세계의 에너지용 AI 시장 예측 : 컴포넌트 유형별, 전개 유형별, 용도별, 최종 사용자별, 지역별 분석(-2030년)

According to Stratistics MRC, the Global AI in Energy Market is accounted for $6.81 billion in 2024 and is expected to reach $19.73 billion by 2030 growing at a CAGR of 19.4% during the forecast period. Artificial intelligence (AI) is transforming the energy industry through cost reduction, efficiency enhancement, and process optimization. Artificial intelligence (AI) technologies are being used to better manage distribution networks, forecast energy demand, and maximize energy production. AI is able to forecast patterns of energy consumption and make real-time adjustments to supply by analyzing large amounts of data from sensors and smart grids using sophisticated algorithms and machine learning. Furthermore, by controlling their variability and guaranteeing a steady supply of energy, AI plays a crucial role in the integration of renewable energy sources into the grid.

According to the International Energy Agency (IEA), the adoption of AI in the energy sector could lead to significant improvements in energy efficiency, enabling smarter energy systems that can adapt to changing demand and supply conditions in real-time.

Market Dynamics:

Driver:

Growing interest in energy efficiency

The demand for more effective energy management is growing as the world's energy consumption keeps rising. Leading the way in meeting this demand are artificial intelligence (AI) technologies, which provide tools to forecast patterns in energy consumption, maximize energy output, and cut down on needless energy spending. Artificial intelligence (AI) has the ability to recognize inefficiencies in energy systems, suggest modifications, and initiate automated reactions to variations in demand using machine learning algorithms. Moreover, by making the best use of the resources at hand, this not only lowers operating costs for energy providers but also helps the global effort to cut greenhouse gas emissions.

Restraint:

Exorbitant implementation expenses

The energy sector can benefit greatly from artificial intelligence (AI), but many organizations-especially smaller utilities and energy companies-may find the initial costs of implementing AI technologies to be unaffordable. Considerable investment in software, hardware, and qualified labor is needed for the integration of AI. Upgrading current infrastructure, investing in hiring or training data scientists and AI specialists, and buying cutting-edge sensors and data processing equipment are all possible needs for businesses. Additionally, AI algorithms must be customized for particular energy applications, which means that creating and maintaining them can be expensive.

Opportunity:

Creating AI-powered predictive maintenance systems

The energy sector has a lot of potential when it comes to AI-driven predictive maintenance. Through constant monitoring of the state of energy infrastructure, including power plants, transmission lines, and renewable energy installations, artificial intelligence (AI) can anticipate maintenance needs before a breakdown happens. In addition to lowering maintenance costs, this increases asset lifespan and decreases downtime. Furthermore, in addition to increasing operational effectiveness, the use of AI in predictive maintenance also increases safety and dependability in the generation and delivery of energy.

Threat:

Threats and risks to cybersecurity

There are major cybersecurity risks associated with the energy sector's growing reliance on AI. Artificial intelligence (AI) systems are becoming increasingly important for controlling power plants, distribution networks, and energy grids. Should an AI-driven energy system be successfully attacked, there could be widespread blackouts, harm to vital infrastructure, and even threats to national security. Hackers may be able to alter AI algorithms to cause equipment malfunctions, compromise energy distribution, or pilfer confidential information. Moreover, the attack surface grows as energy systems become more digitally integrated and dependent, increasing the difficulty of defending against cyber attacks.

Covid-19 Impact:

The COVID-19 pandemic had a significant effect on artificial intelligence (AI) in the energy market. It caused supply chain disruptions, project delays, and a brief decline in energy demand as a result of lockdowns and slower economic growth. But as energy companies looked to streamline operations, improve remote monitoring capabilities, and fortify themselves against future shocks, the pandemic also hastened the adoption of digital technologies, including artificial intelligence (AI). Additionally, interest in AI solutions increased during the crisis as the need for more effective energy management and the integration of renewable energy sources became even more imperative.

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

In the AI in Energy market, the hardware segment is projected to hold the largest share. Parts like sensors, CPUs, storage, and other vital infrastructure are included in this segment that is necessary for implementing AI systems. Because AI applications in energy management, smart grids, and renewable energy integration require reliable data collection, real-time processing, and storage capabilities, there is an increasing need for sophisticated hardware. Furthermore, energy companies are now the dominant segment in the market due to their increasing adoption of AI-driven solutions, which is driving up demand for sophisticated and high-performance hardware.

The Cloud-based segment is expected to have the highest CAGR during the forecast period

The AI in Energy market's cloud-based solutions segment has the highest CAGR. The growing popularity of cloud computing due to its affordability, scalability, and flexibility is the main driver of this growth. Energy companies can now use large amounts of data and sophisticated algorithms without requiring a lot of on-premise infrastructure owing to cloud-based AI platforms. Moreover, cloud solutions support collaboration across geographical boundaries and enable the integration of disparate data sources, which makes them especially appealing for managing complex energy systems and fostering innovation in fields like energy optimization and predictive maintenance.

Region with largest share:

In the AI in Energy market, North America has the largest share. A well-established energy sector, significant investments in research and development, and the region's cutting-edge technological infrastructure are all credited for this dominance. The adoption of AI technologies is leading in North America, especially the US, owing to the substantial funding from the public and private sectors, as well as the strong presence of large technology companies and creative start-ups. Additionally, AI solutions are in high demand because of the region's emphasis on modernizing infrastructure, integrating renewable energy sources, and increasing energy efficiency.

Region with highest CAGR:

The AI in Energy market is growing at the highest CAGR in the Asia-Pacific region. The region's growing industrialization, rising energy infrastructure investment, and major government programs to improve energy efficiency and incorporate renewable energy sources are the main drivers of this fast growth. In order to meet their increasing energy demands and update their energy systems, nations like China and India are setting the standard for the adoption of AI technologies. Furthermore, the adoption of AI in the region is also accelerating due to the development of smart grids, urbanization, and the push for sustainable energy practices.

Key players in the market

Some of the key players in AI in Energy market include Siemens AG, Hazama Ando Corporation, Amazon Web Services, Inc., Informatec Ltd., FlexGen Power Systems, Inc., Schneider Electric, ABB Group, General Electric, SmartCloud Inc, AppOrchid Inc, Origami Energy Ltd., Zen Robotics Ltd and Alpiq AG.

Key Developments:

In July 2024, Boson Energy and Siemens AG have signed a Memorandum of Understanding (MoU) to facilitate collaboration on technology that converts non-recyclable waste into clean energy. The collaboration aims to advance sustainable, local energy security, enabling hydrogen-powered electric vehicle charging infrastructure without compromising grid stability or impacting consumer prices.

In November 2023, Battery storage system integrator FlexGen and battery manufacturer Hithium could be supplying each other with complementary technologies for large-scale battery energy storage system (BESS) projects. FlexGen would buy up to 10GWh of Hithium battery capacity in that time, while the Chinese manufacturer would use FlexGen's energy management system (EMS) in a combined 15GWh of projects.

In November 2023, Schneider Electric, the leader in the digital transformation of energy management and automation, today announced at its Capital Markets Day meeting with investors a $3 billion multi-year agreement with Compass Datacenters. The agreement extends the companies' existing relationship that integrates their respective supply chains to manufacture and deliver prefabricated modular data center solutions.

Component Types Covered:

• Hardware
• Solutions
• Services

Deployment Types Covered:

• On-premise
• Cloud-based

Applications Covered:

• Robotics
• Energy Management
• Renewables Management
• Demand Forecasting
• Predictive Maintenance
• Grid Optimization
• Safety and Security
• Infrastructure
• Other Applications

End Users Covered:

• Power Generation
• Oil & Gas
• Renewable Energy
• Utilities
• 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 Application Analysis
• 3.7 End User Analysis
• 3.8 Emerging Markets
• 3.9 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 AI in Energy Market, By Component Type

• 5.1 Introduction
• 5.2 Hardware
• 5.3 Solutions
• 5.4 Services

6 Global AI in Energy Market, By Deployment Type

• 6.1 Introduction
• 6.2 On-premise
• 6.3 Cloud-based

7 Global AI in Energy Market, By Application

• 7.1 Introduction
• 7.2 Robotics
• 7.3 Energy Management
• 7.4 Renewables Management
• 7.5 Demand Forecasting
• 7.6 Predictive Maintenance
• 7.7 Grid Optimization
• 7.8 Safety and Security
• 7.9 Infrastructure
• 7.10 Other Applications

8 Global AI in Energy Market, By End User

• 8.1 Introduction
• 8.2 Power Generation
• 8.3 Oil & Gas
• 8.4 Renewable Energy
• 8.5 Utilities
• 8.6 Other End Users

9 Global AI in Energy Market, By Geography

• 9.1 Introduction
• 9.2 North America
  • 9.2.1 US
  • 9.2.2 Canada
  • 9.2.3 Mexico
• 9.3 Europe
  • 9.3.1 Germany
  • 9.3.2 UK
  • 9.3.3 Italy
  • 9.3.4 France
  • 9.3.5 Spain
  • 9.3.6 Rest of Europe
• 9.4 Asia Pacific
  • 9.4.1 Japan
  • 9.4.2 China
  • 9.4.3 India
  • 9.4.4 Australia
  • 9.4.5 New Zealand
  • 9.4.6 South Korea
  • 9.4.7 Rest of Asia Pacific
• 9.5 South America
  • 9.5.1 Argentina
  • 9.5.2 Brazil
  • 9.5.3 Chile
  • 9.5.4 Rest of South America
• 9.6 Middle East & Africa
  • 9.6.1 Saudi Arabia
  • 9.6.2 UAE
  • 9.6.3 Qatar
  • 9.6.4 South Africa
  • 9.6.5 Rest of Middle East & Africa

10 Key Developments

• 10.1 Agreements, Partnerships, Collaborations and Joint Ventures
• 10.2 Acquisitions & Mergers
• 10.3 New Product Launch
• 10.4 Expansions
• 10.5 Other Key Strategies

11 Company Profiling

• 11.1 Siemens AG
• 11.2 Hazama Ando Corporation
• 11.3 Amazon Web Services, Inc.
• 11.4 Informatec Ltd.
• 11.5 FlexGen Power Systems, Inc.
• 11.6 Schneider Electric
• 11.7 ABB Group
• 11.8 General Electric
• 11.9 SmartCloud Inc
• 11.10 AppOrchid Inc
• 11.11 Origami Energy Ltd.
• 11.12 Zen Robotics Ltd
• 11.13 Alpiq AG