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市場調查報告書

商品編碼

1594862

再生能源市場中的全球人工智慧 - 2024-2031

Global AI in Renewable Energy Market - 2024-2031

出版日期: 2024年11月21日 | 出版商:

DataM Intelligence | 英文 205 Pages | 商品交期: 最快1-2個工作天內

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簡介目錄

概述

2023年，全球人工智慧再生能源市場規模達8.45億美元，預計2031年將達到48.235億美元，預測期內複合年成長率為24.32%。

由於對永續能源、先進人工智慧技術的高需求以及政府減少碳足跡的政策力度加大，再生能源領域的人工智慧市場正處於早期成長階段。再生能源中的人工智慧包括電網管理、能源預測、預防性維護等應用，還包括太陽能、風能和水力發電等各種再生能源的整合。

人們對氣候變遷的認知不斷提高以及對永續能源的迫切需求是再生能源市場人工智慧的重要驅動力。根據國際再生能源機構(IRENA) 的數據，如果當前目標得以實現，到2050 年再生能源可滿足全球高達86% 的電力需求，這凸顯了人工智慧最佳化再生能源基礎設施的潛在需求。

亞太地區正成為再生能源領域人工智慧成長最快的市場，中國、日本和印度等國家對綠色能源和人工智慧技術進行了大量投資。根據中國再生能源「十四五」規劃和國際能源總署《電力2024》報告，到2025年，再生能源預計將佔能源消耗總量的33%。同樣，印度國家電力計畫（輸電）設定了 2030 年再生能源裝置容量達到 500 吉瓦的目標，強調利用人工智慧來監控電網穩定性並改善能源儲存。

動力學

用於預測性維護和能源預測的數據分析

人工智慧的預測性維護是減少停機時間和延長再生能源壽命的關鍵組成部分。正如歐盟委員會所提到的，由於預測模型能夠預見可能發生的故障並有效安排干預措施，人工智慧分析將使整個歐洲的風電場維護成本降低約 15-20%。隨著人工智慧增強型能源預測的實施，人工智慧正在提高能源調度流程的效率，其中更精確地預測基於可變再生能源的發電量，有助於即時負載管理。

此外，「綠色」等政府政策推動了人工智慧在再生能源產業的使用。例如，歐盟綠色協議的目標是至少2030年將碳排放量削減至淨零，鼓勵能源生態系統內數位技術的開發和應用。

私部門投資和技術夥伴關係

私營部門正在大力投資人工智慧驅動的再生能源項目。例如，Google一直在與能源部門合作，應用人工智慧技術，以提高太陽能電池板的效率和電網的電力分配。世界經濟論壇預計，能源公司將在未來幾年增加人工智慧技術的支出，大型科技公司和能源公司聯手增強再生能源人工智慧解決方案。

同樣，美國能源部也投資資助人工智慧和推動再生能源技術，認可人工智慧在能源管理方面的能力。 IEA表示，基於電網的數位技術投資較2015年增加了50%以上，預計到2023年將佔電網總投資的19%，為再生能源中的人工智慧整合做好準備。

監管和勞動力挑戰

再生能源產業面臨阻礙人工智慧 (AI) 技術部署的重大法規和勞動力挑戰。遵守旨在保護資訊（尤其是個人資料）的法律。例如，歐盟 GDPR 使得人工智慧系統的能源消耗資料難以匯總和使用。根據該法律，任何人都必須獲得知情同意才能將個人資料用於任何目的，這使得人工智慧開發人員在處理資料時面臨一系列法律迷宮。

同樣，再生能源產業也面臨人工智慧和資料分析人才的短缺。國際勞工組織（ILO）估計，該產業在創建和運作人工智慧系統方面面臨勞動力短缺。這種技能差距限制了擴展或效率提升，使得實施基於人工智慧的系統更具挑戰性。

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
- US
- Canada
- Mexico
Europe
- Germany
- UK
- France
- Italy
- Spain
- Rest of Europe
South America
- Brazil
- Argentina
- Rest of South America
Asia-Pacific
- China
- India
- Japan
- Australia
- 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

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