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AI in Energy and Utilities Market

AI in Energy and Utilities Market Size, Share & Trends Analysis Report By Type (Machine Learning, Natural Language Processing, Computer Vision, Predictive Analytics, Deep Learning, Others), By Application, By End-User, By Region, And By Segment Forecasts, 2024-2031

Report ID : 2747 | Published : 2024-09-25 | Pages: 170 | Format: PDF/EXCEL

The AI in Energy and Utilities Market Size is valued at USD 10.9 billion in 2023 and is predicted to reach USD 45.0 billion by the year 2031 at a 19.8% CAGR during the forecast period for 2024-2031.

ai in energy and utility

Artificial intelligence in the energy as well as utilities sector improves efficiency through the optimization of grid management, forecasting energy demand, and the integration of renewable energy sources. It facilitates smart grids, enabling predictive maintenance to avert equipment breakdowns, and assists in diminishing energy use. AI enhances demand responsiveness, equilibrates supply and demand, and aids in energy trading through market pattern analysis. These technologies enhance sustainability, decrease expenses, and facilitate the shift to cleaner energy alternatives.

Additionally, AI in energy and utilities makes it easier to manage and incorporate renewable energy sources into current systems. Advancements in protection and energy management have also enhanced the general efficiency and effectiveness of AI in energy and utility installations. Furthermore, AI in the energy and utilities sector enables data analysis and real-time monitoring, and the development of smart grids is enhanced, leading to more efficient and reliable grid operations.

However, the high cost of developing AI in the energy and utility sector is a significant market constraint. Additionally, market growth is further hindered by a need for more knowledge and familiarity with these technologies. A number of factors are creating opportunities for AI in the energy and utilities market. These include the increasing use of renewable energy sources, improvements in AI technologies, encouragement from government policies, and the necessity for more reliable grids and lower energy consumption across many industries.

Competitive Landscape

Some Major Key Players In The AI in Energy and Utilities Market:

  • Siemens AG
  • General Electric Company
  • International Business Machines Corporation (IBM)
  • Schneider Electric SE
  • ABB Ltd
  • Microsoft Corporation
  • Oracle Corporation
  • Honeywell International Inc.
  • Cisco Systems, Inc.
  • SAS Institute Inc.
  • Intel Corporation
  • Siemens Energy AG
  • Enel X S.r.l.
  • C3.ai, Inc.
  • Tesla, Inc.
  • Google LLC (subsidiary of Alphabet Inc.)
  • Engie SA
  • Accenture plc
  • Hitachi, Ltd.
  • Vestas Wind Systems A/S
  • Wärtsilä Oyj Abp
  • Électricité de France (EDF)
  • Shell plc
  • Nvidia Corporation
  • Eaton Corporation plc
  • Other Market Players

Market Segmentation:

The AI in energy & utilities market is segmented based on type, application, and end-user. Based on type, the market is segmented into machine learning, computer vision, natural language processing, predictive analytics, deep learning, and others. By application, the market is segmented into energy management and optimization, demand forecasting, equipment maintenance and monitoring, grid management, smart metering, customer service and engagement, renewable energy integration, fraud detection, energy trading and pricing, and others. By end-user, the market is segmented into power generation companies, utility companies, renewable energy providers, oil and gas companies, energy service providers, government and regulatory bodies, and others.

Based On The Type, The Predictive Analytics Segment Is Accounted As A Major Contributor To The AI In The Energy And Utilities Market.

Predictive analytics is expected to hold a major global market share in 2023 in the AI in energy and utilities market because of its superior capacity to optimize resource allocation and precisely predict energy consumption. Predictive analytics, which analyzes both historical and real-time data, makes proactive maintenance possible. This helps to reduce operational expenses and downtime. For utilities aiming to achieve sustainability goals, predictive analytics is vital because of the role it plays in improving grid resilience and energy efficiency and aiding the integration of renewable energy sources.

Renewable Energy Integration Segment To Witness Growth At A Rapid Rate.

The renewable energy integration segment is growing because artificial intelligence improves the dependability and efficiency of connecting renewable energy sources to the grid. With the help of artificial intelligence, utilities can maximize the utilization of replenishable energy sources and meet expanding sustainability goals and regulatory mandates. Artificial intelligence enhances grid stability, optimizes energy forecasts, and balances supply and demand.

In The Region, The North American AI In Energy And Utilities Market Holds A Significant Revenue Share.

The North American AI in the energy and utilities market is expected to document the largest market share in revenue in the near future. This can be attributed to the emphasis on sustainability and energy efficiency in many different sectors, as well as substantial expenditures on renewable energy, cutting-edge technical infrastructure, and greater use of artificial intelligence is driving operational efficiencies and utility-wide optimization of energy distribution in response to the region’s push to reduce carbon emissions and increase energy efficiency. In addition, the Europe is expected to grow rapidly in the AI in energy and utilities market because of the region’s growing urban population, increasing energy demand, smart grid development efforts, investments in replenishable energy, and improvements to the region’s infrastructure driven by artificial intelligence.

Recent Developments:

  • In July 2024, Cisco and Morgan Solar, a Toronto-based firm that focuses on solar energy integration into urban environments, unveiled a pilot project to use solar energy to power collaboration and meeting spaces.
  • In Feb 2024, Signing a cooperation agreement in 2023, ABB and Microsoft will be extending their long-standing alliance for the advancement of generative artificial intelligence (AI) technologies for industry. This year, they hope to introduce new solutions integrating Microsoft's OpenAI with ABB's Ability Genix's capabilities. Leveraging Microsoft's Azure OpenAI Service, the two are combining generative AI capabilities for a more intuitive user interface with ABB Ability Genix Industrial Analytics and AI Suite and apps. Real-time production information from the new application Genix Copilot will be available to shop floor engineers, functional experts, and industry leaders.

AI in Energy and Utilities Market Report Scope

Report Attribute

Specifications

Market Size Value In 2023

USD 10.9 Bn

Revenue Forecast In 2031

USD 45.0 Bn

Growth Rate CAGR

CAGR of 19.8% from 2024 to 2031

Quantitative Units

Representation of revenue in US$ Bn and CAGR from 2024 to 2031

Historic Year

2019 to 2023

Forecast Year

2024-2031

Report Coverage

The forecast of revenue, the position of the company, the competitive market structure, growth prospects, and trends

Segments Covered

By Type, Application, End-User

Regional Scope

North America; Europe; Asia Pacific; Latin America; Middle East & Africa

Country Scope

U.S.; Canada; U.K.; Germany; China; India; Japan; Brazil; Mexico; France; Italy; Spain; South East Asia; South Korea

Competitive Landscape

Siemens AG, General Electric Company, International Business Machines Corporation (IBM), Schneider Electric SE, ABB Ltd, Microsoft Corporation, Oracle Corporation, Honeywell International Inc., Cisco Systems, Inc., SAS Institute Inc., Intel Corporation, Siemens Energy AG, Enel X S.r.l., C3.ai, Inc., Tesla, Inc., Google LLC (subsidiary of Alphabet Inc.), Engie SA, Accenture plc, Hitachi, Ltd., Vestas Wind Systems A/S, Wärtsilä Oyj Abp, Électricité de France (EDF), Shell plc, and Nvidia Corporation, and Eaton Corporation plc.

Customization Scope

Free customization report with the procurement of the report and modifications to the regional and segment scope. Particular Geographic competitive landscape.

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Chapter 1. Methodology and Scope
1.1. Research Methodology
1.2. Research Scope & Assumptions

Chapter 2. Executive Summary

Chapter 3. Global AI in Energy and Utilities Market Snapshot

Chapter 4. Global AI in Energy and Utilities Market Variables, Trends & Scope
4.1. Market Segmentation & Scope
4.2. Drivers
4.3. Challenges
4.4. Trends
4.5. Investment and Funding Analysis
4.6. Industry Analysis – Porter’s Five Forces Analysis
4.7. Competitive Landscape & Market Share Analysis
4.8. Impact of Covid-19 Analysis

Chapter 5. Market Segmentation 1: by Type Estimates & Trend Analysis
5.1. by Type & Market Share, 2019 & 2031
5.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2019 to 2031 for the following by Type:

5.2.1. Machine Learning
5.2.2. Natural Language Processing
5.2.3. Computer Vision
5.2.4. Predictive Analytics
5.2.5. Deep Learning
5.2.6. Others

Chapter 6. Market Segmentation 2: by Application Estimates & Trend Analysis
6.1. by Application & Market Share, 2019 & 2031
6.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2019 to 2031 for the following by Application:

6.2.1. Energy Management and Optimization
6.2.2. Grid Management
6.2.3. Demand Forecasting
6.2.4. Equipment Maintenance and Monitoring
6.2.5. Smart Metering
6.2.6. Renewable Energy Integration
6.2.7. Customer Service and Engagement
6.2.8. Fraud Detection
6.2.9. Energy Trading and Pricing
6.2.10. Others

Chapter 7. Market Segmentation 3: by End-User Estimates & Trend Analysis
7.1. by End-User & Market Share, 2019 & 2031
7.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2019 to 2031 for the following by End-User:

7.2.1. Power Generation Companies
7.2.2. Utility Companies
7.2.3. Oil and Gas Companies
7.2.4. Renewable Energy Providers
7.2.5. Energy Service Providers
7.2.6. Government and Regulatory Bodies
7.2.7. Others

Chapter 8. AI in Energy and Utilities Market Segmentation 4: Regional Estimates & Trend Analysis

8.1. North America
8.1.1. North America AI in Energy and Utilities Market Revenue (US$ Million) Estimates and Forecasts by Type, 2024-2031
8.1.2. North America AI in Energy and Utilities Market Revenue (US$ Million) Estimates and Forecasts by Application, 2024-2031
8.1.3. North America AI in Energy and Utilities Market Revenue (US$ Million) Estimates and Forecasts by End-User, 2024-2031
8.1.4. North America AI in Energy and Utilities Market Revenue (US$ Million) Estimates and Forecasts by country, 2024-2031

8.2. Europe
8.2.1. Europe AI in Energy and Utilities Market Revenue (US$ Million) Estimates and Forecasts by Type, 2024-2031
8.2.2. Europe AI in Energy and Utilities Market Revenue (US$ Million) Estimates and Forecasts by Application, 2024-2031
8.2.3. Europe AI in Energy and Utilities Market Revenue (US$ Million) Estimates and Forecasts by End-User, 2024-2031
8.2.4. Europe AI in Energy and Utilities Market Revenue (US$ Million) Estimates and Forecasts by country, 2024-2031

8.3. Asia Pacific
8.3.1. Asia Pacific AI in Energy and Utilities Market Revenue (US$ Million) Estimates and Forecasts by Type, 2024-2031
8.3.2. Asia Pacific AI in Energy and Utilities Market Revenue (US$ Million) Estimates and Forecasts by Application, 2024-2031
8.3.3. Asia-Pacific AI in Energy and Utilities Market Revenue (US$ Million) Estimates and Forecasts by End-User, 2024-2031
8.3.4. Asia Pacific AI in Energy and Utilities Market Revenue (US$ Million) Estimates and Forecasts by country, 2024-2031

8.4. Latin America
8.4.1. Latin America AI in Energy and Utilities Market Revenue (US$ Million) Estimates and Forecasts by Type, 2024-2031
8.4.2. Latin America AI in Energy and Utilities Market Revenue (US$ Million) Estimates and Forecasts by Application, 2024-2031
8.4.3. Latin America AI in Energy and Utilities Market Revenue (US$ Million) Estimates and Forecasts by End-User, 2024-2031
8.4.4. Latin America AI in Energy and Utilities Market Revenue (US$ Million) Estimates and Forecasts by country, 2024-2031

8.5. Middle East & Africa
8.5.1. Middle East & Africa AI in Energy and Utilities Market Revenue (US$ Million) Estimates and Forecasts by Type, 2024-2031
8.5.2. Middle East & Africa AI in Energy and Utilities Market Revenue (US$ Million) Estimates and Forecasts by Application, 2024-2031
8.5.3. Middle East & Africa AI in Energy and Utilities Market Revenue (US$ Million) Estimates and Forecasts by End-User, 2024-2031
8.5.4. Middle East & Africa AI in Energy and Utilities Market Revenue (US$ Million) Estimates and Forecasts by country, 2024-2031

Chapter 9. Competitive Landscape
9.1. Major Mergers and Acquisitions/Strategic Alliances
9.2. Company Profiles

9.2.1. Siemens AG
9.2.2. General Electric Company
9.2.3. International Business Machines Corporation (IBM)
9.2.4. Schneider Electric SE
9.2.5. ABB Ltd
9.2.6. Microsoft Corporation
9.2.7. Oracle Corporation
9.2.8. Honeywell International Inc.
9.2.9. Cisco Systems, Inc.
9.2.10. SAS Institute Inc.
9.2.11. Intel Corporation
9.2.12. Siemens Energy AG
9.2.13. Enel X S.r.l.
9.2.14. C3.ai, Inc.
9.2.15. Tesla, Inc.
9.2.16. Google LLC (subsidiary of Alphabet Inc.)
9.2.17. Engie SA
9.2.18. Accenture plc
9.2.19. Hitachi, Ltd.
9.2.20. Vestas Wind Systems A/S
9.2.21. Wärtsilä Oyj Abp
9.2.22. Électricité de France (EDF)
9.2.23. Shell plc
9.2.24. Nvidia Corporation
9.2.25. Eaton Corporation plc
9.2.26. Other Market Players

Segmentation of AI in Energy and Utilities Market -

AI in Energy and Utilities Market By Type-

  • Machine Learning
  • Natural Language Processing
  • Computer Vision
  • Predictive Analytics
  • Deep Learning
  • Others

ai in energy and utility

AI in Energy and Utilities Market By Application-

  • Energy Management and Optimization
  • Grid Management
  • Demand Forecasting
  • Equipment Maintenance and Monitoring
  • Smart Metering
  • Renewable Energy Integration
  • Customer Service and Engagement
  • Fraud Detection
  • Energy Trading and Pricing
  • Others

AI in Energy and Utilities Market By End-User-

  • Power Generation Companies
  • Utility Companies
  • Oil and Gas Companies
  • Renewable Energy Providers
  • Energy Service Providers
  • Government and Regulatory Bodies
  • Others

AI in Energy and Utilities Market By Region-

North America-

  • The US
  • Canada
  • Mexico

Europe-

  • Germany
  • The UK
  • France
  • Italy
  • Spain
  • Rest of Europe

Asia-Pacific-

  • China
  • Japan
  • India
  • South Korea
  • South East Asia
  • Rest of Asia Pacific

Latin America-

  • Brazil
  • Argentina
  • Rest of Latin America

 Middle East & Africa-

  • GCC Countries
  • South Africa
  • Rest of the Middle East and Africa

 

InsightAce Analytic follows a standard and comprehensive market research methodology focused on offering the most accurate and precise market insights. The methods followed for all our market research studies include three significant steps – primary research, secondary research, and data modeling and analysis - to derive the current market size and forecast it over the forecast period. In this study, these three steps were used iteratively to generate valid data points (minimum deviation), which were cross-validated through multiple approaches mentioned below in the data modeling section.

Through secondary research methods, information on the market under study, its peer, and the parent market was collected. This information was then entered into data models. The resulted data points and insights were then validated by primary participants.

Based on additional insights from these primary participants, more directional efforts were put into doing secondary research and optimize data models. This process was repeated till all data models used in the study produced similar results (with minimum deviation). This way, this iterative process was able to generate the most accurate market numbers and qualitative insights.

Secondary research

The secondary research sources that are typically mentioned to include, but are not limited to:

  • Company websites, financial reports, annual reports, investor presentations, broker reports, and SEC filings.
  • External and internal proprietary databases, regulatory databases, and relevant patent analysis
  • Statistical databases, National government documents, and market reports
  • Press releases, news articles, and webcasts specific to the companies operating in the market

The paid sources for secondary research like Factiva, OneSource, Hoovers, and Statista

Primary Research:

Primary research involves telephonic interviews, e-mail interactions, as well as face-to-face interviews for each market, category, segment, and subsegment across geographies

The contributors who typically take part in such a course include, but are not limited to: 

  • Industry participants: CEOs, CBO, CMO, VPs, marketing/ type managers, corporate strategy managers, and national sales managers, technical personnel, purchasing managers, resellers, and distributors.
  • Outside experts: Valuation experts, Investment bankers, research analysts specializing in specific markets
  • Key opinion leaders (KOLs) specializing in unique areas corresponding to various industry verticals
  • End-users: Vary mainly depending upon the market

Data Modeling and Analysis:

In the iterative process (mentioned above), data models received inputs from primary as well as secondary sources. But analysts working on these models were the key. They used their extensive knowledge and experience about industry and topic to make changes and fine-tuning these models as per the product/service under study.

The standard data models used while studying this market were the top-down and bottom-up approaches and the company shares analysis model. However, other methods were also used along with these – which were specific to the industry and product/service under study.

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Frequently Asked Questions

How big is the AI in Energy and Utilities Market Size?

The AI in Energy and Utilities Market is expected to grow at a 19.8% CAGR during the forecast period for 2024-2031.

Siemens AG, General Electric Company, International Business Machines Corporation (IBM), Schneider Electric SE, ABB Ltd, Microsoft Corporation, Oracle

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