Grid Edge Intelligence & Analytics Market Size, Share, & Forecast by Data Source (Smart Meters, Sensors, DERs), AI/ML Capability, and Application (Fault Detection, Forecasting)

Description

Grid Edge Intelligence & Analytics Market by Data Source (Smart Meters, Sensors, DERs), AI/ML Capability, and Application (Fault Detection, Forecasting) - Global Forecasts (2026-2036)

According to the research report titled, 'Grid Edge Intelligence & Analytics Market by Data Source (Smart Meters, Sensors, DERs), AI/ML Capability, and Application (Fault Detection, Forecasting) - Global Forecasts (2026-2036),' the global grid edge intelligence & analytics market is expected to reach USD 11.23 billion by 2036 from USD 2.47 billion in 2026, at a CAGR of 16.4% from 2026 to 2036.

Grid Edge Intelligence and Analytics are software platforms and algorithms that handle large amounts of data from various grid assets, including smart meters, sensors, distributed energy resources (DERs), and grid devices. They provide real-time insights, predictions, and automated actions to optimize grid operations, increase reliability, and allow for new utility services. These systems aim to turn raw grid data into useful intelligence. They help manage the grid proactively, predict equipment failures before they happen, optimize the use of distributed energy resources, detect anomalies and fraud, and support better decision making. These AI-driven systems use various technologies, such as machine learning for recognizing patterns and making predictions, big data analytics to process billions of data points, and artificial intelligence for making independent decisions. They also employ edge computing for local real-time processing, predictive analytics to forecast grid conditions and failures, deep learning for identifying complex patterns, and cloud-based data lakes for storing historical and information. Grid edge intelligence systems can spot early signs of equipment failures days or weeks in advance, identify energy theft and non-technical losses, accurately forecast renewable generation and load, and optimize volt-VAR control for efficiency, enable predictive maintenance to cut costs, and offer actionable insights from large data sets.

Key Players

The key players operating in the global grid edge intelligence & analytics market are Siemens AG, General Electric Company, Schneider Electric SE, Eaton Corporation, Itron Inc., Landis+Gyr, Xylem Inc., Eka Systems, Arcus Global, and others.

Market Segmentation

The grid edge intelligence & analytics market is segmented by data source (smart meter data, sensor data, distributed energy resource data), AI/ML capability (predictive analytics, prescriptive analytics, descriptive analytics), application (asset health monitoring and predictive maintenance, distributed energy resource optimization, demand forecasting, fraud detection), deployment model (cloud-based, on-premises, hybrid), and geography. The study also evaluates industry competitors and analyzes the market at the country level.

By Data Source

Based on data source, the smart meter data segment is estimated to hold the largest share of the market in 2026, driven by billions of smart meters deployed globally, granular consumption data generation, and proven analytics use cases for operations and customer engagement.

By AI/ML Capability

Based on AI/ML capability, the predictive analytics segment is estimated to dominate the market in 2026, owing to high-value use cases including equipment failure prediction, load forecasting, and maintenance optimization delivering clear ROI.

By Application

Based on application, the asset health monitoring and predictive maintenance segment is expected to witness significant growth during the forecast period, driven by aging infrastructure requiring proactive management and maintenance cost reduction pressures.

By Deployment Model

Based on deployment model, the cloud-based analytics segment is expected to account for the largest share of the market in 2026, fueled by scalability requirements for massive data volumes, advanced AI/ML capabilities, and cost-effective infrastructure.

Geographic Analysis

An in-depth geographic analysis of the industry provides detailed qualitative and quantitative insights into the five major regions (North America, Europe, Asia-Pacific, Latin America, and the Middle East & Africa) and the coverage of major countries in each region. In 2026, North America is estimated to account for the largest share of the global grid edge intelligence & analytics market, driven by mature smart grid infrastructure generating massive data volumes, advanced utility analytics adoption, vendor ecosystem leadership, and utility focus on data-driven operations. Asia-Pacific is projected to register the highest CAGR during the forecast period, fueled by massive smart meter deployments in China and India, grid modernization creating data infrastructure, AI technology development, and utility digital transformation initiatives.

Key Questions Answered in the Report

How big is the grid edge intelligence & analytics market?
What is the grid edge intelligence & analytics market growth?
Who are the major players in the global grid edge intelligence & analytics market?
Which are the driving factors of the grid edge intelligence & analytics market?
Which region will lead the global grid edge intelligence & analytics market?

Scope of the Report

By Data Source

Smart Meter Data
Sensor Data
Distributed Energy Resource (DER) Data

By AI/ML Capability

Predictive Analytics
Prescriptive Analytics
Descriptive Analytics

By Application

Asset Health Monitoring and Predictive Maintenance
Distributed Energy Resource Optimization
Demand Forecasting
Fraud Detection
Miscellaneous / Others

By Deployment Model

Cloud-Based
On-Premises
Hybrid

By Geography

North America
U.S.
Canada
Europe
Germany
U.K.
France
Italy
Spain
Rest of Europe
Asia-Pacific
China
India
Japan
South Korea
Rest of Asia-Pacific
Latin America
Middle East & Africa

Product Code: MREP - 1041685

1. Introduction

1.1. Market Definition
1.2. Market Ecosystem
1.3. Currency and Limitations
- 1.3.1. Currency
- 1.3.2. Limitations
1.4. Key Stakeholder

2. Research Methodology

2.1. Research Approach
2.2. Data Collection & Validation
- 2.2.1. Secondary Research
- 2.2.2. Primary Research
2.3. Market Assessment
- 2.3.1. Market Size Estimation
- 2.3.2. Bottom-Up Approach
- 2.3.3. Top-Down Approach
- 2.3.4. Growth Forecast
2.4. Assumptions for the Stud

3. Executive Summary

3.1. Overview
3.2. Market Analysis, by Data Source
3.3. Market Analysis, by AI/ML Capability
3.4. Market Analysis, by Application
3.5. Market Analysis, by Deployment Model
3.6. Market Analysis, by Analytics Type
3.7. Market Analysis, by Utility Function
3.8. Market Analysis, by Geography
3.9. Competitive Analysis

4. Market Insights

4.1. Introduction
4.2. Global Grid Edge Intelligence & Analytics Market: Impact Analysis of Market Drivers (2026-2036)
- 4.2.1. Exponential Grid Data Growth from Smart Grid Infrastructure
- 4.2.2. Utility Operational Efficiency and Cost Reduction Pressures
- 4.2.3. Distributed Energy Resource Proliferation
4.3. Global Grid Edge Intelligence & Analytics Market: Impact Analysis of Market Restraints (2026-2036)
- 4.3.1. Data Quality and Integration Challenges
- 4.3.2. Utility IT/OT Skillset and Change Management
4.4. Global Grid Edge Intelligence & Analytics Market: Impact Analysis of Market Opportunities (2026-2036)
- 4.4.1. Distributed Energy Resource Integration and Optimization
- 4.4.2. Emerging Markets Utility Digital Transformation
4.5. Global Grid Edge Intelligence & Analytics Market: Impact Analysis of Market Challenges (2026-2036)
- 4.5.1. Model Explainability and Regulatory Acceptance
- 4.5.2. Cybersecurity and Data Privacy
4.6. Global Grid Edge Intelligence & Analytics Market: Impact Analysis of Market Trends (2026-2036)
- 4.6.1. Evolution from Cloud to Edge Computing Analytics
- 4.6.2. Integration with Operational Systems for Closed-Loop Automation
4.7. Porter's Five Forces Analysis
- 4.7.1. Threat of New Entrants
- 4.7.2. Bargaining Power of Suppliers
- 4.7.3. Bargaining Power of Buyers
- 4.7.4. Threat of Substitute Products
- 4.7.5. Competitive Rivalry

5. Grid Edge Intelligence Technologies and AI/ML Architectures

5.1. Introduction to Grid Edge Analytics
5.2. Machine Learning Algorithms for Grid Applications
5.3. Big Data Processing Architectures
5.4. Edge Computing and Distributed Analytics
5.5. Predictive Modeling and Forecasting Techniques
5.6. Deep Learning and Neural Networks
5.7. Digital Twin and Simulation Models
5.8. Explainable AI and Model Interpretability
5.9. Impact on Market Growth and Technology Adoption

6. Competitive Landscape

6.1. Introduction
6.2. Key Growth Strategies
- 6.2.1. Market Differentiators
- 6.2.2. Synergy Analysis: Major Deals & Strategic Alliances
6.3. Competitive Dashboard
- 6.3.1. Industry Leaders
- 6.3.2. Market Differentiators
- 6.3.3. Vanguards
- 6.3.4. Emerging Companies
6.4. Vendor Market Positioning
6.5. Market Share/Ranking by Key Player

7. Global Grid Edge Intelligence & Analytics Market, by Data Source

7.1. Introduction
7.2. Smart Meter Data
- 7.2.1. Interval Consumption Data (15-min, Hourly)
- 7.2.2. Voltage and Power Quality Data
- 7.2.3. Meter Event and Status Data
7.3. Sensor and Monitoring Data
- 7.3.1. Substation Monitoring
- 7.3.2. Feeder and Line Sensors
- 7.3.3. Transformer Monitoring
7.4. Distributed Energy Resource Data
- 7.4.1. Solar Inverter Data
- 7.4.2. Battery Storage Telemetry
- 7.4.3. EV Charger Data
7.5. Weather and Environmental Data
7.6. Customer and GIS Data
7.7. Integrated Multi-Source Analytic

8. Global Grid Edge Intelligence & Analytics Market, by AI/ML Capability

8.1. Introduction
8.2. Predictive Analytics
- 8.2.1. Equipment Failure Prediction
- 8.2.2. Load Forecasting
- 8.2.3. Renewable Generation Forecasting
8.3. Prescriptive Analytics
- 8.3.1. Optimization Recommendations
- 8.3.2. Scenario Analysis
8.4. Anomaly Detection
- 8.4.1. Equipment Anomaly Detection
- 8.4.2. Consumption Anomaly Detection
8.5. Pattern Recognition and Classification
8.6. Deep Learning and Neural Networks
8.7. Reinforcement Learning for Optimization

9. Global Grid Edge Intelligence & Analytics Market, by Application

9.1. Introduction
9.2. Asset Health Monitoring and Predictive Maintenance
- 9.2.1. Transformer Health Monitoring
- 9.2.2. Breaker and Switch Monitoring
- 9.2.3. Cable and Conductor Analysis
9.3. Load and Renewable Forecasting
- 9.3.1. Short-Term Load Forecasting
- 9.3.2. Medium and Long-Term Forecasting
- 9.3.3. Solar and Wind Forecasting
9.4. Non-Technical Loss Detection
- 9.4.1. Energy Theft Detection
- 9.4.2. Meter Malfunction Identification
- 9.4.3. Billing Error Detection
9.5. Grid Optimization and Volt-VAR Control
9.6. Outage Prediction and Prevention
9.7. Demand Response and Load Management
9.8. DER Integration and Optimization
9.9. Customer Analytics and Engagement

10. Global Grid Edge Intelligence & Analytics Market, by Deployment Model

10.1. Introduction
10.2. Cloud-Based Analytics
- 10.2.1. Public Cloud Platforms
- 10.2.2. Private Cloud Solutions
10.3. On-Premise Analytics
10.4. Hybrid Cloud-Edge Architecture
10.5. Edge Computing Analytics
- 10.5.1. Substation Edge Analytics
- 10.5.2. Meter and Device Edge Processing

11. Global Grid Edge Intelligence & Analytics Market, by Analytics Type

11.1. Introduction
11.2. Descriptive Analytics (Historical Analysis)
11.3. Diagnostic Analytics (Root Cause Analysis)
11.4. Predictive Analytics (Forecasting)
11.5. Prescriptive Analytics (Optimization)
11.6. Real-Time Streaming Analytics
11.7. Batch Processing Analytic

12. Global Grid Edge Intelligence & Analytics Market, by Utility Function

12.1. Introduction
12.2. Operations and Engineering
12.3. Asset Management
12.4. Customer Service and Engagement
12.5. Revenue Assurance
12.6. Regulatory Compliance and Reporting
12.7. Strategic Planning

13. Grid Edge Intelligence & Analytics Market, by Geography

13.1. Introduction
13.2. North America
- 13.2.1. U.S.
- 13.2.2. Canada
- 13.2.3. Mexico
13.3. Europe
- 13.3.1. Germany
- 13.3.2. U.K.
- 13.3.3. France
- 13.3.4. Italy
- 13.3.5. Spain
- 13.3.6. Netherlands
- 13.3.7. Nordics
- 13.3.8. Rest of Europe
13.4. Asia-Pacific
- 13.4.1. China
- 13.4.2. India
- 13.4.3. Japan
- 13.4.4. South Korea
- 13.4.5. Australia
- 13.4.6. Singapore
- 13.4.7. Rest of Asia-Pacific
13.5. Latin America
- 13.5.1. Brazil
- 13.5.2. Chile
- 13.5.3. Argentina
- 13.5.4. Rest of Latin America
13.6. Middle East & Africa
- 13.6.1. Saudi Arabia
- 13.6.2. UAE
- 13.6.3. South Africa
- 13.6.4. Rest of Middle East & Afric

14. Company Profiles

14.1. C3.ai Inc.
14.2. Oracle Corporation
14.3. Itron Inc.
14.4. Landis+Gyr Group AG
14.5. AutoGrid Systems Inc.
14.6. Bidgely Inc.
14.7. Sense (Sense Labs Inc.)
14.8. Grid4C (Innowatts)
14.9. Space-Time Insight (Nokia)
14.10. Uplight Inc.
14.11. Copper Labs Inc.
14.12. OhmConnect Inc.
14.13. Whisker Labs Inc.
14.14. Open Systems International Inc. (Emerson)
14.15. General Electric Company
14.16. Siemens AG
14.17. Schneider Electric SE
14.18. ABB Ltd.
14.19. Hitachi Energy Ltd.
14.20. Eaton Corporation
14.21. Other

15. Appendix

15.1. Questionnaire
15.2. Available Customization

Grid Edge Intelligence & Analytics Market Size, Share, & Forecast by Data Source (Smart Meters, Sensors, DERs), AI/ML Capability, and Application (Fault Detection, Forecasting) - Global Forecast to 2036