Artificial Intelligence (AI) in energy refers to the utilization of AI technologies, algorithms and methodologies within the energy sector to enhance various aspects of energy production, distribution, consumption and management. AI is applied to analyze vast amounts of data, make predictions, optimize processes and automate tasks, leading to increased efficiency, reliability and sustainability in energy systems.
The AI in energy market consists of revenues earned by entities (organizations, sole traders and partnerships) by using AI technology in energy and providing related services and solutions, such as predictive maintenance solutions, grid optimization software, energy forecasting service, demand response platform, renewable energy optimization solutions, energy trading and market analysis tools and grid security solutions to the energy industry.
The global AI in energy market was valued at $7,773.0 million in 2018 which grew till 2023 at a compound annual growth rate (CAGR) of more than 15.0%.
Increasing Adoption Of Microgrids
The increasing adoption of microgrids has driven the growth of AI in energy market during the historic period. The increasing adoption of microgrids presents opportunities for leveraging AI to enhance energy management, grid stability, demand response, predictive maintenance, grid integration and scalability. By using microgrid infrastructure with AI-driven intelligence, energy stakeholders can unlock the full potential of distributed energy resources and accelerate the transition toward a more sustainable and resilient energy future. For instance, in 2021, according to a report from Guidehouse, a US-based management consulting company, the U.S. installed 979 MW of renewable energy microgrid capacity. Therefore, the increasing adoption of microgrids drove the AI in energy market.
Innovation In AI Energy Management Systems To Address Specific Market Needs
Companies in AI in energy market are introducing innovative AI-powered energy management systems to gain a competitive edge in the market. These AI-powered system leverage AI technologies to optimize energy consumption, improve operational efficiency and enhance overall sustainability in various sectors, such as utilities, manufacturing and commercial buildings. These systems typically integrate advanced analytics, machine learning algorithms and real-time data processing capabilities, to deliver actionable insights and automate decision-making processes.
For instance, in June 2023, Powerverse, a UK-based smart energy management company, launched its AI-powered energy management system in the UK. This innovative platform, developed by Lightsource bp's technology incubator, Lightsource Labs, leverages AI to provide best-in-class technology for a smarter, more affordable and connected electric world. Grounded by the industry-leading AI platform, called Powerverse Vesta, this system enables cheaper and cleaner energy consumption in real-time for households and businesses.
The top opportunities in the AI in energy market segmented by application will arise in the demand response management segment, which will gain $12,139.9 million of global annual sales by 2028.
AI In Energy Global Market Opportunities And Strategies To 2033 from The Business Research Company provides the strategists; marketers and senior management with the critical information they need to assess the global AI in energy market as it emerges from the COVID-19 shut down.
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Where is the largest and fastest-growing market for AI in energy? How does the market relate to the overall economy; demography and other similar markets? What forces will shape the market going forward? The AI in energy market global report from The Business Research Company answers all these questions and many more.
The report covers market characteristics; size and growth; segmentation; regional and country breakdowns; competitive landscape; market shares; trends and strategies for this market. It traces the market's history and forecasts market growth by geography. It places the market within the context of the wider AI in energy market; and compares it with other markets.
The report covers the following chapters
- Introduction and Market Characteristics- Brief introduction to the segmentations covered in the market, definitions and explanations about the segment by offering, by technology, by deployment mode, by offering and by end-user.
- Key Trends- Highlights the major trends shaping the global market. This section also highlights likely future developments in the market.
- Macro-Economic Scenario- The report provides an analysis of the impact of the COVID-19 pandemic, impact of the Russia-Ukraine war and impact of rising inflation on global and regional markets, providing strategic insights for businesses in the AI in energy market.
- Global Market Size And Growth- Global historic (2018-2023) and forecast (2023-2028, 2033F) market values and drivers and restraints that support and control the growth of the market in the historic and forecast periods.
- Regional And Country Analysis - Historic (2018-2023) and forecast (2023-2028, 2033F) market values and growth and market share comparison by region and country.
- Market Segmentation- Contains the market values (2018-2023) (2023-2028, 2033F) and analysis for each segment by offering, by technology, by deployment mode, by offering and by end-user in the market. Historic (2018-2023) and forecast (2023-2028) and (2028-2033) market values and growth and market share comparison by region market.
- Regional Market Size and Growth- Regional market size (2023), historic (2018-2023) and forecast (2023-2028, 2033F) market values and growth and market share comparison of countries within the region. This report includes information on all the regions Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East and Africa and major countries within each region.
- Competitive Landscape- Details on the competitive landscape of the market, estimated market shares and company profiles of the leading players.
- Competitive Benchmarking - Briefs on the financials comparison between major players in the market.
- Competitive Dashboard - Briefs on competitive dashboard of major players.
- Key Mergers And Acquisitions - Information on recent mergers and acquisitions in the market covered in the report. This section gives key financial details of mergers and acquisitions, which have shaped the market in recent years.
- Market Opportunities and Strategies Describes market opportunities and strategies based on findings of the research, with information on growth opportunities across countries, segments and strategies to be followed in those markets.
- Conclusions And Recommendations-This section includes recommendations for AI in energy providers in terms of product/service offerings geographic expansion, marketing strategies and target groups next five years.
- Appendix- This section includes details on the NAICS codes covered, abbreviations and currencies codes used in this report.
Markets Covered:
- 1) By Offering: Support Services; Hardware; AI-As-A-Service; Software
- 2) By Application: Demand Response Management; Fleet And Asset Management; Renewable Energy Management; Precision Drilling; Safety And Security; Infrastructure Management; Other Applications
- 3) By End-User: Energy Transmission; Energy Generation; Energy Distribution; Utilities; Other End Users
- 4) By Deployment: On-Premise; Cloud
- Companies Mentioned: Microsoft Corporation; Amazon.com, Inc.; Oracle Corporation; Schneider Electric SE; Alphabet Inc.
- Countries: China; Australia; India; Indonesia; Japan; South Korea; USA; Canada; Brazil; France; Germany; Italy; Spain; UK; Russia
- Regions: Asia-Pacific; Western Europe; Eastern Europe; North America; South America; Middle East; Africa.
- Time-series: Five years historic and ten years forecast.
- Data: Ratios of market size and growth to related markets; GDP proportions; expenditure per capita; AI in energy indicators comparison.
- Data segmentations: country and regional historic and forecast data; market share of competitors; market segments.
- Sourcing and Referencing: Data and analysis throughout the report is sourced using end notes.