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再生能源人工智慧市場至2030年的預測：按來源、部署模式、技術、應用、最終用戶和地區的全球分析

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個工作天內

價格

根據 Stratistics MRC 的資料，再生能源領域的全球人工智慧（AI）市場規模在2024年達到 9.405億美元，預計到2030年將達到 36.2231億美元，預測期內的年複合成長率為 25.2%。

再生能源使用先進的演算法、機器學習和資料分析來最大限度地利用太陽能、風能和水力發電等再生能源來源的生產、分配和消耗。人工智慧將加強電網管理，預測能源需求，提高效率，並實現再生能源基礎設施的預測性維護。透過整合人工智慧，能源供應商可以降低成本、減少碳排放並提高可靠性，使再生能源在向更清潔的全球能源系統過渡的過程中更加永續和可擴展。

電網最佳化需求日益增加

電力系統日益複雜以及再生能源的整合需要先進的人工智慧解決方案來實現高效的電網管理。人工智慧有助於預測能源需求、管理供應並確保電網穩定。它還可以最佳化能源儲存和分配，減少損失並提高效率。此外，人工智慧可以促進太陽能和風能等分散式能源資源的整合，使電網更加靈活。隨著再生能源的採用增加，對先進的電網最佳化工具的需求也隨之增加。因此，人工智慧正成為現代能源網的重要組成部分。

AI模型的能耗

人工智慧模型所需的高運算能力會導致高能耗。這種能源消耗有時會抵消再生能源系統所取得的效率提升。訓練大型人工智慧模型需要大量的運算資源，這會導致能源消耗的增加。此外，人工智慧系統持續運作進行即時資料分析和決策，也會進一步增加能源消耗。這對人工智慧在再生能源領域的永續性提出了挑戰。平衡人工智慧的優勢與其能源足跡仍然是一項重大挑戰。

增加對智慧電網的投資

智慧電網採用先進的感測器、通訊網路和人工智慧演算法來改善能源管理。這些投資目的是提高電網可靠性、減少停電並提高效率。人工智慧透過實現預測性維護、需求預測和動態電網平衡在智慧電網中發揮著非常重要的作用。隨著政府和私營部門對智慧電網基礎設施的投資，對基於人工智慧的解決方案的需求預計將成長。這對再生能源市場的人工智慧來說意味著巨大的成長機會。

資料安全和隱私問題

再生能源發電中的人工智慧應用產生的大量資料引發了人們對資料安全和隱私的擔憂。未授權存取敏感資料可能導致嚴重的安全漏洞和財務損失。此外，人工智慧與電網基礎設施的結合使其成為網路攻擊的潛在目標。為了防範這些威脅，採取強力的網路安全措施非常重要。遵守資料保護條例為管理再生能源中的人工智慧系統增加了額外的複雜性。解決這些安全挑戰對於該領域廣泛應用人工智慧非常重要。

COVID-19 的影響

疫情加速了再生能源領域對包括人工智慧在內的數位技術的採用。人工智慧用於遠端監控、預測性維護、最佳化封鎖期間的能源使用等等。對彈性和靈活性能源系統的需求變得更加清晰，推動了對人工智慧解決方案的投資。但疫情也凸顯了能源基礎設施面臨中斷的脆弱性。在這樣的危機期間確保能源系統的可靠性和穩定性非常重要。

預計水電產業將成為預測期內最大的產業

由於水力發電產業擁有完善的基礎設施，並且具有整合人工智慧來最佳化營運和提高效率的潛力，預計在預測期內水力發電產業將佔據最大的市場佔有率。人工智慧可以改善水流管理，預測設備故障，並最佳化能源生產。由於能夠產生大量再生能源並且對環境的影響很小，因此水力發電是一個很有吸引力的選擇。此外，人工智慧的融入可以進一步增強水力發電系統的永續性和可靠性。

預計預測期內住宅部門的年複合成長率最高。

預計預測期內住宅部門將出現最高成長率。支援人工智慧的能源管理系統可以最佳化能源使用、降低成本並為住宅增加便利性。屋頂太陽能等分散式再生能源發電的興起將進一步推動人工智慧解決方案在住宅環境中的應用。此外，政府對住宅再生能源系統的激勵和補貼也促進了該成長。

佔比最大的地區：

在預測期內，亞太地區預計將因對再生能源基礎設施的大量投資而佔據最大的市場佔有率。在政府舉措和優惠政策的支持下，中國和印度等國家在再生能源應用方面處於主導。該地區對永續和減少碳排放的關注推動對能源管理人工智慧解決方案的需求。此外，該地區擁有主要的人工智慧技術供應商，進一步推動了市場成長。

年複合成長率最高的地區：

在預測期內，由於政府的大力支持、技術進步以及再生能源解決方案的強勁市場，預計北美將呈現最高的年複合成長率。美國和加拿大大力投資人工智慧和再生能源計劃，以減少碳排放和提高能源效率。此外，北美主要人工智慧和再生能源公司的存在也推動了這一高成長率。

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目錄

第1章 執行摘要

第2章 前言

  • 概述
  • 相關利益者
  • 研究範圍
  • 調查方法
    • 資料探勘
    • 資料分析
    • 資料檢驗
    • 研究途徑
  • 研究資訊來源
    • 主要研究資訊來源
    • 二手研究資料資訊來源
    • 先決條件

第3章 市場走勢分析

  • 促進因素
  • 限制因素
  • 機會
  • 威脅
  • 技術分析
  • 應用分析
  • 最終用戶分析
  • 新興市場
  • COVID-19 的影響

第4章 波特五力分析

  • 供應商的議價能力
  • 買家的議價能力
  • 替代品的威脅
  • 新進入者的威脅
  • 競爭敵對

第5章 再生能源中的人工智慧（AI）市場（依來源）

  • 風力發電
  • 水力發電
  • 太陽能
  • 地熱能
  • 生質能源
  • 其他來源

第6章 再生能源中的人工智慧（AI）市場（依部署模式）

  • 本地
  • 雲端

第7章 再生能源市場中的人工智慧（AI）技術

  • 機器學習（ML）
  • 深度學習
  • 自然語言處理（NLP）
  • 電腦視覺
  • 其他技術

第8章 再生能源中的人工智慧（AI）市場（依應用）

  • 能源預測
  • 能源儲存管理
  • 電網管理和最佳化
  • 預測性維護
  • 需量反應管理
  • 能源交易
  • 其他用途

第9章 再生能源市場中的人工智慧（AI）（依最終用戶）

  • 公共產業和發電公司
  • 再生能源公司
  • 政府和公共部門
  • 工業領域
  • 住宅
  • 其他最終用戶

第10章 再生能源市場中的人工智慧（AI）（依地區）

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 義大利
    • 法國
    • 西班牙
    • 歐洲其他地區
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 其他亞太地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地區
  • 中東和非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 卡達
    • 南非
    • 其他中東和非洲地區

第11章 重大進展

  • 協議、夥伴關係、合作和合資企業
  • 收購與合併
  • 新產品發布
  • 業務擴展
  • 其他關鍵策略

第12章 公司概況

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

1213 Kayrros

  • 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.