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.
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.
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.
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.
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.
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.
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Report Attribute |
Specifications |
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Market Size Value In 2023 |
USD 10.9 Bn |
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Revenue Forecast In 2031 |
USD 45.0 Bn |
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Growth Rate CAGR |
CAGR of 19.8% from 2024 to 2031 |
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Quantitative Units |
Representation of revenue in US$ Bn and CAGR from 2024 to 2031 |
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Historic Year |
2019 to 2023 |
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Forecast Year |
2024-2031 |
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Report Coverage |
The forecast of revenue, the position of the company, the competitive market structure, growth prospects, and trends |
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Segments Covered |
By Type, Application, End-User |
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Regional Scope |
North America; Europe; Asia Pacific; Latin America; Middle East & Africa |
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Country Scope |
U.S.; Canada; U.K.; Germany; China; India; Japan; Brazil; Mexico; France; Italy; Spain; South East Asia; South Korea |
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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. |
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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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Pricing And Available Payment Methods |
Explore pricing alternatives that are customized to your particular study requirements. |
AI in Energy and Utilities Market By Type-
AI in Energy and Utilities Market By Application-
AI in Energy and Utilities Market By End-User-
AI in Energy and Utilities Market By Region-
North America-
Europe-
Asia-Pacific-
Latin America-
Middle East & 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:
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:
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.
