能源產業巨量資料分析：市場佔有率分析、產業趨勢/統計、成長預測（2024-2029）

Big Data Analytics In Energy Sector - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2024 - 2029)

出版日期: 2024年07月15日 | 出版商:

Mordor Intelligence | 英文 107 Pages | 商品交期: 2-3個工作天內

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簡介目錄

能源產業巨量資料分析市場預計將從 2024 年的 95.6 億美元成長到 2029 年的 161.6 億美元，預測期間（2024-2029 年）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提高儲存生產效率所需的資料。各種地下感測器用於獲取資料（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且可操作的資訊。這使得公司能夠增強營運控制，同時提高盈利。

主要亮點

目前，所有流程均由能源部門驅動和支援。現在，企業比以往任何時候都更需要更多的能源，並希望以合理的價格獲得能源，而巨量資料和分析的進步正在使這一目標成為現實。巨量資料允許公司收集、儲存和分析大量資訊（Terabyte和Petabyte）。多年來，電力和能源產業一直在處理巨量資料，每天處理大量資料。
與按月提供資料的傳統電錶不同，智慧電錶可以按分鐘提供更詳細的讀數，從而產生大量資料並增加收集的資料量。由於感測器、無線通訊、網路通訊和雲端運算技術的應用不斷增加，需求側和供應側的資料正在被收集。
油價波動導致能源相關計劃支出高昂，對巨量資料分析產生了巨大需求。對高品質資訊的需求不斷增加，預計這將推動市場成長。
在當前情況下，缺乏數位技能和數位思維，以及缺乏有效處理非結構化資料進行分析的熟練專業人員和勞動力，是阻礙市場成長的因素之一。
能源消耗直接受到GDP成長率、工業生產、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、交通運輸和住宅等多個部門的經濟成長而增加。為了最佳化生產、分配和消費的流程，能源產業需要日益複雜的分析解決方案。例如，根據世界銀行的估計，2023年北美GDP為32.32兆美元，預計2023年至2024年將成長1.5%，企業活動和能源領域的巨量資料分析的增量將會增加。。

能源產業巨量資料分析市場趨勢

電網營運應用領域預計將佔據主要市場佔有率

世界各地的能源需求正在增加。根據國際能源總署（IEA）預測，2005年至2030年間，能源需求將成長55%，從114億噸石油當量增加到177億噸石油當量，到2050年，全球能源消費量將達到886.3兆噸預計為英國熱量單位。對於為電網供電的太陽能等可再生能源，公用事業公司可以使用需量反應分析來確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重要作用。電網正面臨傳統石化燃料的枯竭，要求電力系統透過脫烴來減少碳排放。智慧電網和超級電網是透過再生能源來源的高滲透率加快電氣化步伐的有效解決方案。
配電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這些數據以用於申請目的。從雙向通訊智慧電網以各種時間解析度收集的大量資料需要先進的資料分析來提取收費資訊和電力網路狀態的關鍵資訊。例如，高解析度用戶消費資料還可用於需求預測、客戶行為分析和能源產生最佳化。
智慧電網巨量資料分析有潛力改變電力產業。但要發揮它的最大價值，就必須正確使用它。智慧電網分析分為後勤部門分析（特定功能，例如並聯型監督、負載預測和可靠性報告）和分散式分析（對來自儀表、感測器和其他設備的資料進行分析）。
基於資料分析和先進測量基礎設施的預測性維護和故障檢測對於電力系統安全更為重要。這些解決方案預計會被早期採用者利用，因為它們已整合到他們的組織中。 GE 的新分析技術正在提高電網的效率。隨著更多分散式資產被引入電網，該公司還有潛力利用來自輸配電網的資料來幫助公共事業實現更高的營運效率。

預計北美將佔據較大市場佔有率

北美是採用巨量資料分析的主要創新者和先驅者之一。由於巨量資料分析供應商的強大立足點，該地區對能源產業巨量資料巨量資料分析有著巨大的需求，為市場成長提供了利潤豐厚的機會。
與加拿大相比，美國在北美地區需求成長方面發揮著重要作用。石油和天然氣、精製和發電行業的需求尤其成長。大多數美國人認為太陽能和風能是環保能源來源。約65%的人認為風力發電的環境效益優於大多數其他能源來源。
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潛在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開發來部署預測性維護解決方案以減少腐蝕和管道損壞的原因。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 含水量、氣體洩漏以及管道的內徑和厚度。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。
該地區處於智慧電網技術實施的前沿。該地區能源和公共事業領域的許多公司已經完全採用或正在實施巨量資料分析。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。據美國能源資訊署稱，預計到年終美國將安裝1.19億個智慧電錶，但到年終已安裝1.28億個智慧電錶。
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進行即時分析。最近，市場領先的可再生能源監測和分析平台 Bazefield 推出了基於機器學習的 EnSight，為風能、太陽能、水力、生質能、電池儲存和其他可再生技術提供現成的支援。已納入Bazefield，以作為單一平台增強太陽能發電能力。

能源產業巨量資料分析產業概述

由於全球參與者和中小企業的存在，能源領域的巨量資料分析市場高度分散。該市場的主要企業包括 IBM 公司、西門子公司、SAP SE、戴爾技術公司和埃森哲公司。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 - 西門子與加拿大的 Copperleaf（一家為關鍵基礎設施公司提供資產規劃和分析軟體的供應商）合作，擴大其現有的電網軟體合作夥伴生態系統。這項策略性合作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。此次合作將西門子的電網規劃、營運和維護軟體與 Copperleaf 的資產管理能力結合，帶來了電力系統和電網控制方面的廣泛專業知識。

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)