and Forecasts by Component Type, 2021-2034
8.3.4. Asia Pacific Artificial Intelligence in Renewable Energy Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
8.4. Latin America
8.4.1. Latin America Artificial Intelligence in Renewable Energy Market Revenue (US$ Million) Estimates and Forecasts by Deployment Type, 2021-2034
8.4.2. Latin America Artificial Intelligence in Renewable Energy Market Revenue (US$ Million) Estimates and Forecasts by End-Use Industry, 2021-2034
8.4.3. Latin America Artificial Intelligence in Renewable Energy Market Revenue (US$ Million) Estimates and Forecasts by Component Type, 2021-2034
8.4.4. Latin America Artificial Intelligence in Renewable Energy Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
8.5. Middle East & Africa
8.5.1. Middle East & Africa Artificial Intelligence in Renewable Energy Market Revenue (US$ Million) Estimates and Forecasts by Deployment Type, 2021-2034
8.5.2. Middle East & Africa Artificial Intelligence in Renewable Energy Market Revenue (US$ Million) Estimates and Forecasts by End-Use Industry, 2021-2034
8.5.3. Middle East & Africa Artificial Intelligence in Renewable Energy Market Revenue (US$ Million) Estimates and Forecasts by Component Type, 2021-2034
8.5.4. Middle East & Africa Artificial Intelligence in Renewable Energy Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
Chapter 9. Competitive Landscape
9.1. Major Mergers and Acquisitions/Strategic Alliances
9.2. Company Profiles
9.2.1. Flex Ltd.
9.2.2. Enel Spa
9.2.3. Alpiq Holding Ltd.
9.2.4. General Electric
9.2.5. Enphase Energy
9.2.6. Siemens AG
9.2.7. Origami
9.2.8. Vestas
9.2.9. Atos SE
9.2.10. App Orchid
9.2.11. Other Market Players
AI in the Renewable Energy Market- By Deployment
AI in the Renewable Energy Market- By End-Use Industry
AI in the Renewable Energy Market- By Component Type
AI in the Renewable Energy Market- By Region
North America-
Europe-
Asia-Pacific-
Latin America-
Middle East & Africa-
InsightAce Analytic follows a standard and comprehensive market research methodology focused on offering the most accurate and precise market insights. The methods followed for all our market research studies include three significant steps – primary research, secondary research, and data modeling and analysis - to derive the current market size and forecast it over the forecast period. In this study, these three steps were used iteratively to generate valid data points (minimum deviation), which were cross-validated through multiple approaches mentioned below in the data modeling section.
Through secondary research methods, information on the market under study, its peer, and the parent market was collected. This information was then entered into data models. The resulted data points and insights were then validated by primary participants.
Based on additional insights from these primary participants, more directional efforts were put into doing secondary research and optimize data models. This process was repeated till all data models used in the study produced similar results (with minimum deviation). This way, this iterative process was able to generate the most accurate market numbers and qualitative insights.
Secondary research
The secondary research sources that are typically mentioned to include, but are not limited to:
The paid sources for secondary research like Factiva, OneSource, Hoovers, and Statista
Primary Research:
Primary research involves telephonic interviews, e-mail interactions, as well as face-to-face interviews for each market, category, segment, and subsegment across geographies
The contributors who typically take part in such a course include, but are not limited to:
Data Modeling and Analysis:
In the iterative process (mentioned above), data models received inputs from primary as well as secondary sources. But analysts working on these models were the key. They used their extensive knowledge and experience about industry and topic to make changes and fine-tuning these models as per the product/service under study.
The standard data models used while studying this market were the top-down and bottom-up approaches and the company shares analysis model. However, other methods were also used along with these – which were specific to the industry and product/service under study.
