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绿色能源

市场调查报告书

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1594862

再生能源市场中的全球人工智慧 - 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个工作天内

价格

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简介目录

概述

2023年，全球人工智慧再生能源市场规模达8.45亿美元，预计2031年将达到48.235亿美元，预测期内复合年增长率为24.32%。

由于对永续能源、先进人工智慧技术的高需求以及政府减少碳足迹的政策力度加大，再生能源领域的人工智慧市场正处于早期成长阶段。再生能源中的人工智慧包括电网管理、能源预测、预防性维护等应用，还包括太阳能、风能和水力发电等各种再生能源的整合。

人们对气候变迁的认识不断提高以及对永续能源的迫切需求是再生能源市场人工智慧的重要驱动力。根据国际再生能源机构(IRENA) 的数据，如果当前目标得以实现，到2050 年再生能源可满足全球高达86% 的电力需求，这凸显了人工智慧优化再生能源基础设施的潜在需求。

亚太地区正成为再生能源领域人工智慧成长最快的市场，中国、日本和印度等国家对绿色能源和人工智慧技术进行了大量投资。根据中国再生能源「十四五」规划和国际能源总署《电力2024》报告，到2025年，再生能源预计将占能源消耗总量的33%。同样，印度国家电力计画（输电）设定了 2030 年再生能源装置容量达到 500 吉瓦的目标，强调利用人工智慧来监控电网稳定性并改善能源储存。

动力学

用于预测性维护和能源预测的数据分析

人工智慧的预测性维护是减少停机时间和延长再生能源寿命的关键组成部分。正如欧盟委员会所提到的，由于预测模型能够预见可能发生的故障并有效安排干预措施，人工智慧分析将使整个欧洲的风电场维护成本降低约 15-20%。随着人工智慧增强型能源预测的实施，人工智慧正在提高能源调度流程的效率，其中更精确地预测基于可变再生能源的发电量，有助于即时负载管理。

此外，「绿色」等政府政策推动了人工智慧在再生能源产业的使用。例如，欧盟绿色协议的目标是至少2030年将碳排放量削减至净零，鼓励能源生态系统内数位技术的开发和应用。

私部门投资和技术伙伴关係

私营部门正在大力投资人工智慧驱动的再生能源项目。例如，Google一直在与能源部门合作，应用人工智慧技术，以提高太阳能电池板的效率和电网的电力分配。世界经济论坛预计，能源公司将在未来几年增加人工智慧技术的支出，大型科技公司和能源公司联手增强再生能源人工智慧解决方案。

同样，美国能源部也投资资助人工智慧和推动再生能源技术，认可人工智慧在能源管理方面的能力。 IEA表示，基于电网的数位技术投资较2015年增加了50%以上，预计到2023年将占电网总投资的19%，为再生能源中的人工智慧整合做好准备。

监管和劳动力挑战

再生能源产业面临阻碍人工智慧 (AI) 技术部署的重大法规和劳动力挑战。遵守旨在保护资讯（尤其是个人资料）的法律。例如，欧盟 GDPR 使得人工智慧系统的能源消耗资料难以汇总和使用。根据该法律，任何人都必须获得知情同意才能将个人资料用于任何目的，这使得人工智慧开发人员在处理资料时面临一系列法律迷宫。

同样，再生能源产业也面临人工智慧和资料分析人才的短缺。国际劳工组织（ILO）估计，该产业在创建和运作人工智慧系统方面面临劳动力短缺。这种技能差距限制了扩展或效率提升，使得实施基于人工智慧的系统更具挑战性。

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