1. 方法論と範囲
1.1. 調査方法
1.2. 調査目的と調査範囲
2. 定義と概要
3. エグゼクティブ・サマリー
3.1. デプロイメント別スニペット
3.2. コンポーネント別スニペット
3.3. アプリケーション別スニペット
3.4. エンドユーザー別スニペット
3.5. 地域別スニペット
4. ダイナミクス
4.1. 影響要因
4.1.1. 推進要因
4.1.1.1. 予知保全とエネルギー予測のためのデータ分析
4.1.1.2. 政府の政策とクリーンエネルギー技術への投資
4.1.2. 阻害要因
4.1.2.1. 規制と労働力の課題
4.1.3. 機会
4.1.4. 影響分析
5. 産業分析
5.1. ポーターのファイブフォース分析
5.2. サプライチェーン分析
5.3. 価格分析
5.4. 規制分析
5.5. ロシア・ウクライナ戦争の影響分析
5.6. DMI意見
6. 展開別
6.1. はじめに
6.1.1. 市場規模分析および前年比成長率分析（%）、デプロイメント別
6.1.2. 市場魅力度指数（デプロイメント別
6.2. オンプレミス*市場
6.2.1. 導入
6.2.2. 市場規模分析と前年比成長率分析(%)
6.3. クラウドベース
7. コンポーネント別
7.1. はじめに
7.1.1. 市場規模分析および前年比成長率分析（%）, コンポーネント別
7.1.2. 市場魅力度指数（コンポーネント別
7.2. ソリューション*市場
7.2.1. 導入
7.2.2. 市場規模分析と前年比成長率分析(%)
7.3. サービス
7.4. 食肉／家禽
7.5. その他
8. 用途別
8.1. 導入
8.1.1. 用途別市場規模分析および前年比成長率分析(%)
8.1.2. 市場魅力度指数、用途別
8.2. ロボット*市場
8.2.1. はじめに
8.2.2. 市場規模分析と前年比成長率分析(%)
8.3. スマートグリッド管理
8.4. 需要予測
8.5. 安全・セキュリティ・インフラ
8.6. その他
9. エンドユーザー別
9.1. はじめに
9.1.1. 市場規模分析および前年比成長率分析(%), エンドユーザー別
9.1.2. 市場魅力度指数、エンドユーザー別
9.2. エネルギー伝送*市場
9.2.1. 序論
9.2.2. 市場規模分析と前年比成長率分析(%)
9.3. エネルギー生成
9.4. エネルギー分配
9.5. 公益事業
10. 持続可能性分析
10.1. 環境分析
10.2. 経済分析
10.3. ガバナンス分析
11. 地域別
11.1. はじめに
11.1.1. 地域別市場規模分析および前年比成長率分析(%)
11.1.2. 市場魅力度指数、地域別
11.2. 北米
11.2.1. 序論
11.2.2. 主な地域別ダイナミクス
11.2.3. 市場規模分析およびYoY成長率分析（%）、デプロイメント別
11.2.4. 市場規模分析とYoY成長率分析（%）, コンポーネント別
11.2.5. 市場規模分析およびYoY成長率分析（%）, アプリケーション別
11.2.6. 市場規模分析および前年比成長率分析 (%)、エンドユーザー別
11.2.7. 市場規模分析および前年比成長率分析(%)、国別
11.2.7.1. 米国
11.2.7.2. カナダ
11.2.7.3. メキシコ
11.3. ヨーロッパ
11.3.1. はじめに
11.3.2. 主な地域別動向
11.3.3. 市場規模分析およびYoY成長率分析（%）、デプロイメント別
11.3.4. 市場規模分析とYoY成長率分析（%）, コンポーネント別
11.3.5. 市場規模分析およびYoY成長率分析（%）, アプリケーション別
11.3.6. 市場規模分析および前年比成長率分析 (%)、エンドユーザー別
11.3.7. 市場規模分析および前年比成長率分析(%)、国別
11.3.7.1. ドイツ
11.3.7.2. イギリス
11.3.7.3. フランス
11.3.7.4. イタリア
11.3.7.5. スペイン
11.3.7.6. その他のヨーロッパ
11.4. 南米
11.4.1. はじめに
11.4.2. 地域別主要市場
11.4.3. 市場規模分析およびYoY成長率分析（%）（デプロイメント別
11.4.4. 市場規模分析とYoY成長率分析（%）, コンポーネント別
11.4.5. 市場規模分析およびYoY成長率分析（%）, アプリケーション別
11.4.6. 市場規模分析および前年比成長率分析 (%)、エンドユーザー別
11.4.7. 市場規模分析および前年比成長率分析(%)、国別
11.4.7.1. ブラジル
11.4.7.2. アルゼンチン
11.4.7.3. その他の南米諸国
11.5. アジア太平洋
11.5.1. はじめに
11.5.2. 主な地域別ダイナミクス
11.5.3. 市場規模分析およびYoY成長率分析（%）、デプロイメント別
11.5.4. 市場規模分析とYoY成長率分析（%）, コンポーネント別
11.5.5. 市場規模分析およびYoY成長率分析（%）, アプリケーション別
11.5.6. 市場規模分析および前年比成長率分析 (%)、エンドユーザー別
11.5.7. 市場規模分析および前年比成長率分析(%)、国別
11.5.7.1. 中国
11.5.7.2. インド
11.5.7.3. 日本
11.5.7.4. オーストラリア
11.5.7.5. その他のアジア太平洋地域
11.6. 中東・アフリカ
11.6.1. 序論
11.6.2. 主な地域別ダイナミクス
11.6.3. 市場規模分析およびYoY成長率分析（%）、デプロイメント別
11.6.4. 市場規模分析とYoY成長率分析（%）, コンポーネント別
11.6.5. 市場規模分析およびYoY成長率分析（%）, アプリケーション別
11.6.6. 市場規模分析および前年比成長率分析(%)、エンドユーザー別
12. 競合情勢
12.1. 競争シナリオ
12.2. 市場ポジショニング／シェア分析
12.3. M&A分析
13. 企業プロフィール
13.1. ABB*
13.1.1. Company Overview
13.1.2. Type Portfolio and Description
13.1.3. Financial Overview
13.1.4. Key Developments
13.2. Alpiq
13.3. Amazon Web Services, Inc.
13.4. Atos SE
13.5. FlexGen Power Systems, Inc.
13.6. General Electric
13.7. Informatec Ltd.
13.8. N-iX LTD
13.9. Schneider Electric
13.10. Siemens AG
リストは網羅的ではありません
14. 付録
14.1. シーメンスについて
14.2. お問い合わせ
Global AI in Renewable Energy Market reached US$ 845 million in 2023 and is expected to reach US$ 4,823.50 million by 2031, growing with a CAGR of 24.32% during the forecast period.
The market for AI in Renewable Energy is at its early growth stage owing to high demand for sustainable energy sources, advanced artificial intelligence technologies as well as increased government policies toward carbon footprint reduction. AI in renewable energy includes applications such as grid management, energy forecasting, preventive maintenance and also includes integration of various renewable energy sources such as solar, wind and hydropower.
Increasing awareness of climate change and the urgent need for sustainable energy sources are significant drivers for the AI in renewable energy market. According to the International Renewable Energy Agency (IRENA), renewable energy could meet up to 86% of the world’s electricity demand by 2050 if current targets are met, underscoring the potential demand for AI to optimize renewable energy infrastructure.
Asia-Pacific is emerging as the fastest-growing market for AI in renewable energy, with countries like China, Japan and India making substantial investments in green energy and AI technologies. 33% of total energy consumption is expected to come from renewables by 2025, according to China's 14th Five-Year Plan for Renewable Energy and the IEA's Electricity 2024 report. Similarly, India's National Electricity Plan (Transmission) has set a target of 500 GW of renewable capacity by 2030, emphasizing AI to monitor grid stability and improve energy storage.
Dynamics
Data Analytics for Predictive Maintenance and Energy Forecasting
Predictive maintenance from AI is a critical component in mitigating downtime and extending the life of renewable energy. As mentioned by the European Commission, AI analytics would cut maintenance windfarm costs across Europe by about 15-20%, owing to the ability of predictive models to foresee possible breakdowns and schedule interventions efficiently. AI is improving the efficiency of energy dispatch processes as the implementation of AI-enhanced energy forecasting in which power generation based on variable renewable sources is predicted with much more precision contributing to real-time load management.
In addition, government policies such as 'green', drive the use of AI in the renewable industry. For instance, the Green Deal of the European Union, where the aim is to cut carbon emissions to net zero at least by 2030, encourages the development and application of digital technologies within the energy ecosystem.
Private Sector Investments and Technological Partnerships
The private sector is investing heavily in AI-driven renewable energy projects. For example, Google has been working with the energy sector to apply AI technologies in order to improve the efficiency of solar panels and the distribution of power in the grids. The World Economic Forum projects that energy firms increase spending on artificial intelligence technology in upcoming years, with large technology players and energy companies joining forces to enhance renewable energy artificial intelligence solutions.
Similarly, the Energy Department of the United States has invested in funding artificial intelligence and advancing renewable energy technologies recognizing AI capacity in energy management. The IEA states that grid-based digital technology investment increased by more than 50% from 2015 and has been forecasted to account for 19% of the total grid investment by 2023 in readiness for AI integration in renewable energy.
Regulatory and Workforce Challenges
The renewable energy sector is faced with substantial regulations and workforce challenges that hinder the deployment of artificial intelligence (AI) technologies. Regulatory compliance with laws designed to protect information, especially personal data. For instance, the EU GDPR makes it difficult to aggregate and use energy consumption data for AI systems. According to the law, one must obtain informed consent to use personal data for any purpose, which leaves AI developers with a maze of laws to work for data.
Similarly, the renewable energy industry is also experiencing a shortage of talent able to work in artificial intelligence and data analytics. The International Labour Organization (ILO) has estimated that the industry faces a labor shortage in the capacity to create and operate artificial intelligence systems. This skills gap restricts the expansion or efficiency gains, making it more challenging to implement AI-based systems.
Segment Analysis
The global AI in renewable energy market is segmented based on deployment, component, application, end-user and region.
High Demand and Emerging Technology Smart Grid Management
The implementation of Artificial Intelligence (AI) technology within the smart grid systems is revolutionizing energy management by supporting data-driven policies and actions. In a study done by the Electric Power Research Institute (EPRI), smart grids powered by AI were able to lower energy distribution losses by up to 30 percent while allowing for energy to be reallocated in real time. Furthermore, the World Economic Forum notes that the use of AI enhances energy reliability in such systems by 25%, which supports the objective of improved grid performance through the use of AI.
AI tools such as machine learning and predictive analytics are capable of generating large volumes of data from diverse inputs within the grid. This enables real-time surveillance and effective management of energy resources within the system. Data from smart meters and sensors allows AI systems to analyze inefficiencies, forecast demand and resolve the challenges of renewable energy sources. Such capability enhances efficiency in operations and also assists in sustainability as it cuts back on waste generation and improves the efficiency of energy supply systems.
Geographical Penetration
Significant Investments in Renewable Energy in North America
North America is the leading region in the global AI in renewable energy market due to substantial investments in the renewable energy infrastructure, favorable government policies and the integration of superior AI techniques. The U.S. Department of Energy (DOE) has invested hundreds of millions of dollars in both federal research projects and tax credits for renewable energy purposes mainly to foster the creation of energy systems based on artificial intelligence. There are various matches for such funding by Amazon, REC and BlackRock, totaling $500 million, aimed at promoting renewable energy AI initiatives.
In Canada, the renewable energy sector is also experiencing an upsurge in the growth of artificial intelligence applications due to supportive government policy measures such as the Pan-Canadian Framework on Clean Growth and Climate Change that actively promotes the use of AI to enhance energy efficiency and mitigate emissions. Similarly, the Emerging Renewable Power Program (ERPP), in Canada aims to provide provinces and territories with an additional $200 million to help diversify the range of commercially viable renewable energy resources available to them to achieve the GHG emissions reduction targets for the electricity sector.
Competitive Landscape
The major global players in the market include ABB, Alpiq, Amazon Web Services, Inc., Atos SE, FlexGen Power Systems, Inc., General Electric, Informatec Ltd., N-iX LTD, Schneider Electric and Siemens AG.
Sustainability Analysis
The application of Artificial Intelligence is an essential factor in achieving sustainability objectives in the renewable energy industry. Optimizing energy use, minimizing waste generation and improving the efficiency of the grid fit within the parameters of system creation that strives to reduce energy sustainably. Due to AI technologies, there is notable management of renewable resources which helps to ensure complete utilization with minimum harm to the environment.
As highlighted by the International Sustainability Council, renewables could help decrease carbon emissions by 20% in the next ten years, as per efforts geared towards net zero. This is in addition to the already enhanced resilience of renewable infrastructure territories where energy systems driven by AI are so predictive that they can bear shocks and bounce back readily from unpredicted occurrences.
Russia-Ukraine War Impact
The ongoing conflict between Russia and Ukraine has brought several factors that impede the global utilization of AI in the renewable energy market. Actively, the supply chain from the manufacturers of raw materials and parts is requisite for the functioning of the renewable energy systems that rely on AI. East Europe has suffered as a result of its geography where advanced technologies in production are employed by the western countries. This exiguity has resulted in increased expenses and prolonged waiting periods for completion of works especially those involving artificial intelligence in renewable energy projects in most parts of Europe.
Also, the concerns for energy policy have been altered in Europe, as there is no longer dependence on Russian gas and oil, which has affected the energy mix of the continent. The European Union has responded to the crisis and is moving towards renewables, with the integration of AI being particularly important in this strategy for energy generation and control of the grid. The European Commission provided emergency assistance to extend the use of renewable energy and the use of Artificial Intelligence in the REPowerEU initiative to cut down on the use of energy from Russia. The funding enhances the deployment of artificial intelligence solutions for energy supply agitation, forecasting renewable energy generation and grid management in the countries that are members of the European Union.
By Deployment
● On-Premises
● Cloud-Based
By Component
● Solutions
● Services
By Application
● Robotics
● Smart Grid Management
● Demand Forecasting
● Safety Security & Infrastructure
● Others
By End-User
● Energy Transmission
● Energy Generation
● Energy Distribution
● Utilities
By Region
● North America
o US
o Canada
o Mexico
● Europe
o Germany
o UK
o France
o Italy
o Spain
o Rest of Europe
● South America
o Brazil
o Argentina
o Rest of South America
● Asia-Pacific
o China
o India
o Japan
o Australia
o Rest of Asia-Pacific
● Middle East and Africa
Key Developments
● In May 2024, Schneider Electric made a significant leap in home energy management with the launch of an AI-powered feature for its Wiser Home app. This new functionality targets two of the largest household energy consumers—water heaters and electric vehicle (EV) chargers—allowing homeowners to optimize their energy consumption.
● In June 2024, N-iX launched Chat-iX, a conversational assistant for business use, infused with artificial intelligence. This safe and user-friendly platform helps employees and professionals to work with various AI systems, enhancing business processes and workflows. N-iX has also adapted Chat-iX for several sectors, including energy, retail, manufacturing, healthcare and finance which provide customized services to the unique requirements for these sectors.
● In February 2024, GE Vernova announced the first release of Proficy for Sustainability Insights. This is a special software solution designed for industries to align their operational goals with environmental objectives. It links the operational processes and the sustainability information systems of the business so that resources are used effectively with the mitigation of waste while ensuring compliance across different sites.
Why Purchase the Report?
● To visualize the global AI in renewable energy market segmentation based on deployment, component, application, end-user and region.
● Identify commercial opportunities by analyzing trends and co-development.
● Excel data sheet with numerous data points at the AI in renewable energy market level for all segments.
● PDF report consists of a comprehensive analysis after exhaustive qualitative interviews and an in-depth study.
● Product mapping available as excel consisting of key products of all the major players.
The global AI in renewable energy market report would provide approximately 70 tables, 63 figures and 205 pages.
Target Audience 2024
• Manufacturers/ Buyers
• Industry Investors/Investment Bankers
• Research Professionals
• Emerging Companies
1. Methodology and Scope
1.1. Research Methodology
1.2. Research Objective and Scope of the Report
2. Definition and Overview
3. Executive Summary
3.1. Snippet by Deployment
3.2. Snippet by Component
3.3. Snippet by Application
3.4. Snippet by End-User
3.5. Snippet by Region
4. Dynamics
4.1. Impacting Factors
4.1.1. Drivers
4.1.1.1. Data Analytics for Predictive Maintenance and Energy Forecasting
4.1.1.2. Governmental Policies and Investments in Clean Energy Technology
4.1.2. Restraints
4.1.2.1. Regulatory and Workforce Challenges
4.1.3. Opportunity
4.1.4. Impact Analysis
5. Industry Analysis
5.1. Porter's Five Force Analysis
5.2. Supply Chain Analysis
5.3. Pricing Analysis
5.4. Regulatory Analysis
5.5. Russia-Ukraine War Impact Analysis
5.6. DMI Opinion
6. By Deployment
6.1. Introduction
6.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
6.1.2. Market Attractiveness Index, By Deployment
6.2. On-Premises*
6.2.1. Introduction
6.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
6.3. Cloud-Based
7. By Component
7.1. Introduction
7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
7.1.2. Market Attractiveness Index, By Component
7.2. Solutions*
7.2.1. Introduction
7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
7.3. Services
7.4. Meat/Poultry
7.5. Other
8. By Application
8.1. Introduction
8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
8.1.2. Market Attractiveness Index, By Application
8.2. Robotics*
8.2.1. Introduction
8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
8.3. Smart Grid Management
8.4. Demand Forecasting
8.5. Safety Security & Infrastructure
8.6. Others
9. By End-User
9.1. Introduction
9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
9.1.2. Market Attractiveness Index, By End-User
9.2. Energy Transmission*
9.2.1. Introduction
9.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
9.3. Energy Generation
9.4. Energy Distribution
9.5. Utilities
10. Sustainability Analysis
10.1. Environmental Analysis
10.2. Economic Analysis
10.3. Governance Analysis
11. By Region
11.1. Introduction
11.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
11.1.2. Market Attractiveness Index, By Region
11.2. North America
11.2.1. Introduction
11.2.2. Key Region-Specific Dynamics
11.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
11.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
11.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
11.2.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
11.2.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
11.2.7.1. US
11.2.7.2. Canada
11.2.7.3. Mexico
11.3. Europe
11.3.1. Introduction
11.3.2. Key Region-Specific Dynamics
11.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
11.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
11.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
11.3.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
11.3.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
11.3.7.1. Germany
11.3.7.2. UK
11.3.7.3. France
11.3.7.4. Italy
11.3.7.5. Spain
11.3.7.6. Rest of Europe
11.4. South America
11.4.1. Introduction
11.4.2. Key Region-Specific Dynamics
11.4.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
11.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
11.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
11.4.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
11.4.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
11.4.7.1. Brazil
11.4.7.2. Argentina
11.4.7.3. Rest of South America
11.5. Asia-Pacific
11.5.1. Introduction
11.5.2. Key Region-Specific Dynamics
11.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
11.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
11.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
11.5.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
11.5.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
11.5.7.1. China
11.5.7.2. India
11.5.7.3. Japan
11.5.7.4. Australia
11.5.7.5. Rest of Asia-Pacific
11.6. Middle East and Africa
11.6.1. Introduction
11.6.2. Key Region-Specific Dynamics
11.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
11.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
11.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
11.6.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
12. Competitive Landscape
12.1. Competitive Scenario
12.2. Market Positioning/Share Analysis
12.3. Mergers and Acquisitions Analysis
13. Company Profiles
13.1. ABB*
13.1.1. Company Overview
13.1.2. Type Portfolio and Description
13.1.3. Financial Overview
13.1.4. Key Developments
13.2. Alpiq
13.3. Amazon Web Services, Inc.
13.4. Atos SE
13.5. FlexGen Power Systems, Inc.
13.6. General Electric
13.7. Informatec Ltd.
13.8. N-iX LTD
13.9. Schneider Electric
13.10. Siemens AG
LIST NOT EXHAUSTIVE
14. Appendix
14.1. About Us and Services
14.2. Contact Us
❖ 世界の再生可能エネルギーにおけるAI市場に関するよくある質問(FAQ) ❖
・再生可能エネルギーにおけるAIの世界市場規模は？
→DataM Intelligence社は2023年の再生可能エネルギーにおけるAIの世界市場規模を8億4,500万米ドルと推定しています。
・再生可能エネルギーにおけるAIの世界市場予測は？
→DataM Intelligence社は2031年の再生可能エネルギーにおけるAIの世界市場規模を48億2,350万米ドルと予測しています。
・再生可能エネルギーにおけるAI市場の成長率は？
→DataM Intelligence社は再生可能エネルギーにおけるAIの世界市場が2024年～2031年に年平均24.3%成長すると予測しています。
・世界の再生可能エネルギーにおけるAI市場における主要企業は？
→DataM Intelligence社は「ABB, Alpiq, Amazon Web Services, Inc., Atos SE, FlexGen Power Systems, Inc., General Electric, Informatec Ltd., N-iX LTD, Schneider Electric and Siemens AG.など ...」をグローバル再生可能エネルギーにおけるAI市場の主要企業として認識しています。
※上記FAQの市場規模、市場予測、成長率、主要企業に関する情報は本レポートの概要を作成した時点での情報であり、納品レポートの情報と少し異なる場合があります。
