AI in Energy Global Market Report 2024

The AI in energy market size has grown exponentially in recent years. It will grow from $5.23 billion in 2023 to $6.39 billion in 2024 at a compound annual growth rate (CAGR) of 22.2%. The growth observed in the historic period for AI in energy can be attributed to several factors, including the use of data analytics for improving efficiency, implementing demand response management strategies, adopting predictive maintenance practices, optimizing grid operations, and integrating renewable energy sources. These applications collectively contributed to the increased adoption and success of AI technologies in the energy sector during the historical period, enhancing overall operational effectiveness and sustainability.

The AI in energy market size is expected to see exponential growth in the next few years. It will grow to $13.36 billion in 2028 at a compound annual growth rate (CAGR) of 20.2%. The anticipated growth in the forecast period for AI in energy can be attributed to various factors, including the adoption of decentralized energy systems, effective management of energy storage, the development of smart cities and infrastructure, the increasing electrification of transportation, and the integration of edge computing technologies. Key trends expected in the forecast period encompass the application of energy efficiency analytics, the development of autonomous energy infrastructure, the implementation of cybersecurity solutions, the emergence of virtual power plants, and the utilization of edge AI for energy management.


The anticipated surge in microgrid adoption is set to catalyze the expansion of AI technology within the energy market in the foreseeable future. Microgrids denote localized energy systems functioning autonomously or in conjunction with the primary power grid. These microgrids wield significant influence in integrating and optimizing AI advancements in the energy sector. Their role encompasses facilitating intelligent energy management, grid optimization, and seamless integration of renewable energy sources. This integration enables diverse AI energy applications such as demand response, load balancing, and the assimilation of renewable energy sources. Notably, a report from November 2021 by Guidehouse, a US-based consulting firm, revealed that approximately 979 megawatts of renewable microgrid capacity were installed in the US in 2021, with an expected surge to reach 32,470 megawatts by 2030. Hence, the escalating adoption of microgrids is poised to propel the growth of AI within the energy market.

The burgeoning demand for energy asset management is projected to steer the expansion of AI integration in the energy market. Energy asset management encapsulates a comprehensive and strategic approach aimed at overseeing, optimizing, and maximizing the performance and efficiency of energy-related assets within an organization or across various facilities. Leveraging artificial intelligence (AI), energy asset management delves into analyzing extensive datasets from energy assets, forecasting demand patterns, optimizing energy production and consumption, detecting anomalies, and implementing real-time adjustments. For instance, statistics from the International Energy Agency (IEA) in July 2023 unveiled a global increase of 2.4% in electricity generation in 2022, totaling around 700 terawatt-hours (TWh), underscoring the growing necessity for energy asset management. Consequently, the escalating demand for energy asset management is anticipated to drive the proliferation of AI within the energy market.

Advancements in technology stand out as a prominent and burgeoning trend in the AI-driven energy market. Leading entities within this sphere are actively embracing novel technologies to fortify their market standing. An illustration of this is Telefonaktiebolaget LM Ericsson, a Sweden-based IT services and consulting company, which, in February 2022, unveiled an AI-powered Energy Infrastructure Operations system. This innovative energy management system aids communications service providers in curtailing energy consumption across their network infrastructure through the utilization of artificial intelligence and sophisticated data analytics. Harnessing AI capabilities, this system targets a reduction of energy-related operational expenses (OPEX) by 15%, a corresponding decrease in passive infrastructure-related site visits by 15%, and a 30% mitigation of outages attributed to energy issues. This strategic approach not only trims OPEX and carbon emissions for communications service providers but also maximizes energy efficiency and site availability by leveraging AI and data analytics.

Significant players within the AI-powered energy market are spearheading innovation through the introduction of groundbreaking technologies, such as the Pangu Mine Model, marking the world's initial large-scale AI model commercially accessible within the energy sector. Tailored to tackle challenges prevalent in both the mining and broader energy domains, the Pangu Mine Model deploys expansive AI models in industrial production. For instance, 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 entity, collaborated on the launch of the Pangu Mine Model. This model encompasses features such as decoupled operation management, intelligent production capabilities, on-site data processing, extensive scalability, and the ability to learn and analyze from limited data samples. The model's core objective is to elevate the intelligence quotient within energy applications within the mining industry by broadening the spectrum of AI applications in various scenarios. The continuous utilization of AI aims to foster automation, enhance efficiency, decrease labor intensity, and elevate safety in energy utilization specifically for mining purposes.

In June 2022, Schneider Electric SE, a French electrical and electronics manufacturing company, successfully acquired AutoGrid Systems Inc. for an undisclosed amount. This strategic acquisition positions Schneider Electric to broaden its reach into new regions and offer energy companies across the globe the necessary tools to integrate over 1,000 GW of distributed and renewable energy resources into the grid. AutoGrid Systems Inc., based in the United States, is a software company specializing in AI-driven software designed to enhance the intelligence of distributed energy resources. The software facilitates prediction, optimization, and real-time control of energy resources.

Major companies operating in the AI in energy market report 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., Flex Ltd., 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 2023. 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, 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, Russia, South Korea, UK, USA, Canada, Italy, Spain.

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 energy market research report is one of a series of new reports that provides AI in energy market statistics, including the 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 scenarios of the industry.

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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