Market Size –
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 the rise of renewable energy sources, demand forecasting, increasing energy storage systems, demand response optimization, and increasing risk management and resilience.
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%. The growth in the forecast period can be attributed to increasing accuracy, demand for improved electricity distribution, increasing focus on customer engagement, increasing adoption of solar and wind energy, and rising asset management. Major trends in the forecast period include real-time forecasting, dynamic adaptation and optimization, enhanced predictive analytics, integration of advanced data sources, predictive maintenance and asset management, and smart grid management.
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Scope Of Generative AI In Energy Market
The Business Research Company’s reports encompass a wide range of information, including:
1. Market Size (Historic and Forecast): Analysis of the market’s historical performance and projections for future growth.
2. Drivers: Examination of the key factors propelling market growth.
3. Trends: Identification of emerging trends and patterns shaping the market landscape.
4. Key Segments: Breakdown of the market into its primary segments and their respective performance.
5. Focus Regions and Geographies: Insight into the most critical regions and geographical areas influencing the market.
6. Macro Economic Factors: Assessment of broader economic elements impacting the market.
Generative AI In Energy Market Overview
Market Drivers –
The increasing solar electricity generation is expected to propel the growth of generative AI in energy market going forward. Solar electricity generation converts sunlight into electricity using photovoltaic (PV) panels or concentrated solar power (CSP) systems. The increasing adoption of solar electricity is driven by the declining costs of solar technology and growing awareness of its environmental benefits, including reduced carbon emissions compared to fossil fuels. The integration of solar electricity generation with generative AI technologies offers significant opportunities to enhance the efficiency, reliability, and sustainability of energy systems for the transition towards a cleaner and more resilient energy future. For instance, in August 2023, according to the Energy Information Administration, a US-based government agency, in 2022, the United States saw the addition of 10.9 gigawatts (GW) of new utility-scale solar capacity, marking the second-largest increase in a single year, trailing 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. Therefore, increasing solar electricity generation is driving generative AI in energy market.
Market Trends –
Major companies operating in generative AI in the energy market are focused on developing innovative products such as real-time asset performance management to optimize energy production, distribution, and consumption processes. Real-time asset performance management refers to monitoring, analyzing, and optimizing the performance of assets, such as machinery, equipment, or infrastructure, in real-time or near real-time. For instance, in April 2024, Databricks Inc., a US-based global data, analytics, and artificial intelligence company, launched the data intelligence platform for energy. This unified platform brings the power of AI to data and people in the energy sector. The platform addresses critical industry challenges through real-time asset performance management, renewable energy forecasting, and grid optimization, empowering organizations to optimize energy infrastructure and mitigate market volatility. The Databricks data intelligence platform is built on a lakehouse architecture to provide an open, unified foundation for all data and governance. It is powered by a data intelligence engine that understands the uniqueness of data.
The generative ai in energy market covered in this report is segmented –
1) By Component: Solutions, Services
2) By Application: Demand Forecasting, Renewable Energy Output Forecasting, Grid Management And Optimization, Energy Trading And Pricing, Customer Offerings, Energy Storage Optimization, Other Applications
3) By End User: Energy Transmission, Energy Generation, Energy Distribution, Utilities, Other End Users
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Regional Insights –
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.
Key Companies –
Major companies operating in the generative AI in energy market report 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
Table of Contents
1. Executive Summary
2. Generative AI In Energy Market Characteristics
3. Generative AI In Energy Market Trends And Strategies
4. Generative AI In Energy Market – Macro Economic Scenario
5. Global Generative AI In Energy Market Size and Growth
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31. Generative AI In Energy Market Other Major And Innovative Companies
32. Global Generative AI In Energy Market Competitive Benchmarking
33. Global Generative AI In Energy Market Competitive Dashboard
34. Key Mergers And Acquisitions In The Generative AI In Energy Market
35. Generative AI In Energy Market Future Outlook and Potential Analysis
36.Appendix
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