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AI In Energy Market Growth From $27.88 Billion In 2026 To $60.6 Billion By 2030 At A CAGR Of 21.4%
The AI in energy market size has expanded considerably in recent years. It is forecast to increase from $22.82 billion in 2025 to $27.89 billion in 2026, achieving a compound annual growth rate (CAGR) of 22.2%. The observed growth in past periods is attributable to factors like rising energy consumption, initial grid automation projects, an increase in renewable energy capacity, the demand for operational efficiency, and the development of smart metering infrastructure.
The AI in energy market is projected to experience substantial expansion in the coming years. By 2030, its valuation is set to reach $60.6 billion, demonstrating a compound annual growth rate (CAGR) of 21.4%. This anticipated growth is driven by several factors over the forecast period, such as decarbonization targets, the increasing adoption of AI-driven grid intelligence, the expansion of distributed energy resources, the rising demand for real-time energy analytics, and energy management strategies focused on resilience. Prominent trends for the forecast duration encompass the use of AI-based demand forecasting, enhanced smart grid optimization, the incorporation of predictive maintenance solutions, wider renewable energy management, and leveraging AI for optimal energy efficiency.
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AI In Energy Market Growth Factors Supporting Long-Term Expansion
The expanded deployment of microgrids is projected to boost the expansion of AI in the energy market. A microgrid represents a local energy infrastructure that can function autonomously or alongside the main power grid. These systems are crucial for integrating and refining AI capabilities within the energy domain, facilitating smart energy management, enhancing grid efficiency, and incorporating renewable power sources. This further supports AI energy uses such as demand response, balancing loads, and integrating renewables. The AI in the energy market is experiencing substantial growth due to the rising adoption of microgrids. For instance, in September 2024, data from the Energy Trends report, released by the UK’s Department for Energy Security and Net Zero, indicated that installed capacity increased by 3.9 percent (2.1 GW) in the second quarter of 2024 compared to the same period in 2023, reaching a total of 57.5 GW. Consequently, the growing utilization of microgrids is anticipated to stimulate the growth of AI in the energy market.
AI In Energy Market Segment Analysis And Revenue Opportunities
The AI in energy market covered in this report is segmented –
1) By Offering: Support Services, Hardware, Artificial Intelligence (AI)-As-A-Service, Software
2) By Deployment: On-Premise, Cloud
3) By Application: Demand Response Management, Fleet And Asset Management, Renewable Energy Management, Precision Drilling, Demand Forecasting, Infrastructure Management, Other Applications
4) By End User: Energy Transmission, Energy Generation, Energy Distribution, Utilities, Other End Users
Subsegments:
1) By Support Services: Consulting Services, Implementation Services, Maintenance And Support Services
2) By Hardware: Artificial Intelligence (AI)-Powered Sensors, Smart Meters, Edge Computing Devices
3) By Artificial Intelligence (AI)-As-A-Service: Machine Learning Platforms, Data Analytics Services, Model Training And Development Services
4) By Software: Energy Management Software, Predictive Maintenance Software, Demand Response Software, Grid Management Software
AI In Energy Market Innovation Trends Driving Future Development
Leading companies in the AI in the energy market are developing innovative technologies, such as the Pangu Mine Model, which represents the world’s first commercially available large AI model for the energy sector. The Pangu Mine Model is engineered to address challenges encountered in the mining industry and the broader energy sector through the deployment of large AI models in industrial production. For example, in July 2023, Shandong Energy Group Co. Ltd., a China-based coal mining company, YunDing Tech Co. Ltd., a China-based network technology company, and Huawei Technologies Co. Ltd., a China-based manufacturing company, collaboratively launched the Pangu Mine Model. This model features decoupled operation management, intelligent production, in-campus data processing, large-scale replicability, and learning and analysis capabilities from small samples. Its design aims to enhance the intelligence level of energy in the mining industry by broadening the depth and breadth of AI applications across different scenarios and consistently utilizing AI to deliver more automation, achieve greater efficiency, reduce labor intensity, and improve safety in energy utilization for mining purposes.
AI In Energy Market Leading Players Shaping Industry Direction
Major companies operating in the AI in energy market are Google; Microsoft Corporation; Engie SA; Huawei Technologies Co Ltd.; Siemens AG; General Electric Company; Intel Corporation; International Business Machines Corporation; Iberdrola; Cisco Systems Inc.; Schneider Electric SE; Honeywell International Inc.; ABB Ltd; Duke Energy Corporation; Nvidia Corporation; Alpiq Holding AG; ATOS SE; Enel Green Power S.p.A.; Databricks Inc.; C3 AI; Uptake Technologies; Sentient Energy Inc.; AutoGrid Systems Inc.; Arundo Analytics Inc.; Bidgely Inc.; Verdigris Technologies; Greenbird Integration Technology AS; AppOrchid Inc.; Ecube Labs Co. Ltd
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AI In Energy Market Leading Geography: Which Region Generates The Most Revenue?
North America was the largest region in AI in the energy market in 2025. Asia-pacific is expected to be the fastest-growing region in the forecast period. The regions covered in the AI in energy market report are Asia-Pacific, South East Asia, Western Europe, Eastern Europe, North America, South America, Middle East, Africa.
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Wasay has over a decade of experience in market research, data modelling, and analytics, with prior experience at GlobalData and Decision Tree Consulting Services. At The Business Research Company , he leads research operations across syndicated studies, customized consulting engagements, and the Global Market Model platform. His professional experience includes supporting organizations such as Boston Consulting Group, KPMG, and Ernst & Young. Wasay holds a degree in Electronics and Communications Engineering, postgraduate management qualifications from International Management Institute Belgium and Indian School of Business and Entrepreneurship, and completed the Integrated Program in Business Analytics from Indian Institute of Management Indore.