The Rise of AI in Energy Market: A $58.66 billion Industry Dominated by Schneider Electric SE, GE Vernova, ABB Ltd | MarketsandMarkets™

Delray Beach, FL, Dec. 19, 2024 (GLOBE NEWSWIRE) -- The global AI in Energy Market size is projected to grow from USD 8.91 billion in 2024 to USD 58.66 billion by 2030 at a Compound Annual Growth Rate (CAGR) of 36.9% during the forecast period, according to a new report by MarketsandMarkets™.

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AI in Energy Market Dynamics:

Drivers

• Energy market volatility and risk management
• Rising consumer demand for smart energy solutions

Restraints

• Data privacy and security
• High implementation costs

Opportunities

• Increasing shift toward carbon emission reduction and sustainability
• Renewable energy integration

List of Key Players in AI in Energy Market:

• Schneider Electric SE (France)
• GE Vernova (US)
• ABB Ltd (Switzerland)
• Honeywell International (US)
• Siemens AG (Germany)
• AWS (US)
• IBM (US)
• Microsoft (US)
• Oracle (US)
• Vestas Wind Systems A/S (Denmark)

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Many electrical generation plants maintain equipment using a schedule-based approach to servicing components. While the proportion of total operating costs differ significantly between the variety of plants, each type of plant can experience moderate to significant cost reductions if maintenance strategies shifted toward a predictive or risk-informed method. Using AI and ML techniques, plants can utilize historical maintenance data and OEM equipment specifications to tailor their maintenance strategy to conform to component condition in contrast to calendar-based strategies. This application has the potential to significantly reduce labor hours needed to keep the plant operational, while also avoiding wasteful replacement of equipment when the component still has a large remaining useful life..

By offering, the solutions segment is expected to have the largest market size during the forecast period. 

AI technologies in energy solutions meet critical operational and strategic demands in the industry, streamlining demand forecasting, grid management, and energy storage processes. Machine learning analyzes consumption patterns, and AI ensures more stable grids, balances out renewable energy sources, and reduces waste in energy use. It helps with trading in the energy market by analyzing trends, supports sustainability by lowering carbon emissions, and can be used for data streaming. It improves disaster resilience as it can forecast potential risks and allow quick recovery. Further, it detects energy theft and provides customer service by offering personalized products and simpler billing, thus making energy operations efficient, sustainable, and innovative.

By energy type, the renewable energy segment is expected to hold a higher growth rate during the forecast period.

AI in renewable energy optimizes the integration and management of diverse sources: solar, wind, hydropower, biomass, geothermal, hydrogen, and marine energy. In the case of solar and wind energies, AI enhances generation efficiency through weather pattern prediction and subsequent optimization of the level of energy capture. For hydroelectric power sources, AI optimizes the output while saving resources through balanced water flow management. Biomass benefits by optimizing feedstock and proper control on emissions. Advanced AI solutions enable integration of geothermal, hydrogen, and ocean energies while estimating the system behaviors and improving efficiency in storage and distribution. In both, AI maintains the grid stability, provides a balance between demand and supply, and reduces energy wastage.

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By Region, North America to have the largest market size during the forecast period.

In September 2024, The Department of Energy announced a USD 67 million investment in several AI projects from institutions in both government and academia as part of its AI for Science initiative. Six ORNL-led (or co-led) projects received funding. The projects were chosen based on competitive peer review under the DOE Funding Opportunity Announcement, or FOA, for Advancements in Artificial Intelligence for Science. Funding for these projects from DOE lasts up to three years. As part of Budget 2024, the Government of Canada has allocated USD 200 million to Canada's regional development agencies (RDAs) to help bring new AI technologies to market and help accelerate AI adoption in critical sectors such as agriculture, healthcare, clean technology, manufacturing and other sectors of regional importance. These investments reflect North America's commitment to fostering innovation in AI, driving advancements across various sectors and accelerating the adoption of AI technologies to address regional and global challenges.

A wide array of solutions optimized to ensure efficient energy operations are being offered by the key players in the AI in energy market. They provide AI-driven platforms for demand forecasting, grid management, and predictive maintenance. Solutions encompass the integration of renewable energy sources, smart energy trading systems, and energy sustainability management tools. Companies also focus on leveraging IoT, computer vision, and machine learning to monitor infrastructure and automate processes. Their innovations focus on cost savings, support for more reliable grid, and power a sustainable energy ecosystem.

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