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市场调查报告书
商品编码
1679317

2030 年可再生能源市场人工智慧预测：按来源、部署模式、技术、应用、最终用户和地区进行的全球分析

Artificial Intelligence in Renewable Energy Market Forecasts to 2030 - Global Analysis By Source, Deployment Mode, Technology, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC 的数据，可再生能源领域的全球人工智慧 (AI) 市场规模预计在 2024 年将达到 9.405 亿美元，到 2030 年将达到 36.2231 亿美元，预测期内的复合年增长率为 25.2%。

可再生能源使用先进的演算法、机器学习和资料分析来最大限度地利用太阳能、风能和水力发电等再生能源来源的生产、分配和消耗。人工智慧将加强电网管理，预测能源需求，提高效率，并实现可再生能源基础设施的预测性维护。透过整合人工智慧，能源供应商可以降低成本、减少碳排放并提高可靠性，使可再生能源在向更清洁的全球能源系统过渡的过程中更加永续和可扩展。

电网优化需求日益增加

电力系统日益复杂以及可再生能源的整合需要先进的人工智慧解决方案来实现高效的电网管理。人工智慧有助于预测能源需求、管理供应并确保电网稳定。它还可以优化能源储存和分配，减少损失并提高效率。此外，人工智慧可以促进太阳能和风能等分散式能源资源的整合，使电网更加灵活。随着可再生能源的采用增加，对先进的电网优化工具的需求也随之增加。因此，人工智慧正成为现代能源网的重要组成部分。

AI模型的能耗

人工智慧模型所需的高运算能力会导致高能耗。这种能源消耗有时会抵消可再生能源系统所取得的效率提升。训练大型人工智慧模型需要大量的运算资源，这会导致能源消耗的增加。此外，人工智慧系统持续运作进行即时资料分析和决策，也会进一步增加能源消耗。这对人工智慧在可再生能源领域的永续性提出了挑战。平衡人工智慧的优势与其能源足迹仍然是一项重大挑战。

增加对智慧电网的投资

智慧电网采用先进的传感器、通讯网路和人工智慧演算法来改善能源管理。这些投资旨在提高电网可靠性、减少停电并提高效率。人工智慧透过实现预测性维护、需求预测和动态电网平衡在智慧电网中发挥着至关重要的作用。随着政府和私营部门对智慧电网基础设施的投资，对基于人工智慧的解决方案的需求预计将增长。这对可再生能源市场的人工智慧来说意味着巨大的成长机会。

资料安全和隐私问题

可再生能源发电中的人工智慧应用产生的大量资料引发了人们对资料安全和隐私的担忧。未授权存取敏感资料可能导致严重的安全漏洞和财务损失。此外，人工智慧与电网基础设施的结合使其成为网路攻击的潜在目标。为了防范这些威胁，采取强有力的网路安全措施至关重要。遵守资料保护条例为管理可再生能源中的人工智慧系统增加了额外的复杂性。解决这些安全挑战对于该领域广泛应用人工智慧至关重要。

COVID-19 的影响

疫情加速了可再生能源领域对包括人工智慧在内的数位技术的采用。人工智慧被用于远端监控、预测性维护、优化封锁期间的能源使用等等。对弹性和灵活性能源系统的需求变得更加清晰，推动了对人工智慧解决方案的投资。但疫情也凸显了能源基础设施面临中断的脆弱性。在这样的危机期间确保我们的能源系统的可靠性和稳定性至关重要。

预计水电产业将成为预测期内最大的产业

由于水力发电产业拥有完善的基础设施，并且具有整合人工智慧来优化营运和提高效率的潜力，预计在预测期内水力发电产业将占据最大的市场占有率。人工智慧可以改善水流管理，预测设备故障，并优化能源生产。由于能够产生大量可再生能源并且对环境的影响很小，因此水力发电是一个很有吸引力的选择。此外，人工智慧的融入可以进一步增强水力发电系统的永续性和可靠性。

预计预测期内住宅部门的复合年增长率最高。

预计预测期内住宅部门将出现最高成长率。支援人工智慧的能源管理系统可以优化能源使用、降低成本并为住宅增加便利性。屋顶太阳能等分散式可再生能源发电的兴起将进一步推动人工智慧解决方案在住宅环境中的应用。此外，政府对住宅可再生能源系统的激励和补贴也促进了这一成长。

占比最大的地区：

在预测期内，亚太地区预计将因对可再生能源基础设施的大量投资而占据最大的市场占有率。在政府倡议和优惠政策的支持下，中国和印度等国家在可再生能源应用方面处于主导。该地区对永续和减少碳排放的关注正在推动对能源管理人工智慧解决方案的需求。此外，该地区拥有主要的人工智慧技术供应商，进一步推动了市场成长。

复合年增长率最高的地区：

在预测期内，由于政府的大力支持、技术进步以及可再生能源解决方案的强劲市场，预计北美将呈现最高的复合年增长率。美国和加拿大正在大力投资人工智慧和可再生能源计划，以减少碳排放和提高能源效率。此外，北美主要人工智慧和可再生能源公司的存在也推动了这一高成长率。

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    • 对其他市场公司（最多 3 家公司）进行全面分析
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    • 根据产品系列、地理分布和策略联盟对主要企业基准化分析

目录

第一章执行摘要

第 2 章 前言

  • 概述
  • 相关利益者
  • 研究范围
  • 调查方法
    • 资料探勘
    • 资料分析
    • 资料检验
    • 研究途径
  • 研究资讯来源
    • 主要研究资讯来源
    • 二手研究资料资讯来源
    • 先决条件

第三章 市场走势分析

  • 驱动程式
  • 限制因素
  • 机会
  • 威胁
  • 技术分析
  • 应用分析
  • 最终用户分析
  • 新兴市场
  • COVID-19 的影响

第 4 章 波特五力分析

  • 供应商的议价能力
  • 买家的议价能力
  • 替代品的威胁
  • 新进入者的威胁
  • 竞争对手之间的竞争

第 5 章可再生能源中的人工智慧 (AI) 市场（按来源）

  • 风力发电
  • 水力发电
  • 太阳能
  • 地热能
  • 生质能源
  • 其他来源

第六章可再生能源中的人工智慧 (AI) 市场（按部署模式）

  • 本地
  • 云端基础

第 7 章可再生能源市场中的人工智慧 (AI) 技术

  • 机器学习 (ML)
  • 深度学习
  • 自然语言处理 (NLP)
  • 电脑视觉
  • 其他技术

第 8 章可再生能源中的人工智慧 (AI) 市场（按应用）

  • 能源预测
  • 能源储存管理
  • 电网管理和最佳化
  • 预测性维护
  • 需量反应管理
  • 能源交易
  • 其他用途

第 9 章。可再生能源市场中的人工智慧 (AI)（按最终用户划分）

  • 公共产业和发电公司
  • 可再生能源公司
  • 政府和公共部门
  • 工业领域
  • 住宅
  • 其他最终用户

第 10 章。按地区分類的可再生能源市场中的人工智慧 (AI)

  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 欧洲其他地区
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 其他亚太地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地区
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲地区

第十一章 重大进展

  • 协议、伙伴关係、合作和合资企业
  • 收购与合併
  • 新产品发布
  • 业务扩展
  • 其他关键策略

第十二章 公司概况

  • Google
  • Microsoft
  • IBM
  • Siemens
  • General Electric（GE）
  • Schneider Electric
  • ABB Ltd.
  • Tesla
  • Enel Group
  • NextEra Energy
  • Shell AI
  • GridBeyond

第1213章

  • Open Energi
  • Autogrid Systems
  • Verdigris Technologies
  • Innowatts
  • Uptake Technologies
  • Xcel Energy
  • UrbanChain
Product Code: SMRC28900

According to Stratistics MRC, the Global Artificial Intelligence (AI) in Renewable Energy Market is accounted for $940.50 million in 2024 and is expected to reach $3622.31 million by 2030 growing at a CAGR of 25.2% during the forecast period. Advanced algorithms, machine learning, and data analytics are used in renewable energy to maximize energy production, distribution, and consumption from renewable sources such as solar, wind, and hydro. AI enhances grid management, predicts energy demand, improves efficiency, and enables predictive maintenance of renewable energy infrastructure. By integrating AI, energy providers can minimize costs, reduce carbon emissions, and enhance reliability, making renewable energy more sustainable and scalable in the transition toward a cleaner global energy system.

Market Dynamics:

Driver:

Rising need for grid optimization

The increasing complexity of power systems and the integration of renewable energy sources necessitate advanced AI solutions for efficient grid management. AI can help in predicting energy demand, managing supply, and ensuring the stability of the grid. It can also optimize energy storage and distribution, reducing losses and improving efficiency. Moreover, AI can facilitate the integration of distributed energy resources like solar and wind, enhancing grid flexibility. As renewable energy adoption grows, so does the need for sophisticated grid optimization tools. Hence, AI is becoming indispensable in modern energy grids.

Restraint:

Energy consumption of AI models

The high computational power required for AI models can lead to significant energy consumption. This energy consumption can sometimes offset the efficiency gains achieved in renewable energy systems. Training large AI models requires substantial computational resources, which translates to increased energy use. Additionally, the continuous operation of AI systems for real-time data analysis and decision-making further adds to energy consumption. This poses a challenge for the sustainability of AI in the renewable energy sector. Balancing the benefits of AI with its energy footprint remains a critical concern.

Opportunity:

Increased investments in smart grids

Smart grids incorporate advanced sensors, communication networks, and AI algorithms to improve energy management. These investments aim to enhance grid reliability, reduce outages, and increase efficiency. AI plays a pivotal role in smart grids by enabling predictive maintenance, demand forecasting, and dynamic grid balancing. As governments and private sectors invest in smart grid infrastructure, the demand for AI-based solutions is set to rise. This presents a significant growth opportunity for AI in the renewable energy market.

Threat:

Data security and privacy concerns

The extensive data generated by AI applications in renewable energy raises concerns about data security and privacy. Unauthorized access to sensitive data can lead to significant security breaches and financial losses. Additionally, the integration of AI with grid infrastructure makes it a potential target for cyber-attacks. Ensuring robust cyber-security measures is crucial to protect against these threats. Compliance with data protection regulations further adds to the complexity of managing AI systems in renewable energy. Addressing these security challenges is vital for the widespread adoption of AI in this sector.

Covid-19 Impact

The pandemic has accelerated the adoption of digital technologies, including AI, in the renewable energy sector. AI has been leveraged for remote monitoring, predictive maintenance, and optimizing energy usage during lockdowns. The need for resilient and flexible energy systems has become more apparent, driving investments in AI solutions. However, the pandemic has also highlighted the vulnerability of energy infrastructure to disruptions. Ensuring the reliability and stability of energy systems during such crises is crucial.

The hydropower segment is expected to be the largest during the forecast period

The hydropower segment is expected to account for the largest market share during the forecast period, due to the established infrastructure and the potential for integrating AI to optimize operations and enhance efficiency. AI can improve water flow management, predict equipment failures, and optimize energy production. The ability to generate large amounts of renewable energy with minimal environmental impact makes hydropower an attractive option. Additionally, the integration of AI can further enhance the sustainability and reliability of hydropower systems.

The residential segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the residential segment is predicted to witness the highest growth rate. AI-enabled energy management systems can optimize energy usage, reducing costs and enhancing convenience for homeowners. The rise of distributed renewable energy generation, such as rooftop solar, further drives the adoption of AI solutions in residential settings. Additionally, government incentives and subsidies for residential renewable energy systems contribute to this growth.

Region with largest share:

During the forecast period, Asia Pacific region is expected to hold the largest market share, due to significant investments in renewable energy infrastructure. Countries like China and India are leading the charge in renewable energy adoption, supported by government initiatives and favourable policies. The region's focus on sustainable development and reducing carbon emissions drives the demand for AI solutions in energy management. Additionally, the presence of major AI technology providers in the region further boosts market growth.

Region with highest CAGR:

Over the forecast period, the North America region is anticipated to exhibit the highest CAGR, owing to strong government support, technological advancements, and a robust market for renewable energy solutions. The United States and Canada are investing heavily in AI and renewable energy projects, driven by the need to reduce carbon emissions and enhance energy efficiency. Additionally, the presence of leading AI and renewable energy companies in North America contributes to this high growth rate.

Key players in the market

Some of the key players profiled in the Artificial Intelligence (AI) in Renewable Energy Market include Google, Microsoft, IBM, Siemens, General Electric (GE), Schneider Electric, ABB Ltd., Tesla, Enel Group, NextEra Energy, Shell AI, GridBeyond, Kayrros, Open Energi, Autogrid Systems, Verdigris Technologies, Innowatts, Uptake Technologies, Xcel Energy, and UrbanChain.

Key Developments:

In January 2025, General Electric (GE) America's leading energy manufacturing company, is planning to invest nearly $600 million in its U.S. factories and facilities over the next two years to help meet the surging electricity demands around the world.

In July 2024, Siemens consortium partners with Bengaluru Metro Rail Corporation Limited for Rail Electrification technologies. Siemens Limited, as part of a consortium along with Rail Vikas Nigam Limited (RVNL), has secured an order from Bangalore Metro Rail Corporation Limited (BMRCL) for electrification of Bengaluru Metro Phase 2 project contributing to sustainable public transport in the city.

Sources Covered:

  • Wind Energy
  • Hydropower
  • Solar Energy
  • Geothermal Energy
  • Bioenergy
  • Other Sources

Deployment Modes Covered:

  • On-Premises
  • Cloud-Based

Technologies Covered:

  • Machine Learning (ML)
  • Deep Learning
  • Natural Language Processing (NLP)
  • Computer Vision
  • Other Technologies

Applications Covered:

  • Energy Forecasting
  • Energy Storage Management
  • Grid Management & Optimization
  • Predictive Maintenance
  • Demand Response Management
  • Energy Trading
  • Other Applications

End Users Covered:

  • Utilities & Power Generation Companies
  • Renewable Energy Companies
  • Government & Public Sector
  • Commercial & Industrial Sector
  • Residential
  • Other End Users

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2022, 2023, 2024, 2026, and 2030
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 Technology Analysis
  • 3.7 Application Analysis
  • 3.8 End User Analysis
  • 3.9 Emerging Markets
  • 3.10 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global Artificial Intelligence (AI) in Renewable Energy Market, By Source

  • 5.1 Introduction
  • 5.2 Wind Energy
  • 5.3 Hydropower
  • 5.4 Solar Energy
  • 5.5 Geothermal Energy
  • 5.6 Bioenergy
  • 5.7 Other Sources

6 Global Artificial Intelligence (AI) in Renewable Energy Market, By Deployment Mode

  • 6.1 Introduction
  • 6.2 On-Premises
  • 6.3 Cloud-Based

7 Global Artificial Intelligence (AI) in Renewable Energy Market, By Technology

  • 7.1 Introduction
  • 7.2 Machine Learning (ML)
  • 7.3 Deep Learning
  • 7.4 Natural Language Processing (NLP)
  • 7.5 Computer Vision
  • 7.6 Other Technologies

8 Global Artificial Intelligence (AI) in Renewable Energy Market, By Application

  • 8.1 Introduction
  • 8.2 Energy Forecasting
  • 8.3 Energy Storage Management
  • 8.4 Grid Management & Optimization
  • 8.5 Predictive Maintenance
  • 8.6 Demand Response Management
  • 8.7 Energy Trading
  • 8.8 Other Applications

9 Global Artificial Intelligence (AI) in Renewable Energy Market, By End User

  • 9.1 Introduction
  • 9.2 Utilities & Power Generation Companies
  • 9.3 Renewable Energy Companies
  • 9.4 Government & Public Sector
  • 9.5 Commercial & Industrial Sector
  • 9.6 Residential
  • 9.7 Other End Users

10 Global Artificial Intelligence (AI) in Renewable Energy Market, By Geography

  • 10.1 Introduction
  • 10.2 North America
    • 10.2.1 US
    • 10.2.2 Canada
    • 10.2.3 Mexico
  • 10.3 Europe
    • 10.3.1 Germany
    • 10.3.2 UK
    • 10.3.3 Italy
    • 10.3.4 France
    • 10.3.5 Spain
    • 10.3.6 Rest of Europe
  • 10.4 Asia Pacific
    • 10.4.1 Japan
    • 10.4.2 China
    • 10.4.3 India
    • 10.4.4 Australia
    • 10.4.5 New Zealand
    • 10.4.6 South Korea
    • 10.4.7 Rest of Asia Pacific
  • 10.5 South America
    • 10.5.1 Argentina
    • 10.5.2 Brazil
    • 10.5.3 Chile
    • 10.5.4 Rest of South America
  • 10.6 Middle East & Africa
    • 10.6.1 Saudi Arabia
    • 10.6.2 UAE
    • 10.6.3 Qatar
    • 10.6.4 South Africa
    • 10.6.5 Rest of Middle East & Africa

11 Key Developments

  • 11.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 11.2 Acquisitions & Mergers
  • 11.3 New Product Launch
  • 11.4 Expansions
  • 11.5 Other Key Strategies

12 Company Profiling

  • 12.1 Google
  • 12.2 Microsoft
  • 12.3 IBM
  • 12.4 Siemens
  • 12.5 General Electric (GE)
  • 12.6 Schneider Electric
  • 12.7 ABB Ltd.
  • 12.8 Tesla
  • 12.9 Enel Group
  • 12.10 NextEra Energy
  • 12.11 Shell AI
  • 12.12 GridBeyond

1213 Kayrros

  • 12.14 Open Energi
  • 12.15 Autogrid Systems
  • 12.16 Verdigris Technologies
  • 12.17 Innowatts
  • 12.18 Uptake Technologies
  • 12.19 Xcel Energy
  • 12.20 UrbanChain

List of Tables

  • Table 1 Global Artificial Intelligence (AI) in Renewable Energy Market Outlook, By Region (2022-2030) ($MN)
  • Table 2 Global Artificial Intelligence (AI) in Renewable Energy Market Outlook, By Source (2022-2030) ($MN)
  • Table 3 Global Artificial Intelligence (AI) in Renewable Energy Market Outlook, By Wind Energy (2022-2030) ($MN)
  • Table 4 Global Artificial Intelligence (AI) in Renewable Energy Market Outlook, By Hydropower (2022-2030) ($MN)
  • Table 5 Global Artificial Intelligence (AI) in Renewable Energy Market Outlook, By Solar Energy (2022-2030) ($MN)
  • Table 6 Global Artificial Intelligence (AI) in Renewable Energy Market Outlook, By Geothermal Energy (2022-2030) ($MN)
  • Table 7 Global Artificial Intelligence (AI) in Renewable Energy Market Outlook, By Bioenergy (2022-2030) ($MN)
  • Table 8 Global Artificial Intelligence (AI) in Renewable Energy Market Outlook, By Other Sources (2022-2030) ($MN)
  • Table 9 Global Artificial Intelligence (AI) in Renewable Energy Market Outlook, By Deployment Mode (2022-2030) ($MN)
  • Table 10 Global Artificial Intelligence (AI) in Renewable Energy Market Outlook, By On-Premises (2022-2030) ($MN)
  • Table 11 Global Artificial Intelligence (AI) in Renewable Energy Market Outlook, By Cloud-Based (2022-2030) ($MN)
  • Table 12 Global Artificial Intelligence (AI) in Renewable Energy Market Outlook, By Technology (2022-2030) ($MN)
  • Table 13 Global Artificial Intelligence (AI) in Renewable Energy Market Outlook, By Machine Learning (ML) (2022-2030) ($MN)
  • Table 14 Global Artificial Intelligence (AI) in Renewable Energy Market Outlook, By Deep Learning (2022-2030) ($MN)
  • Table 15 Global Artificial Intelligence (AI) in Renewable Energy Market Outlook, By Natural Language Processing (NLP) (2022-2030) ($MN)
  • Table 16 Global Artificial Intelligence (AI) in Renewable Energy Market Outlook, By Computer Vision (2022-2030) ($MN)
  • Table 17 Global Artificial Intelligence (AI) in Renewable Energy Market Outlook, By Other Technologies (2022-2030) ($MN)
  • Table 18 Global Artificial Intelligence (AI) in Renewable Energy Market Outlook, By Application (2022-2030) ($MN)
  • Table 19 Global Artificial Intelligence (AI) in Renewable Energy Market Outlook, By Energy Forecasting (2022-2030) ($MN)
  • Table 20 Global Artificial Intelligence (AI) in Renewable Energy Market Outlook, By Energy Storage Management (2022-2030) ($MN)
  • Table 21 Global Artificial Intelligence (AI) in Renewable Energy Market Outlook, By Grid Management & Optimization (2022-2030) ($MN)
  • Table 22 Global Artificial Intelligence (AI) in Renewable Energy Market Outlook, By Predictive Maintenance (2022-2030) ($MN)
  • Table 23 Global Artificial Intelligence (AI) in Renewable Energy Market Outlook, By Demand Response Management (2022-2030) ($MN)
  • Table 24 Global Artificial Intelligence (AI) in Renewable Energy Market Outlook, By Energy Trading (2022-2030) ($MN)
  • Table 25 Global Artificial Intelligence (AI) in Renewable Energy Market Outlook, By Other Applications (2022-2030) ($MN)
  • Table 26 Global Artificial Intelligence (AI) in Renewable Energy Market Outlook, By End User (2022-2030) ($MN)
  • Table 27 Global Artificial Intelligence (AI) in Renewable Energy Market Outlook, By Utilities & Power Generation Companies (2022-2030) ($MN)
  • Table 28 Global Artificial Intelligence (AI) in Renewable Energy Market Outlook, By Renewable Energy Companies (2022-2030) ($MN)
  • Table 29 Global Artificial Intelligence (AI) in Renewable Energy Market Outlook, By Government & Public Sector (2022-2030) ($MN)
  • Table 30 Global Artificial Intelligence (AI) in Renewable Energy Market Outlook, By Commercial & Industrial Sector (2022-2030) ($MN)
  • Table 31 Global Artificial Intelligence (AI) in Renewable Energy Market Outlook, By Residential (2022-2030) ($MN)
  • Table 32 Global Artificial Intelligence (AI) in Renewable Energy Market Outlook, By Other End Users (2022-2030) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.