查看购物车
  • Traditional Chinese
  • English
  • Japanese
  • Korean
封面
市场调查报告书
商品编码
1524124

能源产业巨量资料分析：市场占有率分析、产业趋势/统计、成长预测（2024-2029）

Big Data Analytics In Energy Sector - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2024 - 2029)
出版日期: | 出版商: Mordor Intelligence | 英文 107 Pages | 商品交期: 2-3个工作天内

价格

※ 本网页内容可能与最新版本有所差异。详细情况请与我们联繫。

简介目录

能源产业巨量资料分析市场预计将从 2024 年的 95.6 亿美元成长到 2029 年的 161.6 亿美元，预测期间（2024-2029 年）复合年增长率为 11.07%。

能源产业市场大数据分析

巨量资料解决方案可协助石油和天然气公司收集和处理提高储存生产效率所需的资料。各种地下感测器用于获取资料（温度、声学、压力等）。例如，公司可以使用巨量资料分析来建立储存管理系统，提供有关储存压力、温度、流量和声学变化的快速且可操作的资讯。这使得公司能够增强营运控制，同时提高盈利。

主要亮点

  • 目前，所有流程均由能源部门驱动和支援。现在，企业比以往任何时候都更需要更多的能源，并希望以合理的价格获得能源，而巨量资料和分析的进步正在使这一目标成为现实。巨量资料允许公司收集、储存和分析大量资讯（Terabyte和Petabyte）。多年来，电力和能源产业一直在处理巨量资料，每天处理大量资料。
  • 与按月提供资料的传统电錶不同，智慧电錶可以按分钟提供更详细的读数，从而产生大量资料并增加收集的资料量。由于感测器、无线通讯、网路通讯和云端运算技术的应用不断增加，需求侧和供应侧的资料正在被收集。
  • 油价波动导致能源相关计划支出高昂，对巨量资料分析产生了巨大需求。对高品质资讯的需求不断增加，预计这将推动市场成长。
  • 在当前情况下，缺乏数位技能和数位思维，以及缺乏有效处理非结构化资料进行分析的熟练专业人员和劳动力，是阻碍市场成长的因素之一。
  • 能源消耗直接受到GDP成长率、工业生产、消费者支出等宏观经济变数的影响。能源消耗通常随着製造业、交通运输和住宅等多个部门的经济成长而增加。为了优化生产、分配和消费的流程，能源产业需要日益复杂的分析解决方案。例如，根据世界银行的估计，2023年北美GDP为32.32兆美元，预计2023年至2024年将成长1.5%，企业活动和能源领域的巨量资料分析的增量将会增加。 。

能源产业巨量资料分析市场趋势

电网营运应用领域预计将占据主要市场占有率

  • 世界各地的能源需求正在增加。根据国际能源总署（IEA）预测，2005年至2030年间，能源需求将成长55%，从114亿吨石油当量增加到177亿吨石油当量，到2050年，全球能源消费量将达到886.3兆吨预计为英国热量单位。对于为电网供电的太阳能等可再生能源，公用事业公司可以使用需求响应分析来确定何时在高峰时段释放这些能源。
  • 资料分析在现代工业系统中发挥重要作用。电网正面临传统石化燃料的枯竭，要求电力系统透过脱烃来减少碳排放。智慧电网和超级电网是透过再生能源来源的高渗透率加快电气化步伐的有效解决方案。
  • 配电系统中使用的传统电錶仅产生少量资料，可以手动收集和分析这些数据以用于申请目的。从双向通讯智慧电网以各种时间分辨率收集的大量资料需要先进的资料分析来提取收费资讯和电力网路状态的关键资讯。例如，高解析度用户消费资料还可用于需求预测、客户行为分析和能源产生最佳化。
  • 智慧电网巨量资料分析有潜力改变电力产业。但要发挥它的最大价值，就必须正确使用它。智慧电网分析分为后勤部门分析（特定功能，例如并联型监督、负载预测和可靠性报告）和分散式分析（对来自仪表、感测器和其他设备的资料进行分析）。
  • 基于资料分析和先进测量基础设施的预测性维护和故障检测对于电力系统安全更为重要。这些解决方案预计会被早期采用者利用，因为它们已整合到他们的组织中。 GE 的新分析技术正在提高电网的效率。随着更多分散式资产被引入电网，该公司还有潜力利用来自输配电网的资料来帮助公共事业实现更高的营运效率。

预计北美将占据较大市场占有率

  • 北美是采用巨量资料分析的主要创新者和先驱者之一。由于大数据分析供应商的强大立足点，该地区对能源产业巨量资料巨量资料分析有着巨大的需求，为市场成长提供了利润丰厚的机会。
  • 与加拿大相比，美国在北美地区需求成长方面发挥着重要作用。石油和天然气、精製和发电行业的需求尤其增长。大多数美国人认为太阳能和风能是环保能源来源。约65%的人认为风力发电的环境效益优于大多数其他能源来源。
  • 石油和天然气公司正在从应用预测性维护解决方案中受益。基于物联网的预测性维护使石油和燃气公司能够识别潜在故障并增加关键资产的产量。这就是为什么雪佛龙等公司采用物联网开发来部署预测性维护解决方案以减少腐蚀和管道损坏的原因。该解决方案使用安装在整个管道中的感测器来测量 pH 值、CO2/H2S 含水量、气体洩漏以及管道的内径和厚度。该解决方案收集即时感测器资料并将其传递到云端进行评估、分析和预测。
  • 该地区处于智慧电网技术实施的前沿。该地区能源和公共事业领域的许多公司已经完全采用或正在实施巨量资料分析。在美国市场，许多大型投资者拥有的公用事业公司正在向其客户推出智慧电錶。据美国能源资讯署称，预计到年终美国将安装1.19亿个智慧电錶，但到年终已安装1.28亿个智慧电錶。
  • 巨量资料被广泛用于准确预测该地区的天气变数。使用计算智能技术观察不同的资料来源和模型进行即时分析。最近，市场领先的可再生能源监测和分析平台 Bazefield 推出了基于机器学习的 EnSight，为风能、太阳能、水力、生物质、电池储存和其他可再生技术提供现成的支援。已纳入Bazefield，以作为单一平台增强太阳能发电能力。

能源产业巨量资料分析产业概述

由于全球参与者和中小企业的存在，能源领域的巨量资料分析市场高度分散。该市场的主要企业包括 IBM 公司、西门子公司、SAP SE、戴尔技术公司和埃森哲公司。市场参与者正在采取联盟和收购等策略来增强其产品供应并获得永续的竞争优势。

  • 2023 年 11 月 - 西门子与加拿大的 Copperleaf（一家为关键基础设施公司提供资产规划和分析软体的供应商）合作，扩大其现有的电网软体合作伙伴生态系统。这项策略性合作关係旨在优化输电系统营运商（TSO）和配电系统营运商（DSO）的投资和技术电网规划。此次合作将西门子的电网规划、营运和维护软体与 Copperleaf 的资产管理能力结合，带来了电力系统和电网控制方面的广泛专业知识。

其他福利：

  • Excel 格式的市场预测 (ME) 表
  • 3 个月的分析师支持

目录

第一章简介

  • 研究假设和市场定义
  • 调查范围

第二章调查方法

第三章执行摘要

第四章市场洞察

  • 市场概况
  • 产业吸引力－波特五力分析
    • 供应商的议价能力
    • 买家/消费者的议价能力
    • 新进入者的威胁
    • 替代品的威胁
    • 竞争公司之间敌对关係的强度
  • 评估宏观经济趋势的影响

第五章市场动态

  • 市场驱动因素
    • 海量资料涌入
    • 原油价格波动
  • 市场限制因素
    • 缺乏技术纯熟劳工

第六章 市场细分

  • 按用途
    • 电网营运
    • 智慧电錶
    • 资产和劳动力管理
  • 按地区
    • 北美洲
    • 欧洲
    • 亚洲
    • 澳洲/纽西兰
    • 拉丁美洲
    • 中东/非洲

第七章 竞争格局

  • 公司简介
    • IBM Corporation
    • Siemens AG
    • SAP SE
    • Dell Technologies Inc.
    • Accenture PLC
    • Infosys Limited
    • Intel Corporation
    • Microsoft Corporation
    • Palantir Technologies Inc.
    • Enel X Italia Srl（Enel SpA）

第八章投资分析

第9章市场的未来

简介目录
Product Code: 51502

The Big Data Analytics Market In Energy is expected to grow from USD 9.56 billion in 2024 to USD 16.16 billion by 2029, at a CAGR of 11.07% during the forecast period (2024-2029).

Big Data Analytics In Energy Sector - Market

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

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 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)

8 INVESTMENT ANALYSIS

9 FUTURE OF THE MARKET