再生可能エネルギーにおけるAIの世界市場：2024年～2031年

概要

再生可能エネルギーにおけるAIの世界市場は、2023年に8億4,500万米ドルに達し、2031年には48億2,350万米ドルに達すると予測され、予測期間中のCAGRは24.32%で成長すると予測されます。

再生可能エネルギーにおけるAI市場は、持続可能なエネルギー源に対する高い需要、高度な人工知能技術、二酸化炭素排出量削減に向けた政府政策の増加により、初期成長段階にあります。再生可能エネルギーにおけるAIには、グリッド管理、エネルギー予測、予防保守などのアプリケーションが含まれ、太陽光発電、風力発電、水力発電などのさまざまな再生可能エネルギー源の統合も含まれます。

気候変動に対する意識の高まりと持続可能なエネルギー源に対する緊急のニーズは、再生可能エネルギーにおけるAI市場の重要な促進要因です。国際再生可能エネルギー機関（IRENA）によると、再生可能エネルギーは、現在の目標が達成された場合、2050年までに世界の電力需要の最大86％を満たす可能性があり、再生可能エネルギーインフラを最適化するためのAIの潜在的需要が強調されています。

アジア太平洋は再生可能エネルギーにおけるAIの急成長市場として浮上しており、中国、日本、インドなどの国々がグリーンエネルギーとAI技術に多額の投資を行っています。中国の第14次再生可能エネルギー5ヵ年計画とIEAのElectricity 2024報告書によれば、2025年までに総エネルギー消費の33％が再生可能エネルギーになると予想されています。同様に、インドの国家電力計画（トランスミッション）は、2030年までに500GWの再生可能エネルギー容量を目標に掲げており、送電網の安定性を監視し、エネルギー貯蔵を改善するためにAIを重視しています。

ダイナミクス

予知保全とエネルギー予測のためのデータ分析

AIによる予知保全は、ダウンタイムを緩和し、再生可能エネルギーの寿命を延ばす上で重要な要素です。欧州委員会が言及したように、AIアナリティクスは、起こりうる故障を予測し、介入を効率的にスケジュールする予測モデルの能力により、欧州全域の風力発電所のメンテナンスコストを約15～20％削減します。変動する再生可能エネルギー源に基づく発電をより正確に予測し、リアルタイムの負荷管理に貢献するAI強化エネルギー発電予測の実装により、AIはエネルギー発送プロセスの効率を向上させています。

さらに、「グリーン」などの政府政策が、再生可能エネルギー産業におけるAIの利用を後押ししています。例えば、欧州連合（EU）のグリーン・ディールは、2030年までに炭素排出量を少なくとも正味ゼロにすることを目標としており、エネルギー・エコシステム内でのデジタル技術の開発と応用を奨励しています。

民間セクターの投資と技術提携

民間セクターは、AIを活用した再生可能エネルギープロジェクトに多額の投資を行っています。例えば、グーグルはエネルギー部門と協力し、ソーラーパネルの効率や送電網の配電を改善するためにAI技術を応用しています。世界経済フォーラムは、大手テクノロジー企業とエネルギー企業が手を組み、再生可能エネルギーの人工知能ソリューションを強化することで、エネルギー企業が今後数年間で人工知能技術への支出を増やすと予測しています。

同様に、米国エネルギー省は、人工知能への資金提供と、エネルギー管理におけるAI能力を認識した再生可能エネルギー技術の促進に投資しています。IEAによると、グリッドベースのデジタル技術への投資は2015年から50％以上増加し、再生可能エネルギーにおけるAI統合に備えて、2023年までにグリッド投資全体の19％を占めると予測されています。

規制と労働力の課題

再生可能エネルギー部門は、人工知能（AI）技術の展開を阻害する重大な規制と労働力の課題に直面しています。情報、特に個人データの保護を目的とした法律への規制遵守。例えば、EUのGDPRは、AIシステムのためにエネルギー消費データを集計・利用することを困難にしています。同法によると、個人データをどのような目的で使用するにしても、インフォームドコンセントを得なければならず、AI開発者はデータのために法律の迷路に迷い込むことになります。

同様に、再生可能エネルギー業界も、人工知能やデータ分析に従事できる人材が不足しています。国際労働機関（ILO）は、同産業が人工知能システムの作成・運用能力の労働力不足に直面していると推定しています。このスキルギャップは、拡大や効率化を制限し、AIベースのシステムを導入することをより困難にしています。

目次

第1章 調査手法と調査範囲

第2章 定義と概要

第3章 エグゼクティブサマリー

第4章 市場力学

• 影響要因
  • 促進要因
    • 予知保全とエネルギー予測のためのデータ分析
    • クリーンエネルギー技術に関する政府の政策と投資
  • 抑制要因
    • 規制と労働力の課題
  • 機会
  • 影響分析

第5章 産業分析

• ポーターのファイブフォース分析
• サプライチェーン分析
• 価格分析
• 規制分析
• ロシア・ウクライナ戦争の影響分析
• DMIの見解

第6章 展開別

• オンプレミス
• クラウドベース

第7章 コンポーネント別

• ソリューション
• サービス

第8章 用途別

• ロボット工学
• スマートグリッド管理
• 需要予測
• 安全・セキュリティ・インフラ
• その他

第9章 エンドユーザー別

• エネルギートランスミッション
• エネルギー生成
• エネルギー分配
• ユーティリティ

第10章 サスティナビリティ分析

• 環境分析
• 経済分析
• ガバナンス分析

第11章 地域別

• 北米
  • 米国
  • カナダ
  • メキシコ
• 欧州
  • ドイツ
  • 英国
  • フランス
  • イタリア
  • スペイン
  • その他欧州地域
• 南米
  • ブラジル
  • アルゼンチン
  • その他南米
• アジア太平洋地域
  • 中国
  • インド
  • 日本
  • オーストラリア
  • その他アジア太平洋地域
• 中東・アフリカ

第12章 競合情勢

• 競合シナリオ
• 市況・シェア分析
• M&A分析

第13章 企業プロファイル

• ABB
  • 会社概要
  • タイプポートフォリオと概要
  • 財務概要
  • 主な発展
• Alpiq
• Amazon Web Services, Inc.
• Atos SE
• FlexGen Power Systems, Inc.
• General Electric
• Informatec Ltd.
• N-iX LTD
• Schneider Electric
• Siemens AG

第14章 付録

Overview

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
  • 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

Table of Contents

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

14. Appendix

• 14.1. About Us and Services
• 14.2. Contact Us