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市场调查报告书
商品编码
1936204

电网边缘智慧与分析市场规模、占有率及预测：依资料来源（智慧电錶、感测器、分散式能源）、A/ML 功能和应用（故障检测、预测）划分 - 全球预测至 2036 年

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
出版日期: | 出版商: Meticulous Research | 英文 277 Pages | 商品交期: 5-7个工作天内

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简介目录

全球电网边缘智慧与分析市场预计将从 2026 年的 24.7 亿美元成长至 2036 年的 112.3 亿美元，2026 年至 2036 年的复合年增长率 (CAGR) 为 16.4%。

电网边缘智慧与分析是指能够处理来自各种电网资产（包括智慧电錶、感测器、分散式能源 (DER) 和电网设备）的大量资料的软体平台和演算法。它们提供即时洞察、预测和自动化操作，以优化电网运行、提高可靠性并实现新的公用事业服务。这些系统的目标是将原始电网数据转化为有用的洞察。 这些人工智慧驱动的系统有助于实现主动电网管理，包括主动预测设备故障、优化分散式能源资源利用、侦测异常和诈欺行为以及支援决策。这些系统利用多种技术，包括用于模式识别和预测的机器学习、用于处理数十亿资料点的大数据分析以及用于自主决策的人工智慧。它们还采用边缘运算进行本地即时处理，利用预测分析预测电网状况和故障，利用深度学习识别复杂模式，并利用云端资料湖储存历史资料和资讯。电网边缘智慧系统可以提前数天甚至数週检测到设备故障的早期迹象，识别窃电和非技术性损耗，准确预测可再生能源发电量和负荷，优化电压和无功功率控制以提高效率，实现预测性维护以降低成本，并从大型数据集中提供可操作的洞察。

目录

第一章：引言

第二章：研究方法

第三章：摘要整理

  • 依资料来源划分的市场分析
  • 依人工智慧/机器学习功能划分的市场分析
  • 依应用划分的市场分析
  • 依部署模式划分的市场分析
  • 依分析类型划分的市场分析
  • 依应用功能划分的市场分析
  • 依地区划分的市场分析
  • 竞争分析

第四章 市场洞察

  • 市场驱动因素
    • 智慧电网基础设施推动电网数据呈指数级增长
    • 公用事业公司面临提高营运效率和降低成本的压力
    • 分散式能源的普及资源
  • 市场限制因素
    • 资料品质与整合挑战
    • 公用事业领域的 IT/OT 技能与变革管理
  • 市场机遇
    • 分散式能源资源整合与最佳化
    • 新兴市场公用事业数位转型
  • 市场挑战
    • 模型可解释性和监管认可
    • 网路安全与资料隐私
  • 市场趋势
    • 从云端运算到边缘运算分析的演进
    • 与营运系统整合以实现闭环自动化
  • 波特五力分析

第五章 电网边缘智慧技术与 AI/ML 架构

  • 电网机器学习演算法应用
  • 大数据处理架构
  • 边缘运算与分散式分析
  • 预测建模与预测技术
  • 深度学习与神经网络
  • 数位孪生与模拟模型
  • 可解释人工智慧和模型可解释性
  • 对市场成长与技术采用的影响

第六章：竞争格局

  • 关键成长策略
    • 市场差异化因素
    • 协同效应分析：关键交易与策略联盟
  • 竞争概览
    • 行业领导者
    • 市场差异化因素
    • 先驱者
    • 新兴公司
  • 供应商市场定位
  • 主要公司的市占率/排名

章节7 全球电网边缘智慧与分析市场（依资料来源划分）

  • 智慧电錶数据
    • 间隔用电量资料（15分钟、小时）
    • 电压和电能品质数据
    • 电錶事件与状态数据
  • 感测器和监控数据
    • 变电所监控
    • 馈线和线路感测器
    • 变压器监控
  • 分散式能源数据
    • 光电逆变器数据
    • 电池储能遥测数据
    • 电动车充电器数据
  • 天气与环境数据
  • 客户和GIS数据
  • 多源整合分析

第8章 全球电网边缘智慧与分析市场（依AI/ML功能划分）

  • 预测分析
    • 设备故障预测
    • 负载预测
    • 再生能源发电预测
  • 规范分析
    • 优化建议
    • 场景分析
  • 异常检测
    • 设备异常检测
    • 能耗异常检测
  • 模式识别与分类
  • 深度学习与神经网络
  • 强化学习最佳化

第九章 全球电网边缘智慧与分析市场（依应用划分）

  • 资产健康监测与预测性维护
    • 变压器健康监测
    • 断路器与开关监测
    • 电缆与导体分析
  • 负载与再生能源预测
    • 短期负载预测
    • 中长期预测
    • 太阳能和风能预测
  • 非技术性损耗检测
    • 窃电侦测
    • 电錶故障识别
    • 计费错误侦测
  • 电网优化及电压及无功功率控制
  • 停电预测与预防
  • 需求响应与负载管理
  • 分散式能源 (DER) 整合与最佳化
  • 客户分析与互动

第十章 全球电网边缘智慧与分析市场（依部署模式划分）

  • 云端分析
    • 公有云平台
    • 私有云解决方案
  • 本地部署分析
  • 混合云边缘架构
  • 边缘运算分析
    • 变电站边缘分析
    • 计量与设备边缘处理

第11章：全球电网边缘智慧与分析市场（依分析类型划分）

  • 描述性分析（历史分析）
  • 诊断性分析（根本原因分析）
  • 预测性分析（预测）
  • 规范分析（最佳化）
  • 即时串流分析
  • 批次分析

第12章：全球电网边缘智慧与分析市场（依公用事业功能划分）

  • 营运与工程
  • 资产管理
  • 客户服务与互动
  • 收入保障
  • 监理合规报告
  • 策略规划

第十三章：依地区划分的网格边缘智慧与分析市场

  • 北美
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 法国
    • 义大利
    • 西班牙
    • 荷兰
    • 北欧国家
    • 其他欧洲国家
  • 亚太地区
    • 中国
    • 印度
    • 日本
    • 韩国
    • 澳大利亚
    • 新加坡
    • 其他亚太地区
  • 拉丁美洲美洲
    • 巴西
    • 智利
    • 阿根廷
    • 其他拉丁美洲地区
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯联合大公国
    • 南非
    • 其他中东和非洲地区

第14章 企业简介

  • C3.ai Inc.
  • Oracle Corporation
  • Itron Inc.
  • Landis+Gyr Group AG
  • AutoGrid Systems Inc.
  • Bidgely Inc.
  • Sense(Sense Labs Inc.)
  • Grid4C(Innowatts)
  • Space-Time Insight(Nokia)
  • Uplight Inc.
  • Copper Labs Inc.
  • OhmConnect Inc.
  • Whisker Labs Inc.
  • Open Systems International Inc.(Emerson)
  • General Electric Company
  • Siemens AG
  • Schneider Electric SE
  • ABB Ltd.
  • Hitachi Energy Ltd.
  • Eaton Corporation
  • Other

第15章 附录

简介目录
Product Code: MREP - 1041685

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

TABLE OF CONTENTS

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