재생에너지용 인공지능 시장 : 전개 방식별, 구성요소 유형별, 최종 사용 산업별 - 세계 기회 분석 및 산업 예측(2023-2032년)
Artificial Intelligence in Renewable Energy Market By Deployment Type, By Component Type, By End-Use Industry : Global Opportunity Analysis and Industry Forecast, 2023-2032
상품코드:1396960
리서치사:Allied Market Research
발행일:2023년 10월
페이지 정보:영문 242 Pages
라이선스 & 가격 (부가세 별도)
한글목차
재생에너지용 인공지능 세계 시장 규모는 2022년 6억 달러로 평가되며 2023년부터 2032년까지 23.2%의 CAGR로 성장하여 2032년에는 46억 달러에 달할 것으로 예상됩니다.
재생에너지용 인공지능은 재생에너지의 발전, 저장, 배전, 소비를 개선하기 위해 컴퓨터 마스터링 및 사실 분석과 같은 인공지능 기술을 적용하는 것을 포함합니다. 예측 모델링, 최적화 알고리즘, 독립 관리 시스템 등 다양한 기술을 포괄하여 태양광, 풍력, 수력 등 재생 가능 자원의 활용을 극대화합니다.
AI 예측 모델링은 태양 복사 조도와 기후 패턴을 정확하게 예측하여 더 나은 에너지 기술 예측을 가능하게 하며, AI는 태양광 패널과 추적 장치의 배치를 최적화하여 하루 중 가장 많은 햇빛을 이용할 수 있도록 하여 태양광 시스템의 평균 효율을 향상시킵니다.
풍력발전소에서는 AI의 활용이 두드러집니다. 기계 학습 알고리즘은 방대한 양의 기록을 분석하여 바람의 패턴과 터빈의 성능을 예측합니다. 이는 터빈의 보호 스케줄링을 개선하고 자체 에너지 생산량을 증가시키며, AI는 배전망을 관리하는 데 도움이 됩니다. 머신 마스터링 알고리즘은 전력 수요를 예측하고 전력망에서 실제 고장이나 장애를 발견하기 위해 실시간 사실을 분석합니다. 인공 두뇌는 전력망 운영자에게 능동적인 대응을 가능하게 하여 소비자에게 안정적이고 신뢰할 수 있는 재생에너지를 공급할 수 있도록 합니다.
AI는 새와 박쥐를 보호하는 데도 사용됩니다. 첨단 컴퓨터 비전 구조는 공장 근처에 새가 있다는 것을 인식하고 충돌을 방지하기 위해 새의 움직임을 잠시 멈추게 할 수 있습니다. 이 최신 방법은 풍력 발전소와 관련된 생태학적 문제를 해결하고 있으며, AI는 전력망의 복원력을 높이는 데 도움이 되고 있습니다. 인공지능은 또한 발생할 수 있는 사이버 공격을 예측하고 완화하여 재생 가능 전력 인프라의 무결성과 안전성을 보장할 수 있습니다.
인공지능은 재생에너지로 구동되는 담수화 방식을 개선하는 데 활용되고 있습니다. AI 알고리즘은 담수화 플랜트 운영을 최적화하여 전력 소비와 요금을 줄이면서 담수 생산량을 증가시키고, AI는 수역에 떠 있는 부유식 태양광발전소 계획과 최적화에 활용되고 있습니다. 이러한 농장은 토지가 제한된 지역에서 지속가능한 솔루션으로 부상하고 있으며, AI는 가장 효율적인 레이아웃과 보호 전략을 결정하는 데 도움을 주고 있습니다.
목차
제1장 서론
제2장 주요 요약
제3장 시장 개요
시장 정의와 범위
주요 조사 결과
영향요인
주요 투자 기회
Porter's Five Forces 분석
시장 역학
성장 촉진요인
재생에너지 기술 채용 증가
AI에 의한 재생에너지 효율 향상
성장 억제요인
인공지능 구동 기술의 고비용
기회
에너지 저장 시스템 강화를 위한 AI 주도의 최적화
스마트 그리드의 에너지 분배와 에너지 저장
밸류체인 분석
주요 규제 분석
특허 상황
규제 가이드라인
제4장 재생에너지용 인공지능 시장 : 전개 방식별
개요
구내
클라우드
제5장 재생에너지용 인공지능 시장 : 컴포넌트 유형별
개요
솔루션
서비스
제6장 재생에너지용 인공지능 시장 : 최종 이용 산업별
개요
에너지 발전
에너지 송전
에너지 배급
유틸리티
제7장 재생에너지용 인공지능 시장 : 지역별
개요
북미
미국
캐나다
멕시코
유럽
독일
프랑스
영국
스페인
이탈리아
기타
아시아태평양
중국
일본
인도
한국
호주
기타
라틴아메리카, 중동 및 아프리카
브라질
남아프리카공화국
사우디아라비아
기타
제8장 경쟁 상황
서론
주요 성공 전략
주요 10개사의 제품 매핑
경쟁 대시보드
경쟁 히트맵
주요 기업의 포지셔닝, 2022년
제9장 기업 개요
Alpiq Holding Ltd.
App Orchid
Atos SE
Enel Spa
Enphase Energy
Flex Ltd.
General Electric
Origami
Siemens AG
Vestas
ksm
영문 목차
영문목차
The global artificial intelligence in renewable energy market size was valued at $0.6 billion in 2022, and is projected to reach $4.6 billion by 2032, growing at a CAGR of 23.2% from 2023 to 2032.
Artificial intelligence in renewable energy involves the application of AI techniques, such as computer mastering and facts analytics, to improve the generation, storage, distribution, and consumption of renewable electricity sources. It encompasses a wide range of technologies, including predictive modeling, optimization algorithms, and independent manage systems, to maximize the utilization of renewable resources like solar, wind, and hydropower.
AI-driven predictive modeling can accurately forecast solar irradiance and climate patterns, enabling better energy technology forecasts. AI helps optimize the positioning of photo voltaic panels and trackers to seize most sunlight at some stage in the day, for that reason improving the average efficiency of solar strength systems.
Wind farms gain notably from AI applications. Machine studying algorithms analyze sizeable amounts of records to predict wind patterns and turbine performance. This approves for higher turbine protection scheduling and greater unique energy generation. AI helps in the management of electricity distribution grids. Machine mastering algorithms analyze real-time facts to predict power demand and pick out practicable faults or disturbances in the grid. Artificial brain permits grid operators to respond proactively, making sure a stable and reliable furnish of renewable power to consumers.
AI has been employed to protect birds and bats. Advanced pc vision structures can realize the presence of birds close to mills and briefly shut them down to forestall collisions. This modern method addresses ecological issues related with wind farms. AI is instrumental in enhancing the resilience of strength grids. Artificial intelligence structures can additionally predict and mitigate doable cyberattacks, making sure the integrity and security of renewable power infrastructure.
Artificial intelligence is being used to improve desalination approaches powered by means of renewable energy. AI algorithms optimize the operation of desalination plants, decreasing electricity consumption and charges whilst increasing freshwater production. AI is employed in the plan and optimization of floating photo voltaic farms on our bodies of water. These farms are rising as a sustainable solution for land-constrained regions, and AI helps decide the most efficient layout and protection strategies.
The artificial intelligence in renewable energy market growth is segmented into deployment type, component type, end-use industry, and region. On the basis of deployment type, the market is bifurcated into on-premises and cloud. On the basis of component type, the market is divided into solution, and service. On the basis of end-use industry, the market is classified into energy generation, energy transmission, energy distribution, and utilities. On the basis of region, the market is studied across North America, Europe, Asia-Pacific, and LAMEA.
By deployment type, on-premises is the fastest-growing segment in the artificial intelligence in renewable energy industry in 2022. AI systems can analyze real-time records from on-premises renewable energy sources like solar panels and wind turbines. They can optimize energy production by means of adjusting the perspective of photo voltaic panels, the orientation of wind turbines, and battery storage usage to maximize power technology and consumption. AI models can furnish correct strength production forecasts primarily based on climate conditions, historical data, and different factors. This helps on-premises customers design their power consumption and storage greater effectively.
Based on component type, service is the fastest-growing segment in the AI in renewable energy market in 2022. AI-based grid administration systems can stability the intermittent nature of renewable strength sources. These structures use real-time statistics to control energy distribution efficiently, ensuring a secure supply. AI can optimize electricity storage systems, such as batteries. It can determine when to store excess energy and when to release it, making the most of renewable energy production.
Based on end-use industry, energy distribution is the fastest-growing segment in the AI in renewable energy market in 2022. AI can help manage energy storage systems, such as batteries. Machine gaining knowledge of algorithms can optimize the charging and discharging of batteries primarily based on grid demand and renewable strength availability, maximizing the use of clean power and reducing reliance on fossil fuels. AI can play a crucial role in improving the resilience of strength grids in opposition to disruptions, including those brought about by using extreme climate events. Predictive maintenance algorithms can discover attainable issues in the grid infrastructure earlier than they lead to outages.
Region-wise Asia-Pacific is the fastest-growing and dominated in the market in 2022. In the Asia-Pacific region, hydropower performs a good sized role as a renewable energy source, mainly in countries like China and Japan. The implementation of AI-powered analytics has tested to be worthwhile in optimizing the management of water float within hydropower plants. This technology ensures a constant provide of electricity while simultaneously minimizing the poor have an effect on on aquatic ecosystems. Furthermore, AI-driven building management structures are gaining recognition throughout Asia-Pacific nations. These systems leverage real-time records and occupancy patterns to effectively manipulate lighting, heating, and cooling in each industrial and residential buildings. This not solely reduces electricity consumption however also results in price financial savings on electricity payments for consumers.
The major players operating in the global artificial intelligence in renewable energy market share diversified into Alpiq, AppOrchid Inc., ATOS SE, Enel Green Power, Enphase Energy, Flex Ltd., General Electric, Origami Energy Ltd., Siemens AG, and Vestas.
Key Market Insights
Cloud was the largest revenue generator in 2022, comprising over half of the market share in 2022.
The solutions segment is dominated by component type, contributing to almost two-thirds of the market share and showing a 32.0% Compound Annual Growth Rate (CAGR) in 2022.
In terms of end-use industries, utilities held the top position, with a 22.8% CAGR in the market.
The Asia-Pacific region led in revenue contribution, representing two-thirds of the global market share in 2022.
Key Benefits For Stakeholders
This report provides a quantitative analysis of the market segments, current trends, estimations, and dynamics of the artificial intelligence in renewable energy market analysis from 2022 to 2032 to identify the prevailing artificial intelligence in renewable energy market opportunities.
The market research is offered along with information related to key drivers, restraints, and opportunities.
Porter's five forces analysis highlights the potency of buyers and suppliers to enable stakeholders make profit-oriented business decisions and strengthen their supplier-buyer network.
In-depth analysis of the artificial intelligence in renewable energy market scope assists to determine the prevailing market opportunities.
Major countries in each region are mapped according to their revenue contribution to the global market.
Market player positioning facilitates benchmarking and provides a clear understanding of the present position of the market players.
The report includes the analysis of the regional as well as global artificial intelligence in renewable energy market trends, key players, market segments, application areas, and market growth strategies.
Additional benefits you will get with this purchase are:
Quarterly Update and* (only available with a corporate license, on listed price)
5 additional Company Profile of client Choice pre- or Post-purchase, as a free update.
Free Upcoming Version on the Purchase of Five and Enterprise User License.
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24-hour priority response*
Free Industry updates and white papers.
Possible Customization with this report (with additional cost and timeline, please talk to the sales executive to know more)
End user preferences and pain points
Industry life cycle assessment, by region
Investment Opportunities
Product Benchmarking / Product specification and applications
Product Life Cycles
Upcoming/New Entrant by Regions
Technology Trend Analysis
Consumer Preference and Product Specifications
Market share analysis of players by products/segments
New Product Development/ Product Matrix of Key Players
Patient/epidemiology data at country, region, global level
Regulatory Guidelines
Additional company profiles with specific to client's interest
Additional country or region analysis- market size and forecast
Criss-cross segment analysis- market size and forecast
Expanded list for Company Profiles
Historic market data
Import Export Analysis/Data
List of customers/consumers/raw material suppliers- value chain analysis
Product Consumption Analysis
SWOT Analysis
Volume Market Size and Forecast
Key Market Segments
By Deployment Type
On premises
Cloud
By End-Use Industry
Energy Generation
Energy Transmission
Energy Distribution
Utilities
By Component Type
Solution
Service
By Region
North America
U.S.
Canada
Mexico
Europe
Germany
France
UK
Spain
Italy
Rest of Europe
Asia-Pacific
China
Japan
India
South Korea
Australia
Rest of Asia-Pacific
LAMEA
Brazil
South Africa
Saudi Arabia
Rest of LAMEA
Key Market Players:
Enphase Energy
Flex Ltd.
Vestas
Origami
App Orchid
Enel Spa
Atos SE
Siemens AG
General Electric
Alpiq Holding Ltd.
TABLE OF CONTENTS
CHAPTER 1: INTRODUCTION
1.1. Report description
1.2. Key market segments
1.3. Key benefits to the stakeholders
1.4. Research methodology
1.4.1. Primary research
1.4.2. Secondary research
1.4.3. Analyst tools and models
CHAPTER 2: EXECUTIVE SUMMARY
2.1. CXO Perspective
CHAPTER 3: MARKET OVERVIEW
3.1. Market definition and scope
3.2. Key findings
3.2.1. Top impacting factors
3.2.2. Top investment pockets
3.3. Porter's five forces analysis
3.3.1. Moderate bargaining power of suppliers
3.3.2. High threat of new entrants
3.3.3. High threat of substitutes
3.3.4. High intensity of rivalry
3.3.5. High bargaining power of buyers
3.4. Market dynamics
3.4.1. Drivers
3.4.1.1. Increase in adoption of renewable energy technologies
3.4.1.2. AI-Powered advancements in renewable energy efficiency
3.4.2. Restraints
3.4.2.1. High cost of artificial intelligence-driven technologies
3.4.3. Opportunities
3.4.3.1. AI-Driven optimization for enhanced energy storage systems
3.4.3.2. Smart grid energy distribution and storage
3.5. Value Chain Analysis
3.6. Key Regulation Analysis
3.7. Patent Landscape
3.8. Regulatory Guidelines
CHAPTER 4: ARTIFICIAL INTELLIGENCE IN RENEWABLE ENERGY MARKET, BY DEPLOYMENT TYPE
4.1. Overview
4.1.1. Market size and forecast
4.2. On premises
4.2.1. Key market trends, growth factors and opportunities
4.2.2. Market size and forecast, by region
4.2.3. Market share analysis by country
4.3. Cloud
4.3.1. Key market trends, growth factors and opportunities
4.3.2. Market size and forecast, by region
4.3.3. Market share analysis by country
CHAPTER 5: ARTIFICIAL INTELLIGENCE IN RENEWABLE ENERGY MARKET, BY COMPONENT TYPE
5.1. Overview
5.1.1. Market size and forecast
5.2. Solution
5.2.1. Key market trends, growth factors and opportunities
5.2.2. Market size and forecast, by region
5.2.3. Market share analysis by country
5.3. Service
5.3.1. Key market trends, growth factors and opportunities
5.3.2. Market size and forecast, by region
5.3.3. Market share analysis by country
CHAPTER 6: ARTIFICIAL INTELLIGENCE IN RENEWABLE ENERGY MARKET, BY END-USE INDUSTRY
6.1. Overview
6.1.1. Market size and forecast
6.2. Energy Generation
6.2.1. Key market trends, growth factors and opportunities
6.2.2. Market size and forecast, by region
6.2.3. Market share analysis by country
6.3. Energy Transmission
6.3.1. Key market trends, growth factors and opportunities
6.3.2. Market size and forecast, by region
6.3.3. Market share analysis by country
6.4. Energy Distribution
6.4.1. Key market trends, growth factors and opportunities
6.4.2. Market size and forecast, by region
6.4.3. Market share analysis by country
6.5. Utilities
6.5.1. Key market trends, growth factors and opportunities
6.5.2. Market size and forecast, by region
6.5.3. Market share analysis by country
CHAPTER 7: ARTIFICIAL INTELLIGENCE IN RENEWABLE ENERGY MARKET, BY REGION
7.1. Overview
7.1.1. Market size and forecast By Region
7.2. North America
7.2.1. Key market trends, growth factors and opportunities
7.2.2. Market size and forecast, by Deployment Type
7.2.3. Market size and forecast, by Component Type
7.2.4. Market size and forecast, by End-Use Industry
7.2.5. Market size and forecast, by country
7.2.5.1. U.S.
7.2.5.1.1. Market size and forecast, by Deployment Type
7.2.5.1.2. Market size and forecast, by Component Type
7.2.5.1.3. Market size and forecast, by End-Use Industry
7.2.5.2. Canada
7.2.5.2.1. Market size and forecast, by Deployment Type
7.2.5.2.2. Market size and forecast, by Component Type
7.2.5.2.3. Market size and forecast, by End-Use Industry
7.2.5.3. Mexico
7.2.5.3.1. Market size and forecast, by Deployment Type
7.2.5.3.2. Market size and forecast, by Component Type
7.2.5.3.3. Market size and forecast, by End-Use Industry
7.3. Europe
7.3.1. Key market trends, growth factors and opportunities
7.3.2. Market size and forecast, by Deployment Type
7.3.3. Market size and forecast, by Component Type
7.3.4. Market size and forecast, by End-Use Industry
7.3.5. Market size and forecast, by country
7.3.5.1. Germany
7.3.5.1.1. Market size and forecast, by Deployment Type
7.3.5.1.2. Market size and forecast, by Component Type
7.3.5.1.3. Market size and forecast, by End-Use Industry
7.3.5.2. France
7.3.5.2.1. Market size and forecast, by Deployment Type
7.3.5.2.2. Market size and forecast, by Component Type
7.3.5.2.3. Market size and forecast, by End-Use Industry
7.3.5.3. UK
7.3.5.3.1. Market size and forecast, by Deployment Type
7.3.5.3.2. Market size and forecast, by Component Type
7.3.5.3.3. Market size and forecast, by End-Use Industry
7.3.5.4. Spain
7.3.5.4.1. Market size and forecast, by Deployment Type
7.3.5.4.2. Market size and forecast, by Component Type
7.3.5.4.3. Market size and forecast, by End-Use Industry
7.3.5.5. Italy
7.3.5.5.1. Market size and forecast, by Deployment Type
7.3.5.5.2. Market size and forecast, by Component Type
7.3.5.5.3. Market size and forecast, by End-Use Industry
7.3.5.6. Rest of Europe
7.3.5.6.1. Market size and forecast, by Deployment Type
7.3.5.6.2. Market size and forecast, by Component Type
7.3.5.6.3. Market size and forecast, by End-Use Industry
7.4. Asia-Pacific
7.4.1. Key market trends, growth factors and opportunities
7.4.2. Market size and forecast, by Deployment Type
7.4.3. Market size and forecast, by Component Type
7.4.4. Market size and forecast, by End-Use Industry
7.4.5. Market size and forecast, by country
7.4.5.1. China
7.4.5.1.1. Market size and forecast, by Deployment Type
7.4.5.1.2. Market size and forecast, by Component Type
7.4.5.1.3. Market size and forecast, by End-Use Industry
7.4.5.2. Japan
7.4.5.2.1. Market size and forecast, by Deployment Type
7.4.5.2.2. Market size and forecast, by Component Type
7.4.5.2.3. Market size and forecast, by End-Use Industry
7.4.5.3. India
7.4.5.3.1. Market size and forecast, by Deployment Type
7.4.5.3.2. Market size and forecast, by Component Type
7.4.5.3.3. Market size and forecast, by End-Use Industry
7.4.5.4. South Korea
7.4.5.4.1. Market size and forecast, by Deployment Type
7.4.5.4.2. Market size and forecast, by Component Type
7.4.5.4.3. Market size and forecast, by End-Use Industry
7.4.5.5. Australia
7.4.5.5.1. Market size and forecast, by Deployment Type
7.4.5.5.2. Market size and forecast, by Component Type
7.4.5.5.3. Market size and forecast, by End-Use Industry
7.4.5.6. Rest of Asia-Pacific
7.4.5.6.1. Market size and forecast, by Deployment Type
7.4.5.6.2. Market size and forecast, by Component Type
7.4.5.6.3. Market size and forecast, by End-Use Industry
7.5. LAMEA
7.5.1. Key market trends, growth factors and opportunities
7.5.2. Market size and forecast, by Deployment Type
7.5.3. Market size and forecast, by Component Type
7.5.4. Market size and forecast, by End-Use Industry
7.5.5. Market size and forecast, by country
7.5.5.1. Brazil
7.5.5.1.1. Market size and forecast, by Deployment Type
7.5.5.1.2. Market size and forecast, by Component Type
7.5.5.1.3. Market size and forecast, by End-Use Industry
7.5.5.2. South Africa
7.5.5.2.1. Market size and forecast, by Deployment Type
7.5.5.2.2. Market size and forecast, by Component Type
7.5.5.2.3. Market size and forecast, by End-Use Industry
7.5.5.3. Saudi Arabia
7.5.5.3.1. Market size and forecast, by Deployment Type
7.5.5.3.2. Market size and forecast, by Component Type
7.5.5.3.3. Market size and forecast, by End-Use Industry
7.5.5.4. Rest of LAMEA
7.5.5.4.1. Market size and forecast, by Deployment Type
7.5.5.4.2. Market size and forecast, by Component Type
7.5.5.4.3. Market size and forecast, by End-Use Industry
