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市場調查報告書
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1994598

2026年全球電網邊緣相位辨識分析市場報告

Grid-Edge Phase Identification Analytics Global Market Report 2026
出版日期: | 出版商: The Business Research Company | 英文 250 Pages | 商品交期: 2-10個工作天內

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簡介目錄

近年來，電網邊緣相位辨識分析市場發展迅速。預計該市場規模將從2025年的11億美元成長到2026年的12.8億美元，複合年成長率（CAGR）為16.1%。成長要素包括智慧電錶的廣泛部署、配電網路的早期數位化、電網邊緣數據可用性的提高、配電分析工具的早期應用，以及對提高停電管理準確性的日益重視。

預計未來幾年電網邊緣相位辨識分析市場將快速成長，到2030年市場規模將達到23.4億美元，複合年成長率（CAGR）為16.4%。預測期內的成長預計將受到以下因素的推動：電網現代化投資的增加、分散式能源的日益普及、對電網自動化檢驗需求的成長、電力公司對雲端分析技術的日益重視，以及對電網韌性和可靠性的日益關注。預測期內的關鍵趨勢包括：基於機器學習的相位檢測技術得到更廣泛的應用、智慧電錶資料分析的日益普及、即時拓撲檢驗工具的整合度不斷提高、基於雲端的電網邊緣分析平台得到增強，以及對電網精度的日益關注。

分散式能源（DER）的日益普及預計將在未來幾年推動電網邊緣相位識別分析市場的成長。分散式能源是指在用電點或附近接入電網的小規模發電和儲能系統，例如屋頂太陽能電站、電池儲能系統和電動車充電基礎設施。分散式能源（DER）的日益普及源自於消費者層面向分散式可再生能源發電的轉變。電網邊緣相位識別分析透過精確映射分散式能源與配電相位的連接，為分散式能源提供支持，使電力運營商能夠最佳化負載分配、減少相位不平衡，並確保分散式發電在電網邊緣的安全接入。例如，根據總部位於阿拉伯聯合大公國的政府間機構－國際可再生能源機構（IRENA）預測，到2024年，全球可再生能源發電裝置容量將達到585吉瓦，佔總發電容量成長的90%以上，並且逐年成長。因此，分散式能源的日益普及正在推動電網邊緣相位識別分析市場的成長。

電網邊緣相位識別分析市場的主要企業正致力於開發創新解決方案，例如整合先進即時相位映射和運行智慧的AI驅動型電網邊緣分析平台，以滿足日益成長的電網可視性提升、分佈式能源（DER）快速併網以及故障和負載管理改進的需求。這項需求源自於電網現代化進程和配電網路日益複雜的現狀。基於AI的電網邊緣相位識別分析平台利用機器學習和人工智慧技術，持續處理來自智慧電錶、物聯網感測器和其他邊緣設備的大量電網數據，自動識別相位不平衡和連接模式。這使得電力公司能夠最佳化負載平衡和電網可靠性，而傳統的相位識別方法依賴於人工調查和有限的數據採樣，無法大規模、即時地實現這一目標。例如，2025年11月，總部位於法國的能源管理和自動化技術公司Schneider Electric推出了其「一體化數位電網平台」。這是一個模組化、人工智慧驅動的軟體平台，旨在透過將規劃、營運和資產管理與即時分析和預測洞察相結合，幫助電力公司實現電網營運現代化，從而提升整個電網的停電恢復能力、韌性和成本效益。 「一體化數位電網平台」利用人工智慧演算法整合各種電網資料流，估算停電恢復時間，並在無需昂貴的基礎設施改造的情況下增強決策能力。與缺乏一致的人工智慧驅動型營運工具的傳統電網管理系統相比，這是一個顯著的進步。

目錄

第1章執行摘要

第2章 市場特徵

  • 市場定義和範圍
  • 市場區隔
  • 主要產品和服務概述
  • 全球電網邊緣相位辨識分析市場：吸引力評分及分析
  • 成長潛力分析、競爭評估、策略適宜性評估、風險狀況評估

第3章 市場供應鏈分析

  • 供應鏈與生態系概述
  • 清單：主要原料、資源和供應商
  • 主要經銷商和通路合作夥伴名單
  • 主要最終用戶列表

第4章：全球市場趨勢與策略

  • 關鍵科技與未來趨勢
    • 人工智慧（AI）和自主人工智慧
    • 物聯網、智慧基礎設施、互聯生態系統
    • 數位化、雲端運算、巨量資料、網路安全
    • 工業4.0和智慧製造
    • 電動交通和交通運輸電氣化
  • 主要趨勢
    • 機器學習相位檢測技術的廣泛應用
    • 擴大智慧電錶資料分析的應用
    • 即時拓撲檢驗工具的整合工作正在推進中。
    • 擴展基於雲端的電網邊緣分析平台
    • 人們越來越關注配電網路的精確性

第5章 終端用戶產業市場分析

  • 公共產業
  • 配電網路營運商
  • 智慧電網解決方案供應商
  • 能源服務公司
  • 工業能源用戶

第6章 市場：宏觀經濟情景，包括利率、通貨膨脹、地緣政治、貿易戰和關稅的影響、關稅戰和貿易保護主義對供應鏈的影響，以及 COVID-19 疫情對市場的影響。

第7章：全球策略分析架構、目前市場規模、市場對比及成長率分析

  • 全球電網邊緣相位識別分析市場：PESTEL 分析（政治、社會、技術、環境、法律因素、促進因素和限制因素）
  • 全球電網邊緣相位辨識分析市場規模、對比及成長率分析
  • 全球電網邊緣相位辨識分析市場表現：規模與成長，2020-2025年
  • 全球電網邊緣相位辨識分析市場預測：規模與成長，2025-2030年，2035年

第8章：全球市場總規模（TAM）

第9章 市場細分

  • 按組件
  • 軟體、硬體和服務
  • 部署模式
  • 本地部署、雲端
  • 透過使用
  • 電網最佳化、停電管理、資產管理、負載預測及其他應用。
  • 按銷售管道
  • 直銷、經銷商、線上銷售
  • 最終用戶
  • 公共產業、工業、商業、住宅和其他最終用戶
  • 按類型細分：軟體
  • 相位辨識軟體、數據分析軟體、視覺化軟體、整合軟體、報告產生軟體
  • 按類型細分：硬體
  • 感測器模組、測量儀器、通訊介面、資料擷取單元、訊號處理單元
  • 按類型細分：服務
  • 諮詢服務、實施服務、維修服務、訓練服務、技術支援服務

第10章 區域與國別分析

  • 全球電網邊緣相位識別分析市場：按地區分類，實際數據和預測數據，2020-2025年、2025-2030年、2035年
  • 全球電網邊緣相位識別分析市場：按國家/地區分類，實際數據和預測數據，2020-2025 年、2025-2030 年、2035 年

第11章 亞太市場

第12章：中國市場

第13章：印度市場

第14章：日本市場

第15章：澳洲市場

第16章：印尼市場

第17章：韓國市場

第18章 台灣市場

第19章 東南亞市場

第20章 西歐市場

第21章英國市場

第22章：德國市場

第23章：法國市場

第24章：義大利市場

第25章：西班牙市場

第26章：東歐市場

第27章：俄羅斯市場

第28章 北美市場

第29章：美國市場

第30章：加拿大市場

第31章：南美市場

第32章：巴西市場

第33章 中東市場

第34章：非洲市場

第35章 市場監理與投資環境

第36章：競爭格局與公司概況

  • 電網邊緣相位辨識分析市場：競爭格局與市場佔有率，2024 年
  • 電網邊緣相位辨識分析市場：公司估值矩陣
  • 電網邊緣相位辨識分析市場：公司簡介
    • Siemens AG
    • Hitachi Energy Ltd.
    • International Business Machines Corporation（IBM）
    • Cisco Systems, Inc.
    • Oracle Corporation

第37章 其他大型企業和創新企業

  • Schneider Electric SE, Honeywell International Inc., ABB Ltd., Capgemini SE, Eaton Corporation plc, Itron, Inc., Landis+Gyr Group AG, Schweitzer Engineering Laboratories, Inc.（SEL）, S&C Electric Company, Aclara Technologies LLC（a Hubbell Company）, Enel X Srl, Kamstrup A/S, C3.ai, Inc., Uplight, Inc., Trilliant Holdings Inc.

第38章：全球市場競爭基準分析與儀錶板

第39章：預計進入市場的Start-Ups

第40章 重大併購

第41章 具有高市場潛力的國家、細分市場與策略

  • 2030年電網邊緣相位辨識分析市場：提供新機會的國家
  • 2030年電網邊緣相位辨識分析市場：提供新機會的細分市場
  • 2030年電網邊緣相位辨識分析市場：成長策略
    • 基於市場趨勢的策略
    • 競爭對手的策略

第42章附錄

簡介目錄
Product Code: UT6MGPIA01_G26Q1

Grid-edge phase identification analytics is a data-driven software tool that determines the accurate phase connectivity of customers and devices at the distribution grid edge. It examines voltage, current, and time-series data from smart meters, sensors, and distributed energy resources (DERs) to identify phase errors and mismatches. It enhances load balancing, outage management, and DER integration by ensuring correct phase identification throughout the grid.

The main components of grid-edge phase identification analytics include software, hardware, and services. Software encompasses analytics solutions that collect, process, and interpret grid-edge data to identify phase connections, optimize performance, and support decision-making. These solutions are deployed through on-premises and cloud modes. They are applied across grid optimization, outage management, asset management, load forecasting, and other applications, and are distributed via direct sales, distributors, and online channels. The solutions serve multiple end-users, including utilities, industrial, commercial, residential, and other stakeholders.

Tariffs are impacting the grid-edge phase identification analytics market by increasing costs of imported sensors, metering hardware, communication modules, and data acquisition devices used alongside analytics platforms. Utilities in North America and Europe are most affected due to reliance on imported grid-edge hardware, while Asia-Pacific faces cost pressures on large-scale smart grid rollouts. These tariffs are raising deployment costs and slowing some grid modernization programs. However, they are also encouraging software-centric analytics adoption, domestic hardware sourcing, and greater reliance on cloud-based phase identification solutions that reduce physical infrastructure dependency.

The grid-edge phase identification analytics market research report is one of a series of new reports from The Business Research Company that provides grid-edge phase identification analytics market statistics, including grid-edge phase identification analytics industry global market size, regional shares, competitors with a grid-edge phase identification analytics market share, detailed grid-edge phase identification analytics market segments, market trends and opportunities, and any further data you may need to thrive in the grid-edge phase identification analytics industry. This grid-edge phase identification analytics market research report delivers a complete perspective of everything you need, with an in-depth analysis of the current and future scenario of the industry.

The grid-edge phase identification analytics market size has grown rapidly in recent years. It will grow from $1.1 billion in 2025 to $1.28 billion in 2026 at a compound annual growth rate (CAGR) of 16.1%. The growth in the historic period can be attributed to expansion of smart meter deployments, early digitization of distribution networks, growing data availability at grid edge, initial adoption of distribution analytics tools, increasing focus on outage management accuracy.

The grid-edge phase identification analytics market size is expected to see rapid growth in the next few years. It will grow to $2.34 billion in 2030 at a compound annual growth rate (CAGR) of 16.4%. The growth in the forecast period can be attributed to increasing investments in distribution grid modernization, rising penetration of distributed energy resources, growing demand for automated grid validation, expansion of utility cloud analytics adoption, increasing focus on grid resilience and reliability. Major trends in the forecast period include increasing adoption of machine learning-based phase detection, rising use of smart meter data analytics, growing integration of real-time topology validation tools, expansion of cloud-based grid-edge analytics platforms, enhanced focus on distribution grid accuracy.

The rising penetration of distributed energy resources (DERs) is expected to drive the growth of the grid-edge phase identification analytics market in the coming years. Distributed energy resources refer to small-scale electricity generation and storage systems connected to the power grid at or near the point of use, including rooftop solar installations, battery energy storage systems, and electric vehicle charging infrastructure. The growing penetration of distributed energy resources (DERs) is driven by the increasing shift toward decentralized renewable energy generation at the consumer level. Grid-edge phase identification analytics supports distributed energy resources (DERs) by precisely mapping DER connections to distribution phases, allowing utilities to optimize load distribution, reduce phase imbalances, and ensure dependable integration of distributed generation at the grid edge. For instance, in March 2025, according to the International Renewable Energy Agency, a UAE-based intergovernmental organization, global renewable power capacity additions reached 585 GW in 2024, representing more than 90% of total power capacity expansion, an increase compared to previous years. Therefore, the growing adoption of distributed energy resources is driving the growth of the grid-edge phase identification analytics market.

Key companies operating in the grid-edge phase identification analytics market are focusing on developing innovative solutions, such as AI-enabled grid-edge analytics platforms that integrate advanced real-time phase mapping and operational intelligence, to meet the rising demand for enhanced grid visibility, rapid distributed energy resource (DER) integration, and improved outage and load management driven by grid modernization initiatives and the increasing complexity of distribution networks. AI-based grid-edge phase identification analytics platforms leverage machine learning and artificial intelligence to continuously process high-volume grid data from smart meters, IoT sensors, and other edge devices, automatically identify phase imbalances and connectivity patterns, and enable utilities to optimize load balancing and grid reliability capabilities that traditional phase identification methods, which relied on manual surveys and limited data sampling, could not deliver at scale or in real time. For example, in November 2025, Schneider Electric, a France-based energy management and automation technology company, launched its One Digital Grid Platform, a modular, AI-enabled software platform designed to help utilities modernize grid operations by combining planning, operations, and asset management with real-time analytics and predictive insights to improve outage restoration, resilience, and cost efficiency across distribution networks. The One Digital Grid Platform leverages AI algorithms to integrate diverse grid data streams, estimate restoration times during outages, and enhance decision-making without requiring costly infrastructure overhauls, making it a significant advancement over traditional grid management systems that lacked cohesive, AI-driven operational tools.

In December 2023, Uplight, a US-based provider of energy management and utility software solutions focused on customer engagement, load flexibility, and decarbonization platforms, acquired AutoGrid from Schneider Electric for an undisclosed amount. With this acquisition, Uplight aimed to broaden its capabilities by incorporating AutoGrid's advanced virtual power plant (VPP) and distributed energy resource management system (DERMS) technologies into a unified platform to better support utilities and energy stakeholders with improved grid flexibility and DER orchestration solutions. AutoGrid is a US-based provider of AI-driven software for managing distributed energy resources (DERs), including VPP, DERMS, and real-time optimization tools supporting renewable energy, electric vehicles, storage, and other grid assets.

Major companies operating in the grid-edge phase identification analytics market are Siemens AG, Hitachi Energy Ltd., International Business Machines Corporation (IBM), Cisco Systems, Inc., Oracle Corporation, Schneider Electric SE, Honeywell International Inc., ABB Ltd., Capgemini SE, Eaton Corporation plc, Itron, Inc., Landis+Gyr Group AG, Schweitzer Engineering Laboratories, Inc. (SEL), S&C Electric Company, Aclara Technologies LLC (a Hubbell Company), Enel X S.r.l., Kamstrup A/S, C3.ai, Inc., Uplight, Inc., Trilliant Holdings Inc.

North America was the largest region in the grid-edge phase identification analytics market in 2025. Asia-Pacific is expected to be the fastest-growing region in the forecast period. The regions covered in the grid-edge phase identification analytics market report are Asia-Pacific, South East Asia, Western Europe, Eastern Europe, North America, South America, Middle East, Africa.

The countries covered in the grid-edge phase identification analytics market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Taiwan, Russia, South Korea, UK, USA, Canada, Italy, Spain.

The grid-edge phase identification analytics market consists of revenues earned by entities by providing services such as grid-edge data collection and processing, advanced analytics and machine learning-based phase detection, real-time and periodic network topology validation, data visualization and reporting, and utility workflow automation support. The market value includes the value of related goods sold by the service provider or included within the service offering. The grid-edge phase identification analytics market includes sales of machine learning-based phase detection tools, data processing and visualization modules, application programming interfaces (APIs), cloud-based analytics products and subscriptions, and associated digital platforms. 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.

Grid-Edge Phase Identification Analytics Market Global Report 2026 from The Business Research Company provides strategists, marketers and senior management with the critical information they need to assess the market.

This report focuses grid-edge phase identification analytics market which is experiencing strong growth. The report gives a guide to the trends which will be shaping the market over the next ten years and beyond.

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Where is the largest and fastest growing market for grid-edge phase identification analytics ? How does the market relate to the overall economy, demography and other similar markets? What forces will shape the market going forward, including technological disruption, regulatory shifts, and changing consumer preferences? The grid-edge phase identification analytics market global report from the Business Research Company answers all these questions and many more.

The report covers market characteristics, size and growth, segmentation, regional and country breakdowns, total addressable market (TAM), market attractiveness score (MAS), competitive landscape, market shares, company scoring matrix, trends and strategies for this market. It traces the market's historic and forecast market growth by geography.

  • The market characteristics section of the report defines and explains the market. This section also examines key products and services offered in the market, evaluates brand-level differentiation, compares product features, and highlights major innovation and product development trends.
  • The supply chain analysis section provides an overview of the entire value chain, including key raw materials, resources, and supplier analysis. It also provides a list competitor at each level of the supply chain.
  • The updated trends and strategies section analyses the shape of the market as it evolves and highlights emerging technology trends such as digital transformation, automation, sustainability initiatives, and AI-driven innovation. It suggests how companies can leverage these advancements to strengthen their market position and achieve competitive differentiation.
  • The regulatory and investment landscape section provides an overview of the key regulatory frameworks, regularity bodies, associations, and government policies influencing the market. It also examines major investment flows, incentives, and funding trends shaping industry growth and innovation.
  • The market size section gives the market size ($b) covering both the historic growth of the market, and forecasting its development.
  • The forecasts are made after considering the major factors currently impacting the market. These include the technological advancements such as AI and automation, Russia-Ukraine war, trade tariffs (government-imposed import/export duties), elevated inflation and interest rates.
  • The total addressable market (TAM) analysis section defines and estimates the market potential compares it with the current market size, and provides strategic insights and growth opportunities based on this evaluation.
  • The market attractiveness scoring section evaluates the market based on a quantitative scoring framework that considers growth potential, competitive dynamics, strategic fit, and risk profile. It also provides interpretive insights and strategic implications for decision-makers.
  • Market segmentations break down the market into sub markets.
  • The regional and country breakdowns section gives an analysis of the market in each geography and the size of the market by geography and compares their historic and forecast growth.
  • Expanded geographical coverage includes Taiwan and Southeast Asia, reflecting recent supply chain realignments and manufacturing shifts in the region. This section analyzes how these markets are becoming increasingly important hubs in the global value chain.
  • The competitive landscape chapter gives a description of the competitive nature of the market, market shares, and a description of the leading companies. Key financial deals which have shaped the market in recent years are identified.
  • The company scoring matrix section evaluates and ranks leading companies based on a multi-parameter framework that includes market share or revenues, product innovation, and brand recognition.

Scope

  • Markets Covered:1) By Component: Software; Hardware; Services
  • 2) By Deployment Mode: On-Premises; Cloud
  • 3) By Application: Grid Optimization; Outage Management; Asset Management; Load Forecasting; Other Applications
  • 4) By Sales Channel: Direct Sales; Distributors; Online Sales
  • 5) By End-User: Utilities; Industrial; Commercial; Residential; Other End Users
  • Subsegments:
  • 1) By Software: Phase Identification Software; Data Analytics Software; Visualization Software; Integration Software; Reporting Software
  • 2) By Hardware: Sensor Modules; Metering Devices; Communication Interfaces; Data Acquisition Units; Signal Processing Units
  • 3) By Services: Consulting Services; Deployment Services; Maintenance Services; Training Services; Technical Support Services
  • Companies Mentioned: Siemens AG; Hitachi Energy Ltd.; International Business Machines Corporation (IBM); Cisco Systems; Inc.; Oracle Corporation; Schneider Electric SE; Honeywell International Inc.; ABB Ltd.; Capgemini SE; Eaton Corporation plc; Itron; Inc.; Landis+Gyr Group AG; Schweitzer Engineering Laboratories; Inc. (SEL); S&C Electric Company; Aclara Technologies LLC (a Hubbell Company); Enel X S.r.l.; Kamstrup A/S; C3.ai; Inc.; Uplight; Inc.; Trilliant Holdings Inc.
  • Countries: Australia; Brazil; China; France; Germany; India; Indonesia; Japan; Taiwan; Russia; South Korea; UK; USA; Canada; Italy; Spain
  • Regions: Asia-Pacific; South East Asia; Western Europe; Eastern Europe; North America; South America; Middle East; Africa
  • Time Series: Five years historic and ten years forecast.
  • Data: Ratios of market size and growth to related markets, GDP proportions, expenditure per capita,
  • Data Segmentations: country and regional historic and forecast data, market share of competitors, market segments.
  • Sourcing and Referencing: Data and analysis throughout the report is sourced using end notes.
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Table of Contents

1. Executive Summary

  • 1.1. Key Market Insights (2020-2035)
  • 1.2. Visual Dashboard: Market Size, Growth Rate, Hotspots
  • 1.3. Major Factors Driving the Market
  • 1.4. Top Three Trends Shaping the Market

2. Grid-Edge Phase Identification Analytics Market Characteristics

  • 2.1. Market Definition & Scope
  • 2.2. Market Segmentations
  • 2.3. Overview of Key Products and Services
  • 2.4. Global Grid-Edge Phase Identification Analytics Market Attractiveness Scoring And Analysis
    • 2.4.1. Overview of Market Attractiveness Framework
    • 2.4.2. Quantitative Scoring Methodology
    • 2.4.3. Factor-Wise Evaluation
  • Growth Potential Analysis, Competitive Dynamics Assessment, Strategic Fit Assessment And Risk Profile Evaluation
    • 2.4.4. Market Attractiveness Scoring and Interpretation
    • 2.4.5. Strategic Implications and Recommendations

3. Grid-Edge Phase Identification Analytics Market Supply Chain Analysis

  • 3.1. Overview of the Supply Chain and Ecosystem
  • 3.2. List Of Key Raw Materials, Resources & Suppliers
  • 3.3. List Of Major Distributors and Channel Partners
  • 3.4. List Of Major End Users

4. Global Grid-Edge Phase Identification Analytics Market Trends And Strategies

  • 4.1. Key Technologies & Future Trends
    • 4.1.1 Artificial Intelligence & Autonomous Intelligence
    • 4.1.2 Internet Of Things (Iot), Smart Infrastructure & Connected Ecosystems
    • 4.1.3 Digitalization, Cloud, Big Data & Cybersecurity
    • 4.1.4 Industry 4.0 & Intelligent Manufacturing
    • 4.1.5 Electric Mobility & Transportation Electrification
  • 4.2. Major Trends
    • 4.2.1 Increasing Adoption Of Machine Learning-Based Phase Detection
    • 4.2.2 Rising Use Of Smart Meter Data Analytics
    • 4.2.3 Growing Integration Of Real-Time Topology Validation Tools
    • 4.2.4 Expansion Of Cloud-Based Grid-Edge Analytics Platforms
    • 4.2.5 Enhanced Focus On Distribution Grid Accuracy

5. Grid-Edge Phase Identification Analytics Market Analysis Of End Use Industries

  • 5.1 Utilities
  • 5.2 Distribution Network Operators
  • 5.3 Smart Grid Solution Providers
  • 5.4 Energy Service Companies
  • 5.5 Industrial Energy Users

6. Grid-Edge Phase Identification Analytics Market - Macro Economic Scenario Including The Impact Of Interest Rates, Inflation, Geopolitics, Trade Wars and Tariffs, Supply Chain Impact from Tariff War & Trade Protectionism, And Covid And Recovery On The Market

7. Global Grid-Edge Phase Identification Analytics Strategic Analysis Framework, Current Market Size, Market Comparisons And Growth Rate Analysis

  • 7.1. Global Grid-Edge Phase Identification Analytics PESTEL Analysis (Political, Social, Technological, Environmental and Legal Factors, Drivers and Restraints)
  • 7.2. Global Grid-Edge Phase Identification Analytics Market Size, Comparisons And Growth Rate Analysis
  • 7.3. Global Grid-Edge Phase Identification Analytics Historic Market Size and Growth, 2020 - 2025, Value ($ Billion)
  • 7.4. Global Grid-Edge Phase Identification Analytics Forecast Market Size and Growth, 2025 - 2030, 2035F, Value ($ Billion)

8. Global Grid-Edge Phase Identification Analytics Total Addressable Market (TAM) Analysis for the Market

  • 8.1. Definition and Scope of Total Addressable Market (TAM)
  • 8.2. Methodology and Assumptions
  • 8.3. Global Total Addressable Market (TAM) Estimation
  • 8.4. TAM vs. Current Market Size Analysis
  • 8.5. Strategic Insights and Growth Opportunities from TAM Analysis

9. Grid-Edge Phase Identification Analytics Market Segmentation

  • 9.1. Global Grid-Edge Phase Identification Analytics Market, Segmentation By Component, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion
  • Software, Hardware, Services
  • 9.2. Global Grid-Edge Phase Identification Analytics Market, Segmentation By Deployment Mode, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion
  • On-Premises, Cloud
  • 9.3. Global Grid-Edge Phase Identification Analytics Market, Segmentation By Application, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion
  • Grid Optimization, Outage Management, Asset Management, Load Forecasting, Other Applications
  • 9.4. Global Grid-Edge Phase Identification Analytics Market, Segmentation By Sales Channel, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion
  • Direct Sales, Distributors, Online Sales
  • 9.5. Global Grid-Edge Phase Identification Analytics Market, Segmentation By End-User, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion
  • Utilities, Industrial, Commercial, Residential, Other End Users
  • 9.6. Global Grid-Edge Phase Identification Analytics Market, Sub-Segmentation Of Software, By Type, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion
  • Phase Identification Software, Data Analytics Software, Visualization Software, Integration Software, Reporting Software
  • 9.7. Global Grid-Edge Phase Identification Analytics Market, Sub-Segmentation Of Hardware, By Type, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion
  • Sensor Modules, Metering Devices, Communication Interfaces, Data Acquisition Units, Signal Processing Units
  • 9.8. Global Grid-Edge Phase Identification Analytics Market, Sub-Segmentation Of Services, By Type, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion
  • Consulting Services, Deployment Services, Maintenance Services, Training Services, Technical Support Services

10. Grid-Edge Phase Identification Analytics Market Regional And Country Analysis

  • 10.1. Global Grid-Edge Phase Identification Analytics Market, Split By Region, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion
  • 10.2. Global Grid-Edge Phase Identification Analytics Market, Split By Country, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

11. Asia-Pacific Grid-Edge Phase Identification Analytics Market

  • 11.1. Asia-Pacific Grid-Edge Phase Identification Analytics Market Overview
  • Region Information, Market Information, Background Information, Government Initiatives, Regulations, Regulatory Bodies, Major Associations, Taxes Levied, Corporate Tax Structure, Investments, Major Companies
  • 11.2. Asia-Pacific Grid-Edge Phase Identification Analytics Market, Segmentation By Component, Segmentation By Deployment Mode, Segmentation By Application, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

12. China Grid-Edge Phase Identification Analytics Market

  • 12.1. China Grid-Edge Phase Identification Analytics Market Overview
  • Country Information, Market Information, Background Information, Government Initiatives, Regulations, Regulatory Bodies, Major Associations, Taxes Levied, Corporate Tax Structure, Investments, Major Companies
  • 12.2. China Grid-Edge Phase Identification Analytics Market, Segmentation By Component, Segmentation By Deployment Mode, Segmentation By Application, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

13. India Grid-Edge Phase Identification Analytics Market

  • 13.1. India Grid-Edge Phase Identification Analytics Market, Segmentation By Component, Segmentation By Deployment Mode, Segmentation By Application, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

14. Japan Grid-Edge Phase Identification Analytics Market

  • 14.1. Japan Grid-Edge Phase Identification Analytics Market Overview
  • Country Information, Market Information, Background Information, Government Initiatives, Regulations, Regulatory Bodies, Major Associations, Taxes Levied, Corporate Tax Structure, Investments, Major Companies
  • 14.2. Japan Grid-Edge Phase Identification Analytics Market, Segmentation By Component, Segmentation By Deployment Mode, Segmentation By Application, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

15. Australia Grid-Edge Phase Identification Analytics Market

  • 15.1. Australia Grid-Edge Phase Identification Analytics Market, Segmentation By Component, Segmentation By Deployment Mode, Segmentation By Application, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

16. Indonesia Grid-Edge Phase Identification Analytics Market

  • 16.1. Indonesia Grid-Edge Phase Identification Analytics Market, Segmentation By Component, Segmentation By Deployment Mode, Segmentation By Application, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

17. South Korea Grid-Edge Phase Identification Analytics Market

  • 17.1. South Korea Grid-Edge Phase Identification Analytics Market Overview
  • Country Information, Market Information, Background Information, Government Initiatives, Regulations, Regulatory Bodies, Major Associations, Taxes Levied, Corporate Tax Structure, Investments, Major Companies
  • 17.2. South Korea Grid-Edge Phase Identification Analytics Market, Segmentation By Component, Segmentation By Deployment Mode, Segmentation By Application, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

18. Taiwan Grid-Edge Phase Identification Analytics Market

  • 18.1. Taiwan Grid-Edge Phase Identification Analytics Market Overview
  • Country Information, Market Information, Background Information, Government Initiatives, Regulations, Regulatory Bodies, Major Associations, Taxes Levied, Corporate Tax Structure, Investments, Major Companies
  • 18.2. Taiwan Grid-Edge Phase Identification Analytics Market, Segmentation By Component, Segmentation By Deployment Mode, Segmentation By Application, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

19. South East Asia Grid-Edge Phase Identification Analytics Market

  • 19.1. South East Asia Grid-Edge Phase Identification Analytics Market Overview
  • Region Information, Market Information, Background Information, Government Initiatives, Regulations, Regulatory Bodies, Major Associations, Taxes Levied, Corporate Tax Structure, Investments, Major Companies
  • 19.2. South East Asia Grid-Edge Phase Identification Analytics Market, Segmentation By Component, Segmentation By Deployment Mode, Segmentation By Application, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

20. Western Europe Grid-Edge Phase Identification Analytics Market

  • 20.1. Western Europe Grid-Edge Phase Identification Analytics Market Overview
  • Region Information, Market Information, Background Information, Government Initiatives, Regulations, Regulatory Bodies, Major Associations, Taxes Levied, Corporate Tax Structure, Investments, Major Companies
  • 20.2. Western Europe Grid-Edge Phase Identification Analytics Market, Segmentation By Component, Segmentation By Deployment Mode, Segmentation By Application, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

21. UK Grid-Edge Phase Identification Analytics Market

  • 21.1. UK Grid-Edge Phase Identification Analytics Market, Segmentation By Component, Segmentation By Deployment Mode, Segmentation By Application, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

22. Germany Grid-Edge Phase Identification Analytics Market

  • 22.1. Germany Grid-Edge Phase Identification Analytics Market, Segmentation By Component, Segmentation By Deployment Mode, Segmentation By Application, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

23. France Grid-Edge Phase Identification Analytics Market

  • 23.1. France Grid-Edge Phase Identification Analytics Market, Segmentation By Component, Segmentation By Deployment Mode, Segmentation By Application, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

24. Italy Grid-Edge Phase Identification Analytics Market

  • 24.1. Italy Grid-Edge Phase Identification Analytics Market, Segmentation By Component, Segmentation By Deployment Mode, Segmentation By Application, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

25. Spain Grid-Edge Phase Identification Analytics Market

  • 25.1. Spain Grid-Edge Phase Identification Analytics Market, Segmentation By Component, Segmentation By Deployment Mode, Segmentation By Application, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

26. Eastern Europe Grid-Edge Phase Identification Analytics Market

  • 26.1. Eastern Europe Grid-Edge Phase Identification Analytics Market Overview
  • Region Information, Market Information, Background Information, Government Initiatives, Regulations, Regulatory Bodies, Major Associations, Taxes Levied, Corporate Tax Structure, Investments, Major Companies
  • 26.2. Eastern Europe Grid-Edge Phase Identification Analytics Market, Segmentation By Component, Segmentation By Deployment Mode, Segmentation By Application, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

27. Russia Grid-Edge Phase Identification Analytics Market

  • 27.1. Russia Grid-Edge Phase Identification Analytics Market, Segmentation By Component, Segmentation By Deployment Mode, Segmentation By Application, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

28. North America Grid-Edge Phase Identification Analytics Market

  • 28.1. North America Grid-Edge Phase Identification Analytics Market Overview
  • Region Information, Market Information, Background Information, Government Initiatives, Regulations, Regulatory Bodies, Major Associations, Taxes Levied, Corporate Tax Structure, Investments, Major Companies
  • 28.2. North America Grid-Edge Phase Identification Analytics Market, Segmentation By Component, Segmentation By Deployment Mode, Segmentation By Application, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

29. USA Grid-Edge Phase Identification Analytics Market

  • 29.1. USA Grid-Edge Phase Identification Analytics Market Overview
  • Country Information, Market Information, Background Information, Government Initiatives, Regulations, Regulatory Bodies, Major Associations, Taxes Levied, Corporate Tax Structure, Investments, Major Companies
  • 29.2. USA Grid-Edge Phase Identification Analytics Market, Segmentation By Component, Segmentation By Deployment Mode, Segmentation By Application, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

30. Canada Grid-Edge Phase Identification Analytics Market

  • 30.1. Canada Grid-Edge Phase Identification Analytics Market Overview
  • Country Information, Market Information, Background Information, Government Initiatives, Regulations, Regulatory Bodies, Major Associations, Taxes Levied, Corporate Tax Structure, Investments, Major Companies
  • 30.2. Canada Grid-Edge Phase Identification Analytics Market, Segmentation By Component, Segmentation By Deployment Mode, Segmentation By Application, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

31. South America Grid-Edge Phase Identification Analytics Market

  • 31.1. South America Grid-Edge Phase Identification Analytics Market Overview
  • Region Information, Market Information, Background Information, Government Initiatives, Regulations, Regulatory Bodies, Major Associations, Taxes Levied, Corporate Tax Structure, Investments, Major Companies
  • 31.2. South America Grid-Edge Phase Identification Analytics Market, Segmentation By Component, Segmentation By Deployment Mode, Segmentation By Application, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

32. Brazil Grid-Edge Phase Identification Analytics Market

  • 32.1. Brazil Grid-Edge Phase Identification Analytics Market, Segmentation By Component, Segmentation By Deployment Mode, Segmentation By Application, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

33. Middle East Grid-Edge Phase Identification Analytics Market

  • 33.1. Middle East Grid-Edge Phase Identification Analytics Market Overview
  • Region Information, Market Information, Background Information, Government Initiatives, Regulations, Regulatory Bodies, Major Associations, Taxes Levied, Corporate Tax Structure, Investments, Major Companies
  • 33.2. Middle East Grid-Edge Phase Identification Analytics Market, Segmentation By Component, Segmentation By Deployment Mode, Segmentation By Application, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

34. Africa Grid-Edge Phase Identification Analytics Market

  • 34.1. Africa Grid-Edge Phase Identification Analytics Market Overview
  • Region Information, Market Information, Background Information, Government Initiatives, Regulations, Regulatory Bodies, Major Associations, Taxes Levied, Corporate Tax Structure, Investments, Major Companies
  • 34.2. Africa Grid-Edge Phase Identification Analytics Market, Segmentation By Component, Segmentation By Deployment Mode, Segmentation By Application, Historic and Forecast, 2020-2025, 2025-2030F, 2035F, $ Billion

35. Grid-Edge Phase Identification Analytics Market Regulatory and Investment Landscape

36. Grid-Edge Phase Identification Analytics Market Competitive Landscape And Company Profiles

  • 36.1. Grid-Edge Phase Identification Analytics Market Competitive Landscape And Market Share 2024
    • 36.1.1. Top 10 Companies (Ranked by revenue/share)
  • 36.2. Grid-Edge Phase Identification Analytics Market - Company Scoring Matrix
    • 36.2.1. Market Revenues
    • 36.2.2. Product Innovation Score
    • 36.2.3. Brand Recognition
  • 36.3. Grid-Edge Phase Identification Analytics Market Company Profiles
    • 36.3.1. Siemens AG Overview, Products and Services, Strategy and Financial Analysis
    • 36.3.2. Hitachi Energy Ltd. Overview, Products and Services, Strategy and Financial Analysis
    • 36.3.3. International Business Machines Corporation (IBM) Overview, Products and Services, Strategy and Financial Analysis
    • 36.3.4. Cisco Systems, Inc. Overview, Products and Services, Strategy and Financial Analysis
    • 36.3.5. Oracle Corporation Overview, Products and Services, Strategy and Financial Analysis

37. Grid-Edge Phase Identification Analytics Market Other Major And Innovative Companies

  • Schneider Electric SE, Honeywell International Inc., ABB Ltd., Capgemini SE, Eaton Corporation plc, Itron, Inc., Landis+Gyr Group AG, Schweitzer Engineering Laboratories, Inc. (SEL), S&C Electric Company, Aclara Technologies LLC (a Hubbell Company), Enel X S.r.l., Kamstrup A/S, C3.ai, Inc., Uplight, Inc., Trilliant Holdings Inc.

38. Global Grid-Edge Phase Identification Analytics Market Competitive Benchmarking And Dashboard

39. Upcoming Startups in the Market

40. Key Mergers And Acquisitions In The Grid-Edge Phase Identification Analytics Market

41. Grid-Edge Phase Identification Analytics Market High Potential Countries, Segments and Strategies

  • 41.1 Grid-Edge Phase Identification Analytics Market In 2030 - Countries Offering Most New Opportunities
  • 41.2 Grid-Edge Phase Identification Analytics Market In 2030 - Segments Offering Most New Opportunities
  • 41.3 Grid-Edge Phase Identification Analytics Market In 2030 - Growth Strategies
    • 41.3.1 Market Trend Based Strategies
    • 41.3.2 Competitor Strategies

42. Appendix

  • 42.1. Abbreviations
  • 42.2. Currencies
  • 42.3. Historic And Forecast Inflation Rates
  • 42.4. Research Inquiries
  • 42.5. The Business Research Company
  • 42.6. Copyright And Disclaimer