能源領域的巨量資料分析：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: 2025年03月18日 | 出版商:

Mordor Intelligence | 英文 107 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

能源領域的巨量資料分析市場預計將從 2025 年的 106.2 億美元成長到 2030 年的 179.5 億美元，預測期間（2025-2030 年）的複合年成長率為 11.07%。

巨量資料解決方案可協助石油和天然氣公司收集和處理所需的資料，以提高儲存生產效率。各種井下感測器用於採集資料（溫度、聲學、壓力等）。例如，公司可以使用巨量資料分析來建立儲存管理系統，提供有關儲存壓力、溫度、流量和聲學變化的快速、可操作的資訊。這使得公司能夠提高盈利，同時增強對營運的控制。

主要亮點

目前，整個進程由能源部門推動和支持。每個企業都需要比以往更多的能源，並且他們希望以合適的價格獲得能源，而巨量資料和分析的進步正在使這成為現實。巨量資料使公司能夠收集、儲存和分析大量資訊（Terabyte甚至Petabyte）。多年來，電力和能源產業一直在處理巨量資料，每天都要處理大量資料。
與每月提供資料的傳統電錶不同，智慧電錶可以提供精確到分鐘的更詳細讀數，從而產生重要資料並增加收集的資料量。隨著感測器、無線傳輸、網路通訊、雲端處理技術的應用日益廣泛，資料正從需求方和供給方不斷收集。
油價波動導致能源相關計劃的支出增加，對巨量資料分析產生了巨大的需求。對高品質資訊的需求日益成長，預計這將推動市場成長。
在當前情況下，數位技能和數位思維的短缺，加上能夠有效處理非結構化資料分析的熟練專業人員和勞動力的短缺，是阻礙市場成長的因素之一。
能源消耗直接受到GDP成長、工業生產和消費者支出等宏觀經濟變數的影響。能源消耗通常隨著製造業、運輸業和住宅等多個領域的經濟成長而增加。能源產業越來越需要先進的分析解決方案來最佳化與生產、分配和消費相關的流程。例如，根據世界銀行估計，2023年北美的GDP為32.32兆美元，預計2023-24年將成長1.5％，這預計將導致企業活動和能源領域對巨量資料分析的投資增加。

能源領域的巨量資料分析市場趨勢

電網營運應用領域預計將佔據相當大的市場佔有率

世界各地對能源的需求正在增加。根據國際能源總署 (IEA) 的數據，預計 2005 年至 2030 年間能源需求將增加 55%，從 114 億噸油當量增至 177 億噸，全球能源消費量預計在 2050 年將達到 886.3 兆英熱單位。隨著太陽能等可再生能源輸入電網，公用事業公司可以使用需量反應分析來決定何時在高峰時段釋放這些能源。
資料分析在現代工業系統中發揮著至關重要的作用。電網面臨傳統石化燃料枯竭的問題，迫使電力系統透過脫氫處理減少二氧化碳排放。智慧電網和超級電網是加快電氣化步伐、提高再生能源來源滲透率的有效解決方案。
電力分配系統中使用的傳統電錶僅產生少量資料可以手動收集和分析以用於計費目的。從通訊智慧電網以不同時間解析度收集的大量資料需要高級資料分析來提取收費和電網健康狀況的關鍵資訊。例如，高解析度的用戶消費資料還可用於需求預測、客戶行為分析和能源生產最佳化。
智慧電網巨量資料分析有可能改變電力產業。但要最大限度地發揮其價值，就需要適當地利用它。智慧電網分析可分為後勤部門分析（特定功能，如監督並聯型、負載預測和可靠性報告）和分散式分析（來自儀表、感測器和其他設備的資料分析）。
基於先進計量基礎設施的資料分析的預測性維護和故障檢測對於電力系統的安全變得越來越重要。隨著這些解決方案融入他們的組織中，預計早期採用者將會利用這些解決方案。 GE 的新分析技術正在使電網更有效率。該公司還推出了一系列新的預測分析產品，利用來自輸電和配電網的資料，幫助公用事業公司在更多分散式資產引入電網時實現更高的營運效率。

預計北美將佔據較大的市場佔有率

北美一直是巨量資料分析應用領域的領先創新者和先驅之一。巨量資料巨量資料分析表現出巨大的需求，為市場成長提供了有利可圖的機會。
與加拿大相比，美國在北美地區需求成長中發揮關鍵作用。石油和天然氣、精製和發電行業的需求尤其增加。大多數美國人認為太陽能和風能是環保能源來源。約65%的人認為風能比大多數其他能源來源對環境更有利。
石油和天然氣公司正在從應用預測性維護解決方案中受益。基於物聯網的預測性維護使石油和燃氣公司能夠識別即將發生的故障並增加關鍵資產的產量。這就是為什麼雪佛龍等公司採用物聯網發展來部署預測性維護解決方案來減輕管道腐蝕和損壞的原因。該解決方案使用安裝在整個管道中的感測器來測量 pH 值、CO2/H2S 水溶液含量、氣體洩漏以及管道內徑和厚度。該解決方案收集即時感測器資料並將其傳遞到雲端進行評估、分析和預測。
該地區在智慧電網技術應用方面處於領先地位。該地區能源和公用事業領域的許多公司已經全面採用或正在採用巨量資料分析。在美國市場，許多大型投資者擁有的公用事業公司正在向其客戶推出智慧電錶。根據美國能源資訊署統計，美國原計劃在2022年終安裝1.19億台智慧電錶，但到2023年終將安裝1.28億台智慧電錶。
巨量資料被廣泛用於準確預測該地區的天氣變數。使用計算智慧技術觀察不同的資料來源和模型並進行即時分析。最近，Bazefield 作為一個市場領先的可再生能源監測和分析平台，為風能、太陽能、水力發電、生質能能、電池儲存和其他可再生技術來源提供現成的支持，透過將基於黃金標準機器學習的太陽能高級分析套件 EnSight 整合到 Bazefield 作為單一平台，增強了其太陽能能力。

能源產業巨量資料分析概述

由於全球參與者和中小型企業的存在，能源領域巨量資料分析市場高度分散。市場的主要企業包括 IBM 公司、西門子股份公司、SAP SE、戴爾科技公司和埃森哲公司。市場參與者正在採取聯盟和收購等策略來增強其產品供應並獲得永續的競爭優勢。

2023 年 11 月-西門子與為關鍵基礎設施公司提供資產規劃和分析軟體的加拿大公司 Copperleaf 合作，以擴大其現有的電網軟體合作夥伴生態系統。此策略夥伴關係關係旨在最佳化輸電系統營運商（TSO）和配電系統營運商（DSO）的投資和技術電網規劃。此次夥伴關係將西門子的電網規劃、營運和維護軟體與銅纜的資產管理能力結合，帶來豐富的電力系統和電網控制專業知識。

其他福利：

Excel 格式的市場預測 (ME) 表
3 個月的分析師支持

Big data solutions aid in collecting and processing data required by oil and gas firms to improve reservoir production efficiency. Various downhole sensors are used to obtain the data (temperature, acoustic, pressure, etc.). Companies, for example, can use big data analytics to create reservoir management systems that provide fast and actionable information about changes in reservoir pressure, temperature, flow, and acoustics. This allows companies to gain greater control over their operations while enhancing profitability.

Key Highlights

Every process currently is driven and supported by the energy sector. Every entity requires more energy than ever before and wants it at a reasonable price, and the advancement of big data and analytics has made it a real possibility. Big data enables enterprises to collect, store, and analyze massive amounts of information (terabytes and petabytes). For years, the power and energy industries have worked with big data and routinely processed large amounts of data.
Unlike conventional electricity meters, which provide data every month, smart meters can give readings on a minute basis that are on a more granular level, causing considerable data generation and resulting in a volumetric increase in data gathered. Data is being collected from both the demand and supply side, owing to the increasing application of sensors, wireless transmission, network communication, and cloud computing technologies.
The volatility in the oil prices leads to high expenditure on energy-related projects, which creates a major demand for big data analytics. The need for quality information is increasing, which is expected to boost the market's growth.
In the current scenario, the lack of digital skills and digital mindsets aggravated by the lack of skilled professionals and workforce to handle the unstructured data effectively for analysis is one of the factors hindering the market growth.
Energy consumption is directly impacted by macroeconomic variables such as GDP growth rates, industrial production, and consumer expenditure. Energy consumption generally rises with economic growth in several sectors, including manufacturing, transportation, and residential. To optimize the processes involved in production distribution and consumption, the energy sector needs increasingly sophisticated analytic solutions. For instance, according to a World Bank estimate, the North American GDP, which was USD 32.32 trillion in 2023, is predicted to increase by 1.5% in 2023-24, suggesting that corporate activity and possible big data analytics in energy sector investments are projected to flourish.

Big Data Analytics in Energy Sector Market Trends

Grid Operations Application Segment is Expected to Hold Significant Market Share

The demand for energy across the world is rising. According to the International Energy Agency, between 2005 and 2030, energy needs are estimated to expand by 55%, with the demand rising from 11.4 billion metric tons of oil equivalent to 17.7 billion, and the forecasted global energy consumption will be 886.3 quadrillion British thermal units by 2050. With renewable energy sources, such as solar power, which contributes electricity to the power grid, utilities can use demand response analytics to determine the timings to release these power sources during peak demand.
Data analytics possess a critical role in modern industrial systems. In the power grid, traditional fossil fuels face the problem of depletion, and de-carbonization demands the power system to reduce carbon emissions. Smart grid and super grid are effective solutions to accelerate the pace of electrification with high penetration of renewable energy sources.
Traditional electricity meters used in distribution systems only produce a small amount of data that can be manually collected and analyzed for billing purposes. The huge volume of data collected from two-way communication smart grids at various time resolutions requires advanced data analytics to extract important information for billing information and the status of the electricity network. For instance, the high-resolution user consumption data can also be used for demand forecasting, customer behavior analysis, and energy generation optimization.
Smart grid big data analytics can potentially transform the utility industry. However, it needs to be appropriately used to maximize its value. Smart grid analytics divided itself into back-office analytics (certain functions, like overseeing grid connectivity, load forecasting, and reliability reporting) and distributed analytics (analyzing data from meters, sensors, and other devices).
Predictive maintenance and fault detection based on data analytics with advanced metering infrastructure are more crucial to the security of the power system. They are expected to be the solutions that are expected to be now utilized by the early adopters as the solutions have been integrated into their organization. GE's New Analytics Technologies is boosting grid efficiency. The company has also rolled out a new portfolio of predictive analytics that could allow utilities to use data from transmission and distribution networks to achieve better operational efficiency as more distributed assets are introduced to the grid.

North America is Expected to Hold Significant Market Share

North America is one of the leading innovators and pioneers in the adoption of big data analytics. The region offers lucrative opportunities for market growth, exhibiting a massive demand for big data analytics in the energy sector owing to the strong foothold of big data analytics vendors.
The United States plays a key role in proliferating the demand from the North American region compared to Canada. The country has increased demand, especially from oil and gas, refining, and power generation segments. The majority of Americans consider solar and wind power as good sources of energy for the environment. Around 65% of the population suggests that the environmental effect of wind turbine farms is better than that of most other sources.
The oil and gas companies benefit from applying predictive maintenance solutions. IoT-based predictive maintenance enables oil and gas companies to identify possible failures and increase the production of highly critical assets. Thus, companies such as Chevron employed IoT development to roll out a predictive maintenance solution that helps mitigate corrosion and pipeline damage. The solution uses sensors installed across the pipeline to measure the pH, aqueous CO2/H2S content, and gaseous leakages along with the pipeline's internal diameter and thickness. The solution collects real-time sensor data and passes it to the cloud for evaluation, analysis, and prediction.
The region has been at the forefront of adopting smart grid technology. A large number of companies operating in the energy utility sector in the region have either fully deployed big data analytics or are in the process of implementation. Many large investor-owned utilities in the US market are still in the process of rolling out smart meters for their customers. According to the US Energy Information Administration, 119 million smart meters were to be installed in the US by the end of 2022, whereas 128 million smart meter deployments were completed by the end of 2023.
Big data is extensively being used for the accurate prediction of meteorological variables in the region. Disparate data sources and models are observed using computational intelligence techniques for real-time analysis. Recently, Bazefield, the market-leading renewable monitoring and analytics platform with off-the-shelf support for wind power, solar, hydro, biomass, battery storage, and other renewable technology sources, enhanced its solar capabilities by embedding the gold standard EnSight, machine learning-based solar advanced analytics package, into Bazefield as one single platform.

Big Data Analytics in Energy Sector Industry Overview

Big data analytics in the energy sector market is highly fragmented due to the presence of global players and small- and medium-sized enterprises. Some of the major players in the market are IBM Corporation, Siemens AG, SAP SE, Dell Technologies Inc., and Accenture PLC. Players in the market are adopting strategies such as partnerships and acquisitions to enhance their product offerings and gain sustainable competitive advantage.

November 2023 - Siemens partnered with Copperleaf, a Canadian-based provider of asset planning software and analytics software for critical infrastructure companies, to grow its existing ecosystem of grid software partners. The strategic partnership aims to optimize investment and technical grid planning for transmission system operators (TSOs) and distribution system operators (DSOs). The partnership will bring extensive power systems and grid control domain expertise, combining Siemens grid planning, operations, and maintenance software and Copperleaf's assets management capabilities.

Additional Benefits:

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

1 INTRODUCTION

1.1 Study Assumptions and Market Definition
1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET INSIGHTS

4.1 Market Overview
4.2 Industry Attractiveness - Porter's Five Forces Analysis
- 4.2.1 Bargaining Power of Suppliers
- 4.2.2 Bargaining Power of Buyers/ Consumers
- 4.2.3 Threat of New Entrants
- 4.2.4 Threat of Substitutes
- 4.2.5 Intensity of Competitive Rivalry
4.3 An Assessment of the Impact of Macroeconomics Trends

5 MARKET DYNAMICS

5.1 Market Drivers
- 5.1.1 Enormous Influx of Data
- 5.1.2 Volatility in the Oil Prices
5.2 Market Restraints
- 5.2.1 Lack of Skilled Labor

6 MARKET SEGMENTATION

6.1 By Application
- 6.1.1 Grid Operations
- 6.1.2 Smart Metering
- 6.1.3 Asset and Workforce Management
6.2 By Geography
- 6.2.1 North America
- 6.2.2 Europe
- 6.2.3 Asia
- 6.2.4 Australia and New Zealand
- 6.2.5 Latin America
- 6.2.6 Middle East and Africa

7 COMPETITIVE LANDSCAPE

7.1 Company Profiles
- 7.1.1 IBM Corporation
- 7.1.2 Siemens AG
- 7.1.3 SAP SE
- 7.1.4 Dell Technologies Inc.
- 7.1.5 Accenture PLC
- 7.1.6 Infosys Limited
- 7.1.7 Intel Corporation
- 7.1.8 Microsoft Corporation
- 7.1.9 Palantir Technologies Inc.
- 7.1.10 Enel X Italia Srl (Enel SpA)