The global artificial intelligence (AI) in renewable energy market size is expected to reach around US$ 75.82 billion by 2030 from US$ 8.24 billion in 2021 and is expected to grow at an impressive double-digit rate of 27.9% from 2022 to 2030.
The study includes drivers and restraints of this market. The study provides an analysis of the global artificial intelligence (AI) in renewable energy market for the period 2017-2030, wherein 2022 to 2030 is the forecast period and 2021 is considered as the base year.
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Scope of the Report
| Report Coverage | Details |
| Market Size in 2021 | USD 8.27 Billion |
| CAGR | 27.9% from 2022 to 2030 |
| Revenue Forecast by 2028 | USD 46.03 Billion |
| Base Year | 2021 |
| Forecast Data | 2022 to 2030 |
Report Highlights
- On the basis of application, demand forecasting segment holds the largest market share in the global artificial intelligence (AI) in renewable energy market. In comparison to other technologies, artificial intelligence is extremely efficient. As a result, artificial intelligence is increasingly being used to better this sector. Artificial intelligence, as well as big data, aid in the management and forecasting of renewable energy demand. As a result, over the predicted period, this aspect has a direct impact on the expansion of artificial intelligence (AI) in the renewable energy sector.
- On the basis of end use, energy generation segment holds the largest market share in the global artificial intelligence (AI) in renewable energy market. Growing government funding for infrastructure projects is boosting the rise of artificial intelligence (AI) in the renewable energy market. Furthermore, the government is making ongoing attempts to improve infrastructure. This factor is fueling the segment’s expansion.
Market Dynamics
Drivers
Growing adoption of cloud-based solutions
One of the major trends in artificial intelligence sector is growing adoption of cloud-based solutions in renewable energy sector. Moreover, growing applications of cloud-based solutions in energy sector is paving way for the growth of market. Thus, growing adoption of cloud-based solutions is driving the growth of global artificial intelligence (AI) in renewable energy market over the forecast period.
Restraints
Rising concerns for data privacy
The safety of data and information is biggest concern in the global market. The data with artificial intelligence algorithms can be hacked easily. This data should be kept safe as it is very confidential in nature. As a result, rising concerns for data privacy is restricting the growth of global artificial intelligence (AI) in renewable energy market.
Opportunities
Growing adoption of micro-grids
The micro grids are highly adopted in renewable energy sector due to artificial intelligence. This is possible due to adoption and implementation of smart cities and smart homes. The government are providing favorable conditions for the adoption of micro grids. Thus, growing adoption of micro grids are creating lucrative opportunities for the expansion of global artificial intelligence (AI) in renewable energy market over the forecast period.
Challenges
Lack of resources in underdeveloped regions
The underdeveloped regions do not have enough resources for the development of the artificial intelligence (AI) in renewable energy market. These regions also lack latest technology that can be used in renewable energy sector. As a result, lack of resources in underdeveloped regions is a major challenge for the growth of artificial intelligence (AI) in renewable energy market.
Research Methodology
A unique research methodology has been utilized to conduct comprehensive research on the growth of the global artificial intelligence (AI) in renewable energy market and arrive at conclusions on the future growth prospects of the market. This research methodology is a combination of primary and secondary research, which helps analysts warrant the accuracy and reliability of the draw conclusions. Secondary sources referred to by analysts during the production of the global market report include statistics from company annual reports, SEC filings, company websites, World Bank database, investor presentations, regulatory databases, government publications, and industry white papers. Analysts have also interviewed senior managers, product portfolio managers, CEOs, VPs, and market intelligence managers, who contributed to the production of our study on the market as a primary source.
These primary and secondary sources provided exclusive information during interviews, which serves as a validation from mattress topper industry leaders. Access to an extensive internal repository and external proprietary databases allows this report to address specific details and questions about the global artificial intelligence (AI) in renewable energy market with accuracy. The study also uses the top-down approach to assess the numbers for each segment and the bottom-up approach to counter-validate them. This has helped to estimates the future prospects of the global market more reliable and accurate.
Why should you invest in this report?
If you are aiming to enter the global artificial intelligence (AI) in renewable energy market, this report is a comprehensive guide that provides crystal clear insights into this niche market. All the major application areas for artificial intelligence (AI) in renewable energy are covered in this report and information is given on the important regions of the world where this market is likely to boom during the forecast period of 2022-2030, so that you can plan your strategies to enter this market accordingly.
Besides, through this report, you can have a complete grasp of the level of competition you will be facing in this hugely competitive market and if you are an established player in this market already, this report will help you gauge the strategies that your competitors have adopted to stay as market leaders in this market. For new entrants to this market, the voluminous data provided in this report is invaluable.
Some of the prominent players in the global artificial intelligence (AI) in renewable energy market include:
- Alpiq
- SmartCloud Inc.
- General Electric
- Siemens AG
- Hazama Ando Corporation
- ATOS SE
- AppOrchid Inc.
- Zen Robotics Ltd.
- Origami Energy Ltd.
- Flex Ltd.
Market Segmentation:
By Application
- Robotics
- Renewables Management
- Demand Forecasting
- Safety and Security
- Infrastructure
By End Use
- Energy Generation
- Energy Transmission
- Energy Distribution
- Utilities
By Deployment Type
- On-premise
- Cloud
Regional Analysis:
The geographical analysis of the global artificial intelligence (AI) in renewable energy market has been done for North America, Europe, Asia-Pacific, and the Rest of the World.
The North American Market is again segmented into the US, Canada, and Mexico. Coming to the European Market, it can be segmented further into the UK, Germany, France, Italy, Spain, and the rest. Coming to the Asia-Pacific, the global artificial intelligence (AI) in renewable energy Market is segmented into China, India, Japan, and Rest of Asia Pacific. Among others, the market is segmented into the Middle East and Africa, (GCC, North Africa, South Africa and Rest of the Middle East & Africa).
Key Questions Answered by the Report:
- What will be the size of the global artificial intelligence (AI) in renewable energy market in 2030?
- What is the expected CAGR for the artificial intelligence (AI) in renewable energy market between 2021 and 2030?
- Which are the top players active in this global market?
- What are the key drivers of this global market?
- How will the market situation change in the coming years?
- Which region held the highest market share in this global market?
- What are the common business tactics adopted by players?
- What is the growth outlook of the global artificial intelligence (AI) in renewable energy market?
TABLE OF CONTENT
Chapter 1. Introduction
1.1. Research Objective
1.2. Scope of the Study
1.3. Definition
Chapter 2. Research Methodology
2.1. Research Approach
2.2. Data Sources
2.3. Assumptions & Limitations
Chapter 3. Executive Summary
3.1. Market Snapshot
Chapter 4. Market Variables and Scope
4.1. Introduction
4.2. Market Classification and Scope
4.3. Industry Value Chain Analysis
4.3.1. Raw Material Procurement Analysis
4.3.2. Sales and Deployment Analysis
4.3.3. Downstream Buyer Analysis
Chapter 5. COVID 19 Impact on Artificial Intelligence (AI) in Renewable Energy Market
5.1. COVID-19 Landscape: Artificial Intelligence (AI) in Renewable Energy Industry Impact
5.2. COVID 19 - Impact Assessment for the Industry
5.3. COVID 19 Impact: Global Major Government Policy
5.4. Market Trends and Opportunities in the COVID-19 Landscape
Chapter 6. Market Dynamics Analysis and Trends
6.1. Market Dynamics
6.1.1. Market Drivers
6.1.2. Market Restraints
6.1.3. Market Opportunities
6.2. Porter’s Five Forces Analysis
6.2.1. Bargaining power of suppliers
6.2.2. Bargaining power of buyers
6.2.3. Threat of substitute
6.2.4. Threat of new entrants
6.2.5. Degree of competition
Chapter 7. Competitive Landscape
7.1.1. Company Market Share/Positioning Analysis
7.1.2. Key Strategies Adopted by Players
7.1.3. Vendor Landscape
7.1.3.1. List of Suppliers
7.1.3.2. List of Buyers
Chapter 8. Global Artificial Intelligence (AI) in Renewable Energy Market, By End Use
8.1. Artificial Intelligence (AI) in Renewable Energy Market, by End Use Type, 2022-2030
8.1.1. Energy Generation
8.1.1.1. Market Revenue and Forecast (2017-2030)
8.1.2. Energy Transmission
8.1.2.1. Market Revenue and Forecast (2017-2030)
8.1.3. Energy Distribution
8.1.3.1. Market Revenue and Forecast (2017-2030)
8.1.4. Utilities
8.1.4.1. Market Revenue and Forecast (2017-2030)
Chapter 9. Global Artificial Intelligence (AI) in Renewable Energy Market, By Application
9.1. Artificial Intelligence (AI) in Renewable Energy Market, by Application, 2022-2030
9.1.1. Robotics
9.1.1.1. Market Revenue and Forecast (2017-2030)
9.1.2. Renewable Management
9.1.2.1. Market Revenue and Forecast (2017-2030)
9.1.3. Infrastructure
9.1.3.1. Market Revenue and Forecast (2017-2030)
9.1.4. Safety and Security
9.1.4.1. Market Revenue and Forecast (2017-2030)
9.1.5. Demand Forecasting
9.1.5.1. Market Revenue and Forecast (2017-2030)
Chapter 10. Global Artificial Intelligence (AI) in Renewable Energy Market, By Deployment
10.1. Artificial Intelligence (AI) in Renewable Energy Market, by Deployment, 2022-2030
10.1.1. On-premise
10.1.1.1. Market Revenue and Forecast (2017-2030)
10.1.2. Cloud
10.1.2.1. Market Revenue and Forecast (2017-2030)
Chapter 11. Global Artificial Intelligence (AI) in Renewable Energy Market, Regional Estimates and Trend Forecast
11.1. North America
11.1.1. Market Revenue and Forecast, by End Use (2017-2030)
11.1.2. Market Revenue and Forecast, by Application (2017-2030)
11.1.3. Market Revenue and Forecast, by Deployment (2017-2030)
11.1.4. U.S.
11.1.4.1. Market Revenue and Forecast, by End Use (2017-2030)
11.1.4.2. Market Revenue and Forecast, by Application (2017-2030)
11.1.4.3. Market Revenue and Forecast, by Deployment (2017-2030)
11.1.5. Rest of North America
11.1.5.1. Market Revenue and Forecast, by End Use (2017-2030)
11.1.5.2. Market Revenue and Forecast, by Application (2017-2030)
11.1.5.3. Market Revenue and Forecast, by Deployment (2017-2030)
11.2. Europe
11.2.1. Market Revenue and Forecast, by End Use (2017-2030)
11.2.2. Market Revenue and Forecast, by Application (2017-2030)
11.2.3. Market Revenue and Forecast, by Deployment (2017-2030)
11.2.4. UK
11.2.4.1. Market Revenue and Forecast, by End Use (2017-2030)
11.2.4.2. Market Revenue and Forecast, by Application (2017-2030)
11.2.4.3. Market Revenue and Forecast, by Deployment (2017-2030)
11.2.5. Germany
11.2.5.1. Market Revenue and Forecast, by End Use (2017-2030)
11.2.5.2. Market Revenue and Forecast, by Application (2017-2030)
11.2.5.3. Market Revenue and Forecast, by Deployment (2017-2030)
11.2.6. France
11.2.6.1. Market Revenue and Forecast, by End Use (2017-2030)
11.2.6.2. Market Revenue and Forecast, by Application (2017-2030)
11.2.6.3. Market Revenue and Forecast, by Deployment (2017-2030)
11.2.7. Rest of Europe
11.2.7.1. Market Revenue and Forecast, by End Use (2017-2030)
11.2.7.2. Market Revenue and Forecast, by Application (2017-2030)
11.2.7.3. Market Revenue and Forecast, by Deployment (2017-2030)
11.3. APAC
11.3.1. Market Revenue and Forecast, by End Use (2017-2030)
11.3.2. Market Revenue and Forecast, by Application (2017-2030)
11.3.3. Market Revenue and Forecast, by Deployment (2017-2030)
11.3.4. India
11.3.4.1. Market Revenue and Forecast, by End Use (2017-2030)
11.3.4.2. Market Revenue and Forecast, by Application (2017-2030)
11.3.4.3. Market Revenue and Forecast, by Deployment (2017-2030)
11.3.5. China
11.3.5.1. Market Revenue and Forecast, by End Use (2017-2030)
11.3.5.2. Market Revenue and Forecast, by Application (2017-2030)
11.3.5.3. Market Revenue and Forecast, by Deployment (2017-2030)
11.3.6. Japan
11.3.6.1. Market Revenue and Forecast, by End Use (2017-2030)
11.3.6.2. Market Revenue and Forecast, by Application (2017-2030)
11.3.6.3. Market Revenue and Forecast, by Deployment (2017-2030)
11.3.7. Rest of APAC
11.3.7.1. Market Revenue and Forecast, by End Use (2017-2030)
11.3.7.2. Market Revenue and Forecast, by Application (2017-2030)
11.3.7.3. Market Revenue and Forecast, by Deployment (2017-2030)
11.4. MEA
11.4.1. Market Revenue and Forecast, by End Use (2017-2030)
11.4.2. Market Revenue and Forecast, by Application (2017-2030)
11.4.3. Market Revenue and Forecast, by Deployment (2017-2030)
11.4.4. GCC
11.4.4.1. Market Revenue and Forecast, by End Use (2017-2030)
11.4.4.2. Market Revenue and Forecast, by Application (2017-2030)
11.4.4.3. Market Revenue and Forecast, by Deployment (2017-2030)
11.4.5. North Africa
11.4.5.1. Market Revenue and Forecast, by End Use (2017-2030)
11.4.5.2. Market Revenue and Forecast, by Application (2017-2030)
11.4.5.3. Market Revenue and Forecast, by Deployment (2017-2030)
11.4.6. South Africa
11.4.6.1. Market Revenue and Forecast, by End Use (2017-2030)
11.4.6.2. Market Revenue and Forecast, by Application (2017-2030)
11.4.6.3. Market Revenue and Forecast, by Deployment (2017-2030)
11.4.7. Rest of MEA
11.4.7.1. Market Revenue and Forecast, by End Use (2017-2030)
11.4.7.2. Market Revenue and Forecast, by Application (2017-2030)
11.4.7.3. Market Revenue and Forecast, by Deployment (2017-2030)
11.5. Latin America
11.5.1. Market Revenue and Forecast, by End Use (2017-2030)
11.5.2. Market Revenue and Forecast, by Application (2017-2030)
11.5.3. Market Revenue and Forecast, by Deployment (2017-2030)
11.5.4. Brazil
11.5.4.1. Market Revenue and Forecast, by End Use (2017-2030)
11.5.4.2. Market Revenue and Forecast, by Application (2017-2030)
11.5.4.3. Market Revenue and Forecast, by Deployment (2017-2030)
11.5.5. Rest of LATAM
11.5.5.1. Market Revenue and Forecast, by End Use (2017-2030)
11.5.5.2. Market Revenue and Forecast, by Application (2017-2030)
11.5.5.3. Market Revenue and Forecast, by Deployment (2017-2030)
Chapter 12. Company Profiles
12.1. Alpiq
12.1.1. Company Overview
12.1.2. Product Offerings
12.1.3. Financial Performance
12.1.4. Recent Initiatives
12.2. SmartCloud Inc.
12.2.1. Company Overview
12.2.2. Product Offerings
12.2.3. Financial Performance
12.2.4. Recent Initiatives
12.3. General Electric
12.3.1. Company Overview
12.3.2. Product Offerings
12.3.3. Financial Performance
12.3.4. Recent Initiatives
12.4. Siemens AG
12.4.1. Company Overview
12.4.2. Product Offerings
12.4.3. Financial Performance
12.4.4. Recent Initiatives
12.5. Hazama Ando Corporation
12.5.1. Company Overview
12.5.2. Product Offerings
12.5.3. Financial Performance
12.5.4. Recent Initiatives
12.6. ATOS SE
12.6.1. Company Overview
12.6.2. Product Offerings
12.6.3. Financial Performance
12.6.4. Recent Initiatives
12.7. AppOrchid Inc.
12.7.1. Company Overview
12.7.2. Product Offerings
12.7.3. Financial Performance
12.7.4. Recent Initiatives
12.8. Zen Robotics Ltd.
12.8.1. Company Overview
12.8.2. Product Offerings
12.8.3. Financial Performance
12.8.4. Recent Initiatives
12.9. Origami Energy Ltd.
12.9.1. Company Overview
12.9.2. Product Offerings
12.9.3. Financial Performance
12.9.4. Recent Initiatives
12.10. Flex Ltd.
12.10.1. Company Overview
12.10.2. Product Offerings
12.10.3. Financial Performance
12.10.4. Recent Initiatives
Chapter 13. Research Methodology
13.1. Primary Research
13.2. Secondary Research
13.3. Assumptions
Chapter 14. Appendix
14.1. About Us
14.2. Glossary of Terms
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