AI in Energy Market Report 2026

The AI in energy market size has grown exponentially in recent years. It will grow from $22.82 billion in 2025 to $27.89 billion in 2026 at a compound annual growth rate (CAGR) of 22.2%. The growth in the historic period can be attributed to rising energy consumption, early grid automation initiatives, expansion of renewable energy capacity, demand for operational efficiency, growth of smart metering infrastructure.

The AI in energy market size is expected to see exponential growth in the next few years. It will grow to $60.6 billion in 2030 at a compound annual growth rate (CAGR) of 21.4%. The growth in the forecast period can be attributed to decarbonization targets, AI driven grid intelligence adoption, growth of distributed energy resources, real time energy analytics demand, resilience focused energy management. Major trends in the forecast period include adoption of AI based demand forecasting, growth of smart grid optimization, integration of predictive maintenance solutions, expansion of renewable energy management, use of AI for energy efficiency optimization.

The rising adoption of microgrids is expected to drive the growth of the AI in the energy market in the coming years. A microgrid is a localized energy network that can function independently or alongside the main power grid. Microgrids play a crucial role in integrating and enhancing AI technologies within the energy sector by enabling smart energy management, grid optimization, and the efficient integration of renewable energy sources. These systems support AI-based energy applications such as demand response, load balancing, and renewable energy integration. The AI in energy market is witnessing substantial growth due to the increasing deployment of microgrids. For instance, in September 2024, according to Energy Trends published by the UK-based Department for Energy Security and Net Zero, 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. Hence, the growing use of microgrids is anticipated to fuel the expansion of the AI in the energy market.

Key companies operating in the AI in energy market are developing innovative technologies such as the Pangu Mine Model, recognized as the world’s first commercially available large AI model tailored for the energy sector. The Pangu Mine Model is created to tackle challenges in the mining industry and the wider energy sector by applying large-scale AI models in industrial operations. 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 firm, and Huawei Technologies Co. Ltd., a China-based manufacturing company, collaboratively launched the Pangu Mine Model. This model offers features such as decoupled operational management, intelligent production, on-campus data processing, large-scale replicability, and learning and analysis using small data samples. It is intended to enhance the intelligence level of energy operations in the mining sector by broadening the scope and depth of AI applications across various scenarios while continuously leveraging AI to increase automation, improve efficiency, lower labor intensity, and enhance safety in energy utilization for mining activities.

In March 2025, Bidgely, a US-based provider of AI-powered SaaS energy analytics and consumer engagement solutions, acquired Grid4C for an undisclosed amount. Through this acquisition, Bidgely seeks to strengthen its AI-driven energy intelligence platform and broaden its capabilities in grid-side management and consumer engagement. Grid4C is a US-based company that specializes in AI-powered predictive analytics solutions for the energy industry.

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.

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. The countries covered in the AI in energy market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Taiwan, Russia, South Korea, UK, USA, Canada, Italy, Spain.

Tariffs have impacted the AI in energy market by increasing costs for imported smart meters, sensors, edge devices, and grid hardware. These impacts are strongest in renewable energy and grid modernization projects, particularly in asia pacific and europe. Higher equipment costs have influenced project timelines. However, tariffs are encouraging domestic manufacturing, localized energy technology ecosystems, and software driven optimization across energy operations.

The AI in energy market research report is one of a series of new reports that provides AI in energy market statistics, including AI in energy industry global market size, regional shares, competitors with a AI in energy market share, detailed AI in energy market segments, market trends and opportunities, and any further data you may need to thrive in the AI in energy industry. This AI in energy market research report delivers a complete perspective of everything you need, with an in-depth analysis of the current and future scenario of the industry.

AI in energy refers to the application of artificial intelligence (AI) technologies and techniques within the energy sector to enhance efficiency, optimize operations, and improve decision-making processes. This includes various aspects such as data analysis, grid management and security, and demand response. AI is utilized in the energy sector to maximize the recycling of materials used in renewable energy systems, including solar panels, wind turbines, and hydroelectric dams.

The primary offerings in the field of AI in energy include support services, hardware, AI-as-a-service, and software. Support services encompass a range of activities and resources provided to individuals, organizations, or customers to assist them with their needs, address issues, and ensure satisfaction. The technology can be deployed through on-premises and cloud deployment modes. AI in energy finds applications in diverse areas, including demand response management, fleet and asset management, renewable energy management, precision drilling, demand forecasting, infrastructure management, and more. End-users of AI in energy include entities involved in energy transmission, energy generation, energy distribution, utilities, and other related sectors.

The AI in the energy market consists of revenues earned by entities by providing services such as smart grid operations and robotics. The market value includes the value of related goods sold by the service provider or included within the service offering. The AI in the energy market also includes sales of PyTorch integration and analog device simulators. Values in this market are ‘factory gate’ values, that is, the value of goods sold by the manufacturers or creators of the goods, whether to other entities (including downstream manufacturers, wholesalers, distributors, and retailers) or directly to end customers. The value of goods in this market includes related services sold by the creators of the goods.

The market value is defined as the revenues that enterprises gain from the sale of goods and/or services within the specified market and geography through sales, grants, or donations in terms of the currency (in USD unless otherwise specified).

The revenues for a specified geography are consumption values that are revenues generated by organizations in the specified geography within the market, irrespective of where they are produced. It does not include revenues from resales along the supply chain, either further along the supply chain or as part of other products.

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