查看購物車
  • English
  • Japanese
  • Korean
封面
市場調查報告書
商品編碼
2097363

能源產業巨量資料分析：市場佔有率分析、產業趨勢與統計、成長預測（2025-2030 年）

Big Data Analytics In Energy Sector - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2025 - 2030)
出版日期: | 出版商: Mordor Intelligence | 英文 123 Pages | 商品交期: 2-3個工作天內

價格

※ 本網頁內容可能與最新版本有所差異。詳細情況請與我們聯繫。

簡介目錄

根據 Mordor Intelligence 預測，能源產業的巨量資料分析市場規模預計將在 2025 年達到 106.2 億美元，到 2030 年達到 179.5 億美元，複合年成長率為 11.07%。

能源領域的巨量資料分析-市場-IMG1

本報告按應用領域（例如，電網運作、智慧電錶、資產和人員管理、預測性維護和APM）、元件（軟體、服務）、部署方式（本地部署、雲端部署、混合部署）、最終用戶（例如，電力公司、石油探勘和生產）以及地區進行細分。市場預測以美元（USD）計價。

全球能源產業巨量資料分析市場趨勢及洞察。

智慧電網和物聯網數據的指數級成長

目前，電力公司每天處理數TB的Terabyte遙測數據，與傳統SCADA系統每天處理的MB級數據相比，成長迅速。智慧電錶每15分鐘採集一次的詳細數據，能夠近乎即時地揭示需求異常，使營運商能夠最佳化負載分配。 OracleOracle於2025年3月對其高階配電管理系統（ADAMS）進行的增強，展示了7天逐小時預測如何幫助奧斯汀能源公司（Austin Energy）和塔塔電力公司（Tata Power）等企業更有效地管理分散式資源。安裝在輸電線路上的物聯網感測器正在建構用於預測性維護的回饋迴路，最近的部署已將計畫外停電減少了高達25%。諸如北美電力可靠性委員會（NERC）的持續改進計劃（CIP）等強制性框架，要求北美電力公司持續監控電網數據，從而推動了相關技術的普及應用。

最佳化資產績效的壓力

隨著老舊基礎設施面臨波動性較大的可再生主導需求，營運商被迫從傳統資產中榨取更多價值。通用電氣Vernova的一項案例研究表明，預測性維護使企業能夠從基於時間的調度轉向基於狀態的調度，並在實施的第一年就實現了兩位數的節能效果。關鍵發電資產意外停機造成的損失可能超過每小時100萬美元，因此對分析技術的投資可以迅速獲得回報。Schneider Electric和嘉能可的聯合專案展示了數位孿生技術如何最大限度地提高採礦作業的處理能力，同時降低二氧化碳排放強度。埃克森美孚等大型油氣燃氣公司也將類似的模型應用於鑽井、洩漏檢測和地震成像等領域，從而將最佳化原則擴展到整個能源價值鏈。

專業資料科學人才短缺

能源分析需要電力系統工程和資料科學兩方面的技能，但目前就業市場上具備這種綜合能力的人才十分稀缺。大學很少開設潮流建模或動態最佳化等課程，並結合機器學習理論進行教學，因此畢業生需要接受大量的在職培訓。這些跨領域專家的諮詢費用居高不下，對專案進度和預算造成了巨大壓力。在亞太等快速發展的地區，這種人才短缺問題尤其嚴重，因為這些地區的基礎建設速度遠超人才培養速度。儘管電力公司正在與學術機構合作並推出內部培訓班，但短期內的人才供應仍然無法滿足需求。

基於細分市場的分析

2024年，隨著北美和歐洲監管機構強制部署高階計量系統，智慧電錶在能源產業的巨量資料分析市場佔了42.5%的佔有率。電力公司利用這一領域以15分鐘為間隔收集數據，從而實現濫用檢測、停電管理和分時電價。隨後，電網運作和需量反應模組可以重複使用資料湖，提高基礎設施投資的盈利。由於需要減少老舊火力發電廠和可再生能源設施的停機時間，預測性維護和資產性能管理預計到2030年將以28.7%的複合年成長率成長。 Oracle 2025年的Oracle更新整合了分散式資源模型，展示了該平台如何在單一介面下整合多個應用程式。隨著雲端容量的擴展，以前需要企業級預算才能運行的AI模型現在可以由小規模合作社運行，從而擴大了應用程式供應商的潛在基本客群。

能源產業的巨量資料分析市場正受惠於公用事業公司對多租戶訂閱定價模式的需求，這種模式可根據計量表的數量調整成本。能源交易領域目前正利用電網資料饋送來驅動小時市場的演算法競標，從而將分析範圍擴展到營運任務之外。客戶參與平台正在利用智慧電錶的洞察數據，提案提高能源效率的維修，並透過輔助服務創造收入。在整個預測期內，監管機構計劃進一步細化價格訊號，提高數據密度，並將分析功能進一步嵌入到基於測量數據的工作流程中。隨著點對點交易P2P在歐洲和澳洲的推進，應用供應商可能會將數位帳本功能整合到其現有的分析套件中，以追蹤交易歷史記錄。

2024年，軟體在能源產業的巨量資料分析市場仍佔61%的佔有率。這凸顯了公用事業公司歷來偏好的以平台為中心的採購模式。整合套件比單一功能解決方案更具吸引力，因為它們功能廣泛，涵蓋資料收集和清洗、模型編配和視覺化等各個方面。然而，「服務」類別預計到2030年將以27.5%的複合年成長率成長，這表明合約模式正向基於結果的方向轉變。Schneider Electric的私募股權和金融服務部門已證明，諮詢和工具集的結合能夠為資產管理公司帶來可衡量的脫碳成果。公用事業公司正在增加資料科學和模型維護的外包，從而可以將資源重新分配到電網現代化策略中。託管分析合約將供應商的報酬與損耗率降低等績效指標掛鉤，鼓勵供應商自行承擔技術風險。能夠將領域專業知識與人工智慧工具鏈結合的供應商將超越純粹專注於軟體的競爭對手。隨著監管報告日益複雜，可審計性要求對那些能夠維護端到端資料處理歷程的服務供應商更為有利。因此，軟體模組化與服務可自訂性之間的相互作用將在預測期內塑造競爭優勢。

區域分析

預計到2024年，北美將佔據能源產業巨量資料分析市場35%的佔有率，這主要得益於北美電力可靠性委員會（NERC）的資本改善計畫（CIP）法規強制要求對電網進行精細化監測，以及完善的公共產業採購流程。先進計量系統的早期應用、競爭激烈的零售市場以及成熟的供應商生態系統，都為分析預算的永續分配提供了支持。加拿大的跨國電力交易進一步增加了對預測性擁塞管理工具的需求。聯邦政府對可再生能源併網的獎勵，以及各省的脫碳目標，將推動數據量持續成長，鞏固分析作為公共產業核心能力的地位。

亞太地區是成長最快的地區，預計到2030年將以27.4%的複合年成長率成長，這主要得益於中國和印度數十億美元的電網現代化和可再生能源擴張計畫。中國國家電網公司部署的人工智慧故障定位感測器正在減少停電時間，並展現出Terabyte驅動營運所能帶來的規模經濟效益。印度的智慧城市和太陽能園區計畫正在向新興雲端平台提供數TB的遙測數據，並促進供應商之間的合作。日本和韓國正在推動強制性的能源效率提升，而這些提升依賴於物聯網感測器的整合；與此同時，澳洲的市場改革正在拓展演算法交易的應用場景。

在歐洲，隨著電力公司履行可再生能源組合和碳減排義務（這需要高度精確的預測和最佳化），電力市場持續穩定成長。德國和荷蘭的P2P（P2P）能源交易計畫正在促進結算和來源追蹤方面的分析。中東和非洲蘊藏著新的潛力，石油出口國正在實現發電結構多元化並部署智慧電網試點計畫。儘管資金和人力資源的限制制約了其普及，但有針對性的政府計劃和國際夥伴關係有望加速未來的成長。

其他好處：

  • Excel格式的市場預測（ME）表
  • 3個月的分析師支持

目錄

第1章：引言

  • 市場分析與定義的前提條件
  • 分析範圍

第2章 分析方法

第3章執行摘要

第4章 市場狀況

  • 市場概覽
  • 市場促進因素
    • 智慧電網和物聯網數據的指數級成長
    • 最佳化資產績效的壓力
    • 可再生能源整合的複雜性
    • 疫情後的數位轉型投資
    • P2P能源交易的分析需求
    • 降低雲端和邊緣分析成本
  • 市場限制因素
    • 特定領域資料科學人才短缺
    • OT/IT網路安全和隱私風險
    • 舊有系統和資料孤島
    • 新興地區對分析設備的前期投資較高
  • 監理情勢
  • 技術展望
  • 波特五力分析

第5章：預測市場規模與成長率

  • 透過使用
    • 輸配電網路運行
    • 智慧電錶
    • 資產和人員管理
    • 預測性維護和輔助效能管理 (APM)
    • 需量反應（DR）和負載預測
    • 能源交易、風險管理
  • 按組件
    • 軟體
    • 服務
  • 透過部署方法
    • 現場
    • 雲
    • 混合
  • 最終用戶
    • 電力公司
    • 石油探勘與生產
    • 中游/煉油廠
    • 可再生能源開發公司
    • 能源服務公司（ESCO）
    • 其他最終用戶
  • 按地區
    • 北美洲
      • 美國
      • 加拿大
      • 墨西哥
    • 南美洲
      • 巴西
      • 阿根廷
      • 其他南美國家
    • 歐洲
      • 德國
      • 英國
      • 法國
      • 義大利
      • 西班牙
      • 俄羅斯
      • 其他歐洲國家
    • 亞太地區
      • 中國
      • 日本
      • 印度
      • 韓國
      • 澳洲
      • 其他亞太國家
    • 中東和非洲
      • 中東
        • 沙烏地阿拉伯
        • 阿拉伯聯合大公國
        • 土耳其
        • 其他中東國家
      • 非洲
        • 南非
        • 奈及利亞
        • 其他非洲國家

第6章 競爭情勢

  • 市場集中度
  • 策略趨勢
  • 市佔率分析
  • 公司簡介
    • International Business Machines Corporation
    • SAP SE
    • Microsoft Corporation
    • Siemens Aktiengesellschaft
    • Accenture plc
    • Schneider Electric SE
    • Oracle Corporation
    • General Electric Company
    • SAS Institute Inc.
    • Honeywell International Inc.
    • Schlumberger Limited
    • Halliburton Company
    • Hitachi, Ltd.
    • C3.ai, Inc.
    • Teradata Corporation
    • Amazon.com, Inc.
    • Alphabet Inc.
    • Dell Technologies Inc.
    • Palantir Technologies Inc.
    • Enel SpA
    • Itron, Inc.

第7章 市場機會與未來展望

簡介目錄
Product Code: 97120

According to Mordor Intelligence, the big data analytics market in energy sector stood at USD 10.62 billion in 2025 and is forecast to reach USD 17.95 billion by 2030, advancing at an 11.07% CAGR.

Big Data Analytics In Energy Sector - Market - IMG1

This report is Segmented by Application (Grid Operations, Smart Metering, Asset and Workforce Management, Predictive Maintenance and APM, and More), Component (Software, and Services), Deployment Model (On-Premise, Cloud, and Hybrid), End-User (Power Utilities, Oil Exploration and Production, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Insights and Trends of Big Data Analytics Market In Energy Sector

Exponential Smart-Grid and IoT Data Growth

Utilities now process terabytes of grid telemetry every day, a drastic leap from the megabytes handled by legacy SCADA systems. Granular 15-minute interval data from smart meters exposes demand anomalies in near real time, letting operators fine-tune load allocation. Oracle's March 2025 enhancements to its Advanced Distribution Management System illustrate how 7-day hourly forecasting supports companies such as Austin Energy and Tata Power in orchestrating distributed resources more effectively. IoT sensors across transmission lines yield feedback loops for predictive maintenance that have cut unplanned outages by up to 25% in recent deployments. Mandatory frameworks such as NERC CIP reinforce adoption by requiring continuous grid-data surveillance across North American utilities.

Asset-Performance Optimization Pressure

Aging infrastructure meets volatile, renewable-driven demand, forcing operators to squeeze more output from legacy assets. Predictive maintenance lets firms shift from time-based to condition-based schedules, generating double-digit energy savings in year one, according to GE Vernova case studies. Unplanned downtime can exceed USD 1 million per hour for critical generation assets, so analytics rapidly pay for themselves. Schneider Electric's work with Glencore shows how digital twins reduce CO2 intensity in mining while maximizing throughput. Oil and gas majors such as ExxonMobil apply similar models to drilling, leak detection, and seismic imaging, extending optimization principles across the energy value chain.

Domain-Specific Data-Science Talent Gap

Energy analytics requires skill sets that straddle power-system engineering and data science, a combination scarce in today's labor market. Universities seldom teach load-flow modeling or thermodynamic optimization alongside machine-learning theory, so graduates need lengthy on-the-job training. Consulting rates for such hybrid specialists remain elevated, stretching project timelines and budgets. The shortage is acute in fast-growing regions such as Asia-Pacific, where infrastructure build-outs outpace workforce development. Utilities are teaming with academic programs and launching internal boot camps, yet near-term supply still lags demand.

Other drivers and restraints analyzed in the detailed report include:

  1. Renewable-Integration Complexity
  2. Post-Pandemic Digital-Transformation Spend
  3. OT/IT Cyber-Security and Privacy Risks

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Smart Metering claimed 42.5% share of the Big Data Analytics Market in Energy Sector in 2024 as regulators in North America and Europe mandated advanced metering roll-outs. Utilities rely on the segment to collect 15-minute interval data, enabling theft detection, outage management, and time-of-use pricing. Grid operations and demand-response modules then reuse the data lake, amplifying return on infrastructure spending. Predictive Maintenance and Asset Performance Management is set to log a 28.7% CAGR through 2030, propelled by the need to cut downtime in aging thermal plants and renewables alike. Oracle's 2025 ADMS update integrates distributed-resource models, showing how platforms bundle multiple applications under one interface. As cloud capacity expands, even smaller cooperatives can run AI models that once required enterprise budgets, widening the addressable base for application vendors.

The Big Data Analytics Market in Energy Sector benefits from utility appetite for multi-tenant subscription pricing that aligns cost with meter count. Energy-trading desks now tap grid-data feeds to drive algorithmic bids in hourly markets, extending analytical reach beyond operations. Customer-engagement platforms leverage smart-meter insights to recommend energy-efficiency retrofits, spawning ancillary service revenue. Over the forecast horizon, regulators plan sharper price-signal granularity, which will raise data density and further entrench analytics across metering-led workflows. As peer-to-peer trading pilots gain traction in Europe and Australia, application vendors will embed digital-ledger functions into existing analytics suites to track transaction provenance.

Software retained a 61% share of the Big Data Analytics Market in the Energy Sector in 2024, a testament to the platform-centric procurement model that utilities historically prefer. The feature breadth from ingestion and cleansing to model orchestration and visualization makes integrated suites attractive relative to point solutions. Yet the Services category will accelerate at a 27.5% CAGR through 2030, signaling a pivot toward outcome-based engagements. Schneider Electric's Private Equity and Financial Services practice illustrates how consultancy plus toolset delivers measurable decarbonization returns for asset managers. Utilities increasingly outsource data-science and model-maintenance tasks, freeing resources for grid-modernization strategy. Managed analytics contracts tie vendor compensation to performance metrics such as loss-factor reduction, pushing suppliers to absorb technology risk. Vendors able to blend domain expertise with AI toolchains will outpace pure-software rivals. As regulatory reporting grows complex, auditability requirements favor service providers who maintain end-to-end data lineage. The interplay of software modularity and service customization thus shapes competitive moats over the outlook period.

Complete Report Scope:

  • By Application
    • Grid Operations
    • Smart Metering
    • Asset and Workforce Management
    • Predictive Maintenance and APM
    • Demand Response and Load Forecasting
    • Energy Trading and Risk Management
  • By Component
    • Software
    • Services
  • By Deployment Model
    • On-Premise
    • Cloud
    • Hybrid
  • By End-User
    • Power Utilities
    • Oil Exploration and Production
    • Midstream and Refining Operators
    • Renewable Energy Developers
    • Energy Service Companies (ESCOs)
    • Other End-Users
  • By Geography
    • North America
      • United States
      • Canada
      • Mexico
    • South America
      • Brazil
      • Argentina
      • Rest of South America
    • Europe
      • Germany
      • United Kingdom
      • France
      • Italy
      • Spain
      • Russia
      • Rest of Europe
    • Asia Pacific
      • China
      • Japan
      • India
      • South Korea
      • Australia
      • Rest of Asia Pacific
    • Middle East and Africa
      • Middle East
        • Saudi Arabia
        • United Arab Emirates
        • Turkey
        • Rest of Middle East
      • Africa
        • South Africa
        • Nigeria
        • Rest of Africa

Geography Analysis

North America captured a 35% share of the Big Data Analytics Market in Energy Sector in 2024 on the back of NERC CIP rules that require granular grid-monitoring and established utility procurement processes. Early adoption of advanced metering, competitive retail markets, and a mature vendor ecosystem support sustained analytics budgets. Canadian cross-border trade further elevates the need for predictive congestion management tools. Federal incentives for renewable integration plus state-level decarbonization targets will keep data volumes rising, cementing analytics as a core utility competency.

Asia-Pacific is the fastest-growing region with a 27.4% CAGR through 2030, driven by multibillion-dollar grid-modernization and renewable-expansion programs in China and India. China's state-grid deployment of AI-enabled fault-location sensors reduces outage duration and showcases the scale benefit of data-driven operations. India's smart-city and solar-park initiatives feed terabytes of telemetry into nascent cloud platforms, catalyzing vendor partnerships. Japan and South Korea pursue energy-efficiency mandates that depend on IoT sensor integration, while Australia's market reforms elevate algorithmic trading use cases.

Europe maintains steady growth as utilities comply with renewable-portfolio and carbon-reduction mandates that require high-resolution forecasting and optimization. Peer-to-peer energy-trading pilots in Germany and the Netherlands spur analytics for settlement and provenance tracking. The Middle East and Africa offer emerging potential where oil-exporting economies diversify generation mixes and introduce smart-grid pilots. Though capital and talent constraints restrain uptake, targeted government programs and international partnerships hint at future acceleration.

  1. International Business Machines Corporation
  2. SAP SE
  3. Microsoft Corporation
  4. Siemens Aktiengesellschaft
  5. Accenture plc
  6. Schneider Electric SE
  7. Oracle Corporation
  8. General Electric Company
  9. SAS Institute Inc.
  10. Honeywell International Inc.
  11. Schlumberger Limited
  12. Halliburton Company
  13. Hitachi, Ltd.
  14. C3.ai, Inc.
  15. Teradata Corporation
  16. Amazon.com, Inc.
  17. Alphabet Inc.
  18. Dell Technologies Inc.
  19. Palantir Technologies Inc.
  20. Enel SpA
  21. Itron, Inc.

Additional Benefits:

  • The market estimate (ME) sheet in Excel format
  • 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

  • 1.1 Study Assumptions and Market Definition
  • 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET LANDSCAPE

  • 4.1 Market Overview
  • 4.2 Market Drivers
    • 4.2.1 Exponential Smart-Grid and IoT Data Growth
    • 4.2.2 Asset-Performance Optimization Pressure
    • 4.2.3 Renewable-Integration Complexity
    • 4.2.4 Post-Pandemic Digital-Transformation Spend
    • 4.2.5 Peer-To-Peer Energy Trading Analytics Need
    • 4.2.6 Falling Cloud and Edge Analytics Costs
  • 4.3 Market Restraints
    • 4.3.1 Domain-Specific Data-Science Talent Gap
    • 4.3.2 OT/IT Cyber-Security and Privacy Risks
    • 4.3.3 Legacy Systems and Data Silos
    • 4.3.4 High Upfront Analytics CAPEX In Emerging Regions
  • 4.4 Regulatory Landscape
  • 4.5 Technological Outlook
  • 4.6 Porter's Five Forces Analysis
    • 4.6.1 Bargaining Power of Suppliers
    • 4.6.2 Bargaining Power of Buyers
    • 4.6.3 Threat of New Entrants
    • 4.6.4 Threat of Substitutes
    • 4.6.5 Intensity of Competitive Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

  • 5.1 By Application
    • 5.1.1 Grid Operations
    • 5.1.2 Smart Metering
    • 5.1.3 Asset and Workforce Management
    • 5.1.4 Predictive Maintenance and APM
    • 5.1.5 Demand Response and Load Forecasting
    • 5.1.6 Energy Trading and Risk Management
  • 5.2 By Component
    • 5.2.1 Software
    • 5.2.2 Services
  • 5.3 By Deployment Model
    • 5.3.1 On-Premise
    • 5.3.2 Cloud
    • 5.3.3 Hybrid
  • 5.4 By End-User
    • 5.4.1 Power Utilities
    • 5.4.2 Oil Exploration and Production
    • 5.4.3 Midstream and Refining Operators
    • 5.4.4 Renewable Energy Developers
    • 5.4.5 Energy Service Companies (ESCOs)
    • 5.4.6 Other End-Users
  • 5.5 By Geography
    • 5.5.1 North America
      • 5.5.1.1 United States
      • 5.5.1.2 Canada
      • 5.5.1.3 Mexico
    • 5.5.2 South America
      • 5.5.2.1 Brazil
      • 5.5.2.2 Argentina
      • 5.5.2.3 Rest of South America
    • 5.5.3 Europe
      • 5.5.3.1 Germany
      • 5.5.3.2 United Kingdom
      • 5.5.3.3 France
      • 5.5.3.4 Italy
      • 5.5.3.5 Spain
      • 5.5.3.6 Russia
      • 5.5.3.7 Rest of Europe
    • 5.5.4 Asia Pacific
      • 5.5.4.1 China
      • 5.5.4.2 Japan
      • 5.5.4.3 India
      • 5.5.4.4 South Korea
      • 5.5.4.5 Australia
      • 5.5.4.6 Rest of Asia Pacific
    • 5.5.5 Middle East and Africa
      • 5.5.5.1 Middle East
        • 5.5.5.1.1 Saudi Arabia
        • 5.5.5.1.2 United Arab Emirates
        • 5.5.5.1.3 Turkey
        • 5.5.5.1.4 Rest of Middle East
      • 5.5.5.2 Africa
        • 5.5.5.2.1 South Africa
        • 5.5.5.2.2 Nigeria
        • 5.5.5.2.3 Rest of Africa

6 COMPETITIVE LANDSCAPE

  • 6.1 Market Concentration
  • 6.2 Strategic Moves
  • 6.3 Market Share Analysis
  • 6.4 Company Profiles (includes Global level Overview, Market level overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share for key companies, Products and Services, and Recent Developments)
    • 6.4.1 International Business Machines Corporation
    • 6.4.2 SAP SE
    • 6.4.3 Microsoft Corporation
    • 6.4.4 Siemens Aktiengesellschaft
    • 6.4.5 Accenture plc
    • 6.4.6 Schneider Electric SE
    • 6.4.7 Oracle Corporation
    • 6.4.8 General Electric Company
    • 6.4.9 SAS Institute Inc.
    • 6.4.10 Honeywell International Inc.
    • 6.4.11 Schlumberger Limited
    • 6.4.12 Halliburton Company
    • 6.4.13 Hitachi, Ltd.
    • 6.4.14 C3.ai, Inc.
    • 6.4.15 Teradata Corporation
    • 6.4.16 Amazon.com, Inc.
    • 6.4.17 Alphabet Inc.
    • 6.4.18 Dell Technologies Inc.
    • 6.4.19 Palantir Technologies Inc.
    • 6.4.20 Enel SpA
    • 6.4.21 Itron, Inc.

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

  • 7.1 White-space and Unmet-Need Assessment