Artificial Intelligence (AI) in Energy - Thematic Intelligence

Artificial Intelligence (AI) in Energy - Thematic Intelligence

Summary

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. GlobalData 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.

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 GlobalData’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 GlobalData’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 GlobalData’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.

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