Generative AI in Energy Global Market Report 2024

The generative AI in energy market size has grown exponentially in recent years. It will grow from $0.75 billion in 2023 to $0.95 billion in 2024 at a compound annual growth rate (CAGR) of 26.3%. The growth in the historic period can be attributed to several factors, including the increasing adoption of renewable energy sources, the need for accurate demand forecasting, the rise of energy storage systems, optimization of demand response strategies, and heightened focus on risk management and resilience within the energy sector.

The generative AI in energy market size is expected to see exponential growth in the next few years. It will grow to $2.43 billion in 2028 at a compound annual growth rate (CAGR) of 26.5%. Forecast period growth can be attributed to several factors, including the pursuit of greater accuracy in energy management, the demand for enhanced electricity distribution systems, a heightened focus on engaging customers in energy consumption, the increasing adoption of solar and wind energy solutions, and a growing emphasis on effective asset management practices. Major trends expected during this period include advancements in real-time forecasting, dynamic adaptation and optimization techniques, improved predictive analytics capabilities, integration of advanced data sources for better insights, emphasis on predictive maintenance and asset management strategies, and the implementation of smarter grid management solutions.

The upward trend in solar electricity generation is expected to drive the growth of generative AI in the energy market in the foreseeable future. Solar electricity generation involves converting sunlight into electricity using photovoltaic (PV) panels or concentrated solar power (CSP) systems. The increasing adoption of solar electricity is fueled by decreasing costs of solar technology and heightened awareness of its environmental advantages, notably reduced carbon emissions compared to fossil fuels. Integrating solar electricity generation with generative AI technologies presents significant opportunities to improve the efficiency, reliability, and sustainability of energy systems, facilitating the transition to a cleaner and more resilient energy landscape. For example, data from August 2023, sourced from the Energy Information Administration, a US-based government agency, revealed that in 2022, the United States witnessed the addition of 10.9 gigawatts (GW) of new utility-scale solar capacity, marking the second-largest increase in a single year, following only the record-setting 13.5 GW added in 2021. Additionally, the country added a record-breaking 6.4 GW of new small-scale solar capacity in 2022, representing a 17% increase compared to the 5.5 GW added in 2021. Thus, the growth in solar electricity generation is propelling the adoption of generative AI in the energy market.

Key players in generative AI in the energy market are focusing on developing innovative products, such as real-time asset performance management solutions, to optimize energy production, distribution, and consumption processes. Real-time asset performance management involves monitoring, analyzing, and optimizing the performance of assets like machinery, equipment, or infrastructure in real-time or near real-time. For instance, in April 2024, Databricks Inc., a leading US-based global data, analytics, and artificial intelligence company, introduced the data intelligence platform for the energy sector. This unified platform harnesses the power of AI to empower data-driven decision-making in the energy industry. It addresses critical industry challenges through features like real-time asset performance management, renewable energy forecasting, and grid optimization, enabling organizations to enhance energy infrastructure and manage market volatility effectively. The Databricks data intelligence platform operates on a lakehouse architecture, providing an open, unified foundation for data and governance, and is driven by a data intelligence engine designed to understand data uniqueness.

In January 2023, Snowflake Inc., a prominent US-based cloud computing-based data cloud company, acquired Myst AI Inc. in an undisclosed transaction. This acquisition is part of Snowflake's strategy to integrate machine learning capabilities into its data cloud and strengthen its time series forecasting capabilities. Myst AI Inc. specializes in generative AI solutions tailored for the energy sector, contributing to Snowflake's efforts to enhance its offerings in the energy analytics domain.

Major companies operating in the generative AI in energy market are Google LLC, Microsoft Corporation, Engie SA, Enel Green Power S.p.A., Huawei Technologies Co. Ltd, Amazon Web Services Inc, Siemens AG, General Electric Company, Intel Corporation, International Business Machines Corporation, Deloitte Touche Tohmatsu Limited, Cisco Systems Inc, Schneider Electric SE, Honeywell International Inc., Flex Ltd, ABB Ltd, Duke Energy Corporation, Nvidia Corporation, Atos SE, Zen Robotics Ltd, Freshworks Inc., C3 AI inc, Databricks Inc, AppOrchid Inc, Verdigris Technologies, Ecube Labs Co. Ltd, Bidgely Inc.

North America was the largest region in the generative AI in energy market in 2023. Asia-Pacific is expected to be the fastest-growing region in the forecast period. The regions covered in the generative AI in energy market report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East, Africa. The countries covered in the generative AI in energy market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Russia, South Korea, UK, USA, Canada, Italy, Spain.

Generative AI in energy involves leveraging artificial intelligence techniques, like generative adversarial networks (GANs), to create synthetic data or models simulating energy-related processes. These AI systems aid in optimizing energy production, forecasting demand, enhancing grid stability, and developing efficient energy management strategies, thus advancing sustainability and reliability in the energy sector.

The primary components of generative AI in energy include solutions and services. Generative AI solutions in energy utilize artificial intelligence models to tackle various challenges and optimize operations within the energy sector. These solutions find applications in demand forecasting, renewable energy output prediction, grid management, energy trading, customer offerings, energy storage optimization, and more. End users encompass energy transmission, generation, distribution, utilities, and related sectors.

The generative AI in energy market research report is one of a series of new reports that provides generative AI in energy market statistics, including generative AI in energy industry global market size, regional shares, competitors with a generative AI in energy market share, detailed generative AI in energy market segments, market trends and opportunities, and any further data you may need to thrive in the generative AI in energy industry. This generative 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.

The generative AI in energy market consists of revenues earned by entities by providing services such as optimizing energy and utility grid management and performance, production capacity, demand patterns, streamlining operations and maintenance, predictive maintenance, energy trading and market analysis, and carbon emissions reduction. The market value includes the value of related goods sold by the service provider or included within the service offering. The generative AI in energy market also includes sales of Internet of Things (IoT) devices, sensors, smart meters, weather stations, voltage sensors, data acquisition systems, energy management systems, edge computing devices, and energy storage systems. 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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