AI in Energy Market Size, Share, Emerging Trends, Technologies, Top Companies, Future Opportunities, and Forecast 2030

Artificial Intelligence in Energy Market by Application (Energy Demand Forecasting, Grid optimization & management, Energy Storage Optimization), End Use (Generation, Transmission, Distribution, Consumption) - Global Forecast to 2030.
The global AI in energy market [https://www.marketsandmarkets.com/Market-Reports/ai-in-energy-market-231720509.html?utm_campaign=aiinenergymarket&utm_source=abnewswire.com&utm_medium=paidpr] is expected to grow significantly, from USD 8.91 billion in 2024 to USD 58.66 billion by 2030, reflecting a Compound Annual Growth Rate (CAGR) of 36.9% during the forecast period. The power grid, one of the largest and most complex systems ever constructed, requires ongoing development to ensure accessibility for all users, adequate energy generation to meet demand, and effective transmission of power to consumption points. Transitioning to a fully clean power grid will demand major investments in new infrastructure, including expanded clean energy production and distributed energy systems. Artificial Intelligence (AI) can play a crucial role in minimizing the scale of new infrastructure needed for a 100% clean grid by optimizing underutilized assets, improving coordination and efficiency in infrastructure projects, and leveraging advanced foundational models to process the vast and diverse technical data essential for this transformation.

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"Based on application, the grid optimization & management segment to hold the largest market size during the forecast period."

This AI in grid optimization and management enhances the strength, robustness, and efficiency of energy systems in distribution. It scans very large amounts of real-time data to detect inefficiencies, predict demand patterns, and engage in load balancing for the prevention of overload and possibilities of overloads and short-circuit outages. It adjusts grids dynamically, introduces renewable sources of energy in a very efficient manner, and minimizes energy losses in the transmission process. Moreover, AI-based automation enables faster responses to disruptions to ensure energy supply and efficiently maintain grid infrastructure. It is one of the applications that are paving the way toward modernization of the energy network and a move toward smart grids.

"The distribution segment to have the highest growth during the forecast period."

AI in energy distribution improves efficiency and reliability in power delivery by optimizing grid management and reducing losses. Realtime monitoring and predictive analytics help AI detect faults, anticipate equipment failure, and smooth out the flow of electricity across the grid. It can allow utilities to balance supply and demand better, ensuring steady power delivery, even during peak usage periods. AI algorithms may also optimize voltage regulation so that power is delivered efficiently and within safe limits. Also, AI facilitates the integration of renewable energy sources into the distribution network; it balances intermittent generation sources such as solar and wind with grid needs. With improved forecasting and dynamic control from AI, waste energy is minimized, operations respond better, and it is possible to automate some features of grid maintenance, thus bringing downtime and the cost of operations down. In general, AI is revolutionizing an energy distribution system to be much smarter and more dynamic.

"Asia Pacific is expected to hold the highest market growth rate during the forecast period."

State Power Rixin Technolog in collaboration with Huawei and China Huadian Corporation, launched a new energy meteorological power prediction solution in October 2024 in China that enhanced the prediction accuracy performance at a reduced operating cost for power plants. AI is also utilized to generate new energy efficiently and predict extreme weather impacts on renewable sources. Suola wind farm in Hebei province uses AI for intelligent control and management of wind and solar stations to become more efficient with low-cost manpower, thus achieving efficiency in service operations. In September 2024, KIER finalized its research on Urban Electrification with AI. This decreases the use of fossil fuel through integrated renewable energy in the source of energy from the city, for example, building-integrated solar technology. AI Energy Management Algorithms-in Model weather and human behavior optimize energy sharing and stabilize power grids during Low-Probability High-Impact Events. In June 2024, CSIRO collaborated with CoreLogic in launching RapidRate, the rate of AI-enabled estimation of the energy efficiency of existing homes. The CSIRO RapidRate AI tool assesses the energy efficiency of dwellings with minimal input. Utilizing a set of key factors based on floor area, orientation, and building materials, RapidRate applies the power of machine learning techniques to determine an indicative star rating consistent with the Nationwide House Energy Rating Scheme (NatHERS).

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Unique Features in the AI in Energy Market

AI enables the optimization of energy systems by analyzing real-time and historical data to improve energy distribution, reduce losses, and maximize efficiency. For instance, AI algorithms can predict energy demand, helping to balance generation and consumption effectively.

The integration of renewable energy sources like solar and wind into the grid presents challenges due to their intermittent nature. AI-driven solutions help address these issues by forecasting generation patterns and aligning them with energy demand, enhancing grid reliability and renewable energy utilization.

AI technologies provide advanced tools for grid monitoring, fault detection, and automated response systems. This ensures quick resolution of issues, minimizes downtime, and supports the transition to smart grids that can self-optimize and adapt to changing conditions.

AI has the potential to reduce the need for extensive infrastructure investments by optimizing the use of existing assets. Predictive analytics and machine learning models identify inefficiencies, enabling better asset utilization and prioritization of investments in critical areas.

AI facilitates dynamic pricing models and demand-response programs that encourage consumers to modify energy usage patterns. Smart algorithms analyze consumer behavior and suggest energy-saving opportunities, creating a more engaged and efficient energy market.

Major Highlights of the AI in Energy Market

AI is revolutionizing the energy sector through the development of smart grids that enhance energy distribution, monitor performance, and improve resilience. These grids use AI-powered tools for real-time data analysis, predictive maintenance, and automated issue resolution.

AI plays a pivotal role in integrating renewable energy sources by addressing their variability. Machine learning algorithms predict weather patterns and energy output, helping to stabilize the grid and maximize renewable energy utilization.

AI solutions help energy companies optimize the use of existing infrastructure, reducing operational costs and minimizing the need for new investments. Advanced analytics identify inefficiencies, prioritize upgrades, and ensure effective resource allocation.

AI supports global sustainability initiatives by accelerating the transition to clean energy and reducing carbon emissions. AI technologies optimize energy consumption, enhance the efficiency of renewable systems, and contribute to achieving carbon neutrality.

The rise of decentralized energy systems, including microgrids and distributed energy resources, is being supported by AI-driven solutions. These systems enhance energy security and resilience while allowing for localized energy generation and consumption.

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Top Companies in the 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), Atos zData (US), C3.ai (US), Tesla (US), Alpiq (Switzerland), Enel group (Italy), Origami Energy (UK), Innowatts (US), Irasus technologies (India), Grid4C (US), Uplight (US), GridBeyond (Ireland), eSmart Systems (Norway), Ndustrial (US), Datategy (France), Omdena (US). The market players have adopted various strategies to strengthen their AI in energy market position. Organic and inorganic strategies have helped the market players expand globally by providing energy solutions & services.

Schneider Electric SE

Schneider Electric SE is a French multinational corporation that specializes in digital automation and energy management. Schneider Electric offers AI-driven energy solutions for homes and industries. Its Wiser Home platform optimizes household energy consumption using machine learning, additionally its EcoStruxure Platform enhances industrial energy efficiency, sustainability, and productivity. Schneider Electric partnered with Ooredoo Qatar to collaborate on digital transformation projects. The partnership will focus on incorporation of innovative technologies such as cloud computing, artificial intelligence (AI), and eco-friendly data centers, enhancing efficiency and sustainability in sectors like utilities, healthcare, energy, and infrastructure.

GE Vernova

GE Vernova Inc. is an energy equipment manufacturing and services company. GE Vernova offers innovative solutions in energy segment, integrating AI and digital technologies. The solutions include Autonomous Inspection, AI/ML tools, and CERIUS for optimized asset performance, emissions reduction, and compliance. GE Vernova and U.S. Department of Energy collaborated to develop AI Assistant for permitting and trainings for hydrogen deployment. GE Vernova will lead a project team named H2Net, including Clemson University, and Roper Mountain Science Center. As part of this initiative, H2Net is will be to developing an AI Assistant that is trained specifically on the relevant, critical documents for safe H2 handling and permitting.

Siemens AG

Siemens AG is a German multinational technology company. It provides solutions & services across industrial automation, distributed energy resources, rail transport, and health technology segment. Siemens offers AI-powered solutions in energy through tools like the Hydrogen Plant Configurator and advanced automation systems. The configurator uses generative AI to streamline hydrogen plant design, providing detailed layouts and predicting key metrics. Its energy automation solutions integrate IoT, digital twins, and data analytics to enhance energy protection, communication, and operational efficiency. SparkCognition and Siemens partnered on a cybersecurity system, DeepArmor Industrial, fortified by Siemens, which is designed to protect endpoint, or remote, operational technology (OT) assets across the energy value chain by leveraging artificial intelligence to monitor and detect cyberattacks.

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