Press release
Analysis of Key Market Segments Influencing the Edge Artificial Intelligence (AI) in Smart Grids Market
Edge Artificial Intelligence (AI) In Smart Grids Market
Projected Market Value and Growth Trajectory of the Edge AI in Smart Grids Market
The edge artificial intelligence in smart grids market is anticipated to experience remarkable growth in the coming years. Forecasts estimate the market will reach a value of $48.91 billion by 2030, expanding at an impressive compound annual growth rate (CAGR) of 25.9%. This surge is largely driven by the increasing use of edge AI for predictive analytics, the rollout of intelligent relays and processing units, and the growing adoption of AI-based grid management solutions. Additional factors supporting growth include heightened investments in smart grid cybersecurity and broadening applications in distributed energy resource management. Key trends expected to shape the market during this period include AI-powered grid optimization, real-time energy load forecasting, predictive maintenance and asset monitoring, the integration of smart sensors and edge devices, and enhanced energy management aimed at operational efficiency.
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Top Industry Players Leading the Edge AI in Smart Grids Market
Several prominent companies are actively shaping the edge AI in smart grids sector. These include 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., and ClearBlade Inc.
Notably, in March 2025, US-based technology firm Bidgely Inc. acquired Grid4C, another American company specializing in edge AI solutions for smart grids. This acquisition aims to accelerate Bidgely's AI-driven energy analytics capabilities and expand its UtilityAI platform by enhancing grid-side management, customer engagement, fault detection, and forecasting functions for distributed energy resources (DER) and load.
Innovative Developments and Growing Trends in Edge AI for Smart Grids
Leading market players are dedicating efforts to innovate with AI-enabled IoT edge compute cellular gateways that support real-time grid monitoring, automated decision-making, and optimized energy distribution. These gateways process data locally using AI, connect IoT devices, and transmit actionable insights over cellular networks, facilitating immediate control and management.
For example, in October 2024, Lantronix Inc. introduced the SmartLV Gateway, an AI-powered IoT edge compute cellular gateway crafted for low-voltage substations within smart grid and industrial settings. This gateway offers real-time automation, energy management, multi-protocol integration, and enhanced cybersecurity. Featuring LTE and 5G connectivity, the SmartLV Gateway enables dependable remote operations by processing data at the edge, reducing reliance on centralized cloud infrastructures. It also integrates smoothly with existing systems, helping utilities and industrial operators improve energy distribution efficiency, operational resilience, and grid stability.
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Detailed Segmentation Analysis of the Edge AI in Smart Grids Market
This market is broadly segmented to capture all critical aspects of edge AI in smart grids:
By Component:
- Hardware
- Software
- Services
By Deployment Model:
- On-Premises
- Cloud-Based
By Solution Type:
- Edge Intelligence Platforms
- Real-Time Grid Analytics Solutions
- Predictive Maintenance Solutions
- AI-Driven Control and Automation Services
- Cybersecurity and Risk Management Services
By Application:
- Grid Management
- Asset Management
- Advanced Metering Infrastructure (AMI)
- Distributed Energy Resource Management
- Other Applications
By End-User Industry:
- Electric Utilities
- Industrial Facilities
- Commercial Energy Operators
- Smart City and Infrastructure Projects
Further subcategories include:
Hardware: edge computing devices, sensors, actuators, networking equipment, processing units, and control modules.
Software: edge analytics platforms, machine learning software, predictive maintenance software, grid optimization software, and cybersecurity software.
Services: integration and consulting, managed services, training and support, maintenance, and cloud and edge computing services.
This comprehensive segmentation highlights the various components and applications that collectively drive the growth of the edge AI in smart grids market.
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