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市場調查報告書
商品編碼
1936204

電網邊緣智慧與分析市場規模、佔有率及預測：依資料來源（智慧電錶、感測器、分散式能源）、A/ML 功能和應用（故障檢測、預測）劃分 - 全球預測至 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個工作天內

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簡介目錄

全球電網邊緣智慧與分析市場預計將從 2026 年的 24.7 億美元成長至 2036 年的 112.3 億美元，2026 年至 2036 年的複合年增長率 (CAGR) 為 16.4%。

電網邊緣智慧與分析是指能夠處理來自各種電網資產（包括智慧電錶、感測器、分散式能源 (DER) 和電網設備）的大量資料的軟體平台和演算法。它們提供即時洞察、預測和自動化操作，以優化電網運行、提高可靠性並實現新的公用事業服務。這些系統的目標是將原始電網數據轉化為有用的洞察。 這些人工智慧驅動的系統有助於實現主動電網管理，包括主動預測設備故障、優化分散式能源資源利用、偵測異常和詐欺行為以及支援決策。這些系統利用多種技術，包括用於模式識別和預測的機器學習、用於處理數十億資料點的大數據分析以及用於自主決策的人工智慧。它們還採用邊緣運算進行本地即時處理，利用預測分析預測電網狀況和故障，利用深度學習識別複雜模式，並利用雲端資料湖儲存歷史資料和資訊。電網邊緣智慧系統可以提前數天甚至數週檢測到設備故障的早期跡象，識別竊電和非技術性損耗，準確預測可再生能源發電量和負荷，優化電壓和無功功率控制以提高效率，實現預測性維護以降低成本，並從大型數據集中提供可操作的洞察。

目錄

第一章：引言

第二章：研究方法

第三章：摘要整理

  • 依資料來源劃分的市場分析
  • 依人工智慧/機器學習功能劃分的市場分析
  • 依應用劃分的市場分析
  • 依部署模式劃分的市場分析
  • 依分析類型劃分的市場分析
  • 依應用功能劃分的市場分析
  • 依地區劃分的市場分析
  • 競爭分析

第四章 市場洞察

  • 市場驅動因素
    • 智慧電網基礎設施推動電網數據呈指數級增長
    • 公用事業公司面臨提高營運效率和降低成本的壓力
    • 分散式能源的普及資源
  • 市場限制因素
    • 資料品質與整合挑戰
    • 公用事業領域的 IT/OT 技能與變革管理
  • 市場機遇
    • 分散式能源資源整合與最佳化
    • 新興市場公用事業數位轉型
  • 市場挑戰
    • 模型可解釋性和監管認可
    • 網路安全與資料隱私
  • 市場趨勢
    • 從雲端運算到邊緣運算分析的演進
    • 與營運系統整合以實現閉環自動化
  • 波特五力分析

第五章 電網邊緣智慧技術與 AI/ML 架構

  • 電網機器學習演算法應用
  • 大數據處理架構
  • 邊緣運算與分散式分析
  • 預測建模與預測技術
  • 深度學習與神經網絡
  • 數位孿生與模擬模型
  • 可解釋人工智慧和模型可解釋性
  • 對市場成長與技術採用的影響

第六章：競爭格局

  • 關鍵成長策略
    • 市場差異化因素
    • 協同效應分析：關鍵交易與策略聯盟
  • 競爭概覽
    • 行業領導者
    • 市場差異化因素
    • 先驅者
    • 新興公司
  • 供應商市場定位
  • 主要公司的市佔率/排名

章節7 全球電網邊緣智慧與分析市場（依資料來源劃分）

  • 智慧電錶數據
    • 間隔用電量資料（15分鐘、小時）
    • 電壓和電能品質數據
    • 電錶事件與狀態數據
  • 感測器和監控數據
    • 變電所監控
    • 饋線和線路感測器
    • 變壓器監控
  • 分散式能源數據
    • 光電逆變器數據
    • 電池儲能遙測數據
    • 電動車充電器數據
  • 天氣與環境數據
  • 客戶和GIS數據
  • 多源整合分析

第8章 全球電網邊緣智慧與分析市場（依AI/ML功能劃分）

  • 預測分析
    • 設備故障預測
    • 負載預測
    • 再生能源發電預測
  • 規範分析
    • 優化建議
    • 場景分析
  • 異常檢測
    • 設備異常檢測
    • 能耗異常檢測
  • 模式識別與分類
  • 深度學習與神經網絡
  • 強化學習最佳化

第九章 全球電網邊緣智慧與分析市場（依應用劃分）

  • 資產健康監測與預測性維護
    • 變壓器健康監測
    • 斷路器與開關監測
    • 電纜與導體分析
  • 負載與再生能源預測
    • 短期負載預測
    • 中長期預測
    • 太陽能和風能預測
  • 非技術性損耗檢測
    • 竊電偵測
    • 電錶故障識別
    • 計費錯誤偵測
  • 電網優化及電壓及無功功率控制
  • 停電預測與預防
  • 需求響應與負載管理
  • 分散式能源 (DER) 整合與最佳化
  • 客戶分析與互動

第十章 全球電網邊緣智慧與分析市場（依部署模式劃分）

  • 雲端分析
    • 公有雲平台
    • 私有雲解決方案
  • 本地部署分析
  • 混合雲邊緣架構
  • 邊緣運算分析
    • 變電站邊緣分析
    • 計量與設備邊緣處理

第11章：全球電網邊緣智慧與分析市場（依分析類型劃分）

  • 描述性分析（歷史分析）
  • 診斷性分析（根本原因分析）
  • 預測性分析（預測）
  • 規範分析（最佳化）
  • 即時串流分析
  • 批次分析

第12章：全球電網邊緣智慧與分析市場（依公用事業功能劃分）

  • 營運與工程
  • 資產管理
  • 客戶服務與互動
  • 收入保障
  • 監理合規報告
  • 策略規劃

第十三章：依地區劃分的網格邊緣智慧與分析市場

  • 北美
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 法國
    • 義大利
    • 西班牙
    • 荷蘭
    • 北歐國家
    • 其他歐洲國家
  • 亞太地區
    • 中國
    • 印度
    • 日本
    • 韓國
    • 澳大利亞
    • 新加坡
    • 其他亞太地區
  • 拉丁美洲美洲
    • 巴西
    • 智利
    • 阿根廷
    • 其他拉丁美洲地區
  • 中東和非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 南非
    • 其他中東和非洲地區

第14章 企業簡介

  • 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 附錄

簡介目錄
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