AI is driving measurable improvements in renewable energy forecasting, grid operations and optimization, the coordination of distributed energy assets, and demand-side management. It will play a major role in enhancing asset optimization and customer segmentation. Implementing AI in the energy sector will benefit resource management, failure prevention, and predictive analytics for renewables. The publisher anticipates that the total AI market will be worth $909 billion in 2030, up from $81 billion in 2022.
The energy sector has historically been slow to adopt conversational platforms. Recent advancements in generative AI hold promise for elevating the existing AI framework within the energy sector. Large language models (LLMs) not only analyze data but also extract actionable insights to inform decision-making and strategy development. While the technology can resolve the sector's “black box” perception of AI, it glosses over the problem instead of addressing it. Investments in explainable AI will be required to overcome this issue fully.
The power industry is investing heavily in AI and machine learning (ML) to deliver the necessary solutions, such as sensor-connected power plants and smart grids to balance electricity supply and demand. AI technology can process large quantities of data, predict the likely outcomes, and assist in making decisions that will impact emission levels. The energy sector's inherent lack of innovation is a crucial hurdle for incorporating AI-based solutions. Energy equipment such as power stations and oil rigs typically have lifespans extending decades. This makes it difficult to ensure seamless compatibility and communication between existing infrastructure and AI solutions.
The energy sector has historically been slow to adopt conversational platforms. Recent advancements in generative AI hold promise for elevating the existing AI framework within the energy sector. Large language models (LLMs) not only analyze data but also extract actionable insights to inform decision-making and strategy development. While the technology can resolve the sector's “black box” perception of AI, it glosses over the problem instead of addressing it. Investments in explainable AI will be required to overcome this issue fully.
The power industry is investing heavily in AI and machine learning (ML) to deliver the necessary solutions, such as sensor-connected power plants and smart grids to balance electricity supply and demand. AI technology can process large quantities of data, predict the likely outcomes, and assist in making decisions that will impact emission levels. The energy sector's inherent lack of innovation is a crucial hurdle for incorporating AI-based solutions. Energy equipment such as power stations and oil rigs typically have lifespans extending decades. This makes it difficult to ensure seamless compatibility and communication between existing infrastructure and AI solutions.
Scope
- This report provides an overview of the AI theme. The detailed value chain comprises of four segments: human AI interaction, decision making AI, motion and creation. Leading and challenging vendors are identified across both segments.
- It identifies energy challenges, such as an aging workforce, the energy transition, energy security, industry consolidation, and a lack of innovation. The impact section identifies how AI addresses these challenges.
- It includes five case studies, outlining market-leading use cases of AI in energy to solve specific challenges.
- It contains comprehensive industry analysis, including forecasts for AI revenues to 2030, and insight from the publisher's Job Analytics, Patent Analytics, and Company Filing Analytics databases. It contains details of M&A deals driven by the AI theme, and a timeline highlighting AI milestones and events in energy.
- The report has extensive coverage and analysis of relevant companies' positions in the AI theme. This includes leading adopters, vendors, and specialist AI vendors in energy.
- It includes the publisher's unique thematic scorecard that ranks energy companies according to their positioning in the ten themes most important to the sector, of which AI is one.
Reasons to Buy
- This report will help you to understand AI and its potential impact on the energy sector.
- Benchmark your company against your competitors, by comparing how prepared companies in the energy sector are for AI disruption.
- Identify and differentiate between the leading AI vendors and formulate an adoption plan for your company.
- Position yourself for future success by investing in the right AI technologies. Cut through the noise with the publisher's priority ratings for each AI technology for each segment of the sector (upstream, midstream, downstream, equipment manufacture and services, engineering, procurement, construction, generation, transmission and distribution, and end-user).
- Develop relevant and credible sales and marketing messages for energy companies by understanding key sector challenges and where AI use cases are most useful.
- Identify attractive investment targets by understanding which companies are most advanced in the themes that will determine future success in the energy sector.
Table of Contents
- Executive Summary
- Players
- Energy Challenges
- Impact Assessment
- The impact of AI on power
- The impact of AI on oil and gas
- The impact of AI on energy challenges
- Case Studies
- AI Timeline
- Market Size and Growth Forecasts
- Signals
- Mergers and acquisitions
- Patent trends
- Company filings trends
- Hiring trends
- AI Value Chain
- Hardware
- Data management
- Foundational AI
- Advanced AI capabilities
- Delivery
- Companies
- Sector Scorecard
- Power utilities sector scorecard
- Integrated oil & gas sector scorecard
- Industrial automation sector scorecard
- Glossary
- Further Reading
- Our Thematic Research Methodology
- About the Publisher
- Contact the Publisher
- Challenges
- The AI story
- Mergers and Aquisitions
- Adopters
- Vendors
- Specialist vendors
- Scorecards
- Glossary
- Further reading
- Glossary
- Half of poll respondents claim they only partially understand AI
- AI has potential use cases across the entire energy value chain
- AI is being used to drive measurable improvements across the power value chain
- GE’s Bently Nevada 3500 vibration monitoring system
- Power companies are using AI across different power sources to manage and maintain assets
- Automated home energy systems coordinate household assets to optimize energy consumption
- AI is being used to enhance the extraction, transport, storage, and sale of hydrocarbons
- Oil and gas companies are using AI in upstream activities to optimize hydrocarbon production
- AI can be used to analyze the environment surrounding renewables equipment and forecast supply
- Most energy companies use smart monitoring to accelerate the energy transition
- Incumbent energy companies have more discretionary spending to direct into AI investments
- A prototype model of the Genix Copilot user interface
- Tatu is installed in 11 cabinets
- Global AI revenue will grow at a CAGR of 35.2% between 2022 and 2030
- AI-related patents in the energy sector grew exponentially between 2010 and 2022
- China leads AI-related patent activity in the energy sector by a wide margin
- AI-related filing mentions across the energy sector peaked in 2021
- Job vacancies across the energy sector have increased steadily since Q2 2020
- Siemens leads AI-related hiring in energy
- The AI value chain
Companies Mentioned (Partial List)
A selection of companies mentioned in this report includes, but is not limited to:
- ABB
- Alibaba
- Alphabet
- Amazon
- AMD
- Apple
- AutoGrid
- Baidu
- Bidgely
- BP
- C3.ai
- Cambricon
- Chevron
- Drift Marketplace
- Ecopetrol
- EDF
- Enel
- Fluence
- Hyundai
- Iberdrola
- IBM
- iFlytek
- Intel
- JERA
- Meta
- Microsoft
- Nesh
- Nvidia
- Orsted
- Petrobras
- Reliance Industries
- Repsol
- Rockwell
- Rosneft
- Saudi Aramco
- Shell
- Southern Company
- SparkCognition
- Stem
- Tencent
- Tesla
- Uplight
- XenonStack
