Edge AI in Smart Grids Market Report 2026

The edge artificial intelligence (AI) in smart grids market size has grown exponentially in recent years. It will grow from $15.49 billion in 2025 to $19.46 billion in 2026 at a compound annual growth rate (CAGR) of 25.7%. The growth in the historic period can be attributed to rise in grid failures and outages, increasing demand for energy efficiency, growth in renewable energy integration, expansion of smart meter deployments, adoption of industrial iot in utilities.

The edge artificial intelligence (AI) in smart grids market size is expected to see exponential growth in the next few years. It will grow to $48.91 billion in 2030 at a compound annual growth rate (CAGR) of 25.9%. The growth in the forecast period can be attributed to implementation of edge ai for predictive analytics, deployment of intelligent relays and processing units, adoption of ai-driven grid management solutions, increasing investment in cybersecurity for smart grids, expansion of distributed energy resource management. Major trends in the forecast period include edge ai-driven grid optimization, real-time energy load forecasting, predictive maintenance and asset monitoring, integration of smart sensors and edge devices, energy management and operational efficiency.

The increasing energy demand is anticipated to support the expansion of the edge artificial intelligence (AI) in the smart grids market going forward. Energy demand represents the total amount of energy required by consumers, industries, and services to meet operational and daily needs over a defined period. The rise in energy demand is influenced by population growth, as a larger population increases consumption for electricity, transportation, heating, and other energy-intensive activities. Edge artificial intelligence in smart grids improves energy management by processing data locally in real time, enabling quicker decisions, reducing energy losses, and optimizing renewable energy integration for efficient and reliable power delivery. For example, in September 2025, according to the International Energy Agency, a US-based intergovernmental organization, total net electricity generation in the OECD reached 922.6 TWh in June, marking a 1.4% increase compared to June 2024. Therefore, the increasing energy demand is contributing to the expansion of edge artificial intelligence in the smart grids market.

Leading companies in the edge artificial intelligence in smart grids market are introducing innovative technologies, such as AI-enabled IoT edge compute cellular gateways, to support real-time grid monitoring, automated control, and optimized energy distribution. An AI-enabled IoT edge compute cellular gateway is a device that processes data locally using AI at the network edge, connects IoT systems, and transmits actionable insights via cellular networks for real-time control and monitoring. For example, in October 2024, Lantronix Inc., a US-based technology company, launched the SmartLV Gateway, an AI-powered IoT edge compute cellular gateway designed for low-voltage substations in smart grid and industrial environments. The SmartLV Gateway enables real-time automation, control, and energy management while incorporating edge AI processing, multi-protocol connectivity, and advanced cybersecurity features. With LTE and 5G communication capabilities, the device supports reliable remote operations and reduces reliance on centralized cloud systems by handling data locally. It integrates smoothly with existing infrastructure, allowing utilities and industrial operators to optimize energy flow, improve operational efficiency, and strengthen grid resilience.

In March 2025, Bidgely Inc., a US-based technology company, acquired Grid4C for an undisclosed amount. Through this acquisition, Bidgely accelerated development of its UtilityAI platform by expanding capabilities in grid-side intelligence, customer engagement, fault detection, and load and DER forecasting. Grid4C Inc. is a US-based provider of edge AI solutions designed for smart grid applications.

Major companies operating in the edge artificial intelligence (ai) in smart grids market are Siemens AG, Hitachi Energy Ltd., NVIDIA Corporation, Intel Corporation, Schneider Electric SE, Qualcomm Technologies Inc., GE Vernova, ABB Ltd., Hewlett Packard Enterprise Company, Beckwith Electric Co Inc., Itron Inc., Advantech Co. Ltd., Schweitzer Engineering Laboratories Inc., C3.ai Inc., SparkCognition Inc., Lantronix Inc., Uplight Inc., Utilidata Inc, Smarter Grid Solutions Ltd., ClearBlade Inc.

Tariffs have affected the edge artificial intelligence in smart grids market by increasing costs of imported edge computing devices, sensors, and specialized processing units. The impact is most notable on hardware components and software-integrated solutions in regions like North America and Europe that rely heavily on imported technologies. Some domestic manufacturers benefit as utilities and industrial operators turn to local suppliers to mitigate cost increases. Overall, tariffs drive both innovation in cost-effective edge AI solutions and a push for local supply chain development.

Edge artificial intelligence in smart grids refers to a set of coordinated energy management and grid intelligence initiatives aimed at improving power reliability, efficiency, and operational sustainability by applying artificial intelligence directly at the edge of electricity networks. These solutions typically involve the integration of edge computing devices, machine learning models, and real-time analytics to enable utilities and industrial operators to monitor, control, and optimize grid performance more efficiently.

The primary components of edge artificial intelligence in smart grids consist of hardware, software, and services. Hardware refers to physical devices such as sensors, edge servers, and communication modules that enable real-time data collection, processing, and artificial intelligence computation at the edge of the smart grid network. These solutions are deployed through on-premises and cloud-based deployment models. They include solution types such as edge intelligence platforms, real-time grid analytics solutions, predictive maintenance solutions, AI-driven control and automation services, and cybersecurity and risk management services. The solutions are used across applications including grid management, asset management, advanced metering infrastructure, distributed energy resource management, and other applications, and serve end-user industries such as electric utilities, industrial facilities, commercial energy operators, and smart city and infrastructure projects.

The edge artificial intelligence (AI) in smart grids market consists of revenues earned by entities by providing services such as edge artificial intelligence model development, edge device integration services, real-time grid monitoring services, predictive maintenance services, and energy load forecasting services. The market value includes the value of related goods sold by the service provider or included within the service offering. The edge artificial intelligence (AI) in smart grids market also includes sales of edge artificial intelligence processors, smart sensors, intelligent relays, edge gateways, industrial IoT controllers, smart meters, and embedded artificial intelligence modules. Values in this market are ‘factory gate’ values, that is, the value of goods sold by the manufacturers or creators of the goods, whether to other entities (including downstream manufacturers, wholesalers, distributors, and retailers) or directly to end customers. The value of goods in this market includes related services sold by the creators of the goods.

The market value is defined as the revenues that enterprises gain from the sale of goods and/or services within the specified market and geography through sales, grants, or donations in terms of the currency (in USD unless otherwise specified).

The revenues for a specified geography are consumption values that are revenues generated by organizations in the specified geography within the market, irrespective of where they are produced. It does not include revenues from resales along the supply chain, either further along the supply chain or as part of other products.

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