Smart Grid Analytics Market Size, Share, Growth Analysis, by Offering (Meter analytics, Reliability Analytics, Services), Application (AMI, Load Forecasting, Demand Response, Grid Optimization), Analytics Type and Region - Global Industry Forecast to 2029
[323 Pages Report] The smart grid analytics market is witnessing a rapid growth trajectory, with estimates projecting a substantial market value surge from approximately USD 7.9 billion in 2024 to USD 14.3 billion by 2029. This phenomenal upward trend, characterized by a remarkable CAGR of 12.4% between 2024–2029, is driven by enhanced adoption of smart grid metering solutions, growing need to upgrade aging power grid systems, and increasing drive towards energy-efficient and sustainable technologies. Governments worldwide are actively supporting the deployment of smart grid technologies through various initiatives and policies. Additionally, collaborations between governments, utilities, and technology providers are fostering innovation in smart grid analytics, ensuring continuous improvements in grid performance and resilience.
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Market Dynamics
Driver: Growing demand for energy efficiency and sustainability
Growing demand for energy efficiency and sustainability is driving the smart grid analytics market as utilities and consumers alike seek to optimize energy usage and reduce environmental impact. Smart grid analytics enable real-time monitoring and management of energy consumption, leading to more efficient operations and reduced wastage. They also support the integration of renewable energy sources, enhancing grid stability and promoting sustainable practices. Regulatory pressures and incentives for reducing carbon footprints further compel utilities to adopt these advanced analytics solutions. Consequently, the push for more sustainable energy practices aligns with the capabilities of smart grid analytics, fostering their widespread adoption and market growth.
Restraint: High initial investment and implementation costs
High initial investment and implementation costs in the smart grid analytics market stem from the need for advanced hardware, software, and skilled personnel to deploy and maintain these systems. Utilities must invest in upgrading existing infrastructure, integrating new technologies, and training staff, which can be financially burdensome, especially for smaller entities. Moreover, the complexity of integrating various data sources and ensuring interoperability between different systems adds to the costs. These financial and technical barriers can delay or limit the adoption of smart grid analytics, particularly in regions or organizations with constrained budgets or less access to capital, hindering widespread implementation.
Opportunity: Boost to the growing smart city initiatives
Smart grid analytics significantly enhance the growth of smart city initiatives by optimizing energy distribution, improving grid reliability, and facilitating the integration of renewable energy sources. These analytics enable real-time monitoring and predictive maintenance of grid infrastructure, reducing downtime and operational costs. By providing detailed insights into energy consumption patterns, they help cities implement more efficient energy management practices, support the development of electric vehicle infrastructure, and enhance overall sustainability. Furthermore, smart grid analytics enable better demand response strategies, enhancing the ability of cities to balance energy supply and demand dynamically, thereby supporting the broader goals of smart city development and environmental sustainability.
Challenge: Data management complexity associated with smart grids
Data management complexity in smart grids arises from the vast amount of data generated by diverse sources such as smart meters, sensors, and distributed energy resources. This data is heterogeneous, coming in various formats and requiring integration and real-time processing to be useful. Ensuring data accuracy, consistency, and security is challenging, especially with the need for interoperability across different systems and platforms. Additionally, advanced analytics require robust data storage and management solutions to handle the volume and velocity of data, necessitating significant investment in infrastructure and expertise.
Smart grid analytics Market Ecosystem
By software type, operational analytics segment to account for the largest market share in 2024
Operational analytics software will command the largest market share in the smart grid analytics market due to its critical role in enhancing grid performance and efficiency. This software enables real-time monitoring, data analysis, and optimization of grid operations, helping utilities to quickly identify and resolve issues, reduce outages, and improve energy distribution. The growing complexity of modern grids, driven by the integration of renewable energy sources and distributed energy resources, necessitates robust operational analytics to ensure reliability and resilience, making it indispensable for utilities aiming to maintain high service standards and regulatory compliance.
By deployment mode, cloud deployment mode is slated to register the highest growth rate during the forecast period.
The cloud deployment mode of the smart grid analytics market is set for the fastest growth due to its scalability, cost-effectiveness, and ease of implementation. Cloud solutions enable utilities to quickly deploy advanced analytics without the need for significant upfront investments in hardware. They offer flexible, on-demand access to powerful computing resources and data storage, facilitating real-time analytics and remote monitoring. Additionally, cloud-based platforms are continuously updated with the latest features and security measures, allowing utilities to stay ahead of technological advancements and regulatory requirements efficiently.
By organization size, large enterprises segment will hold the largest market share in 2024.
The large enterprises segment will dominate the smart grid analytics market due to their substantial financial resources, extensive infrastructure, and ability to invest in advanced technologies. These enterprises require robust analytics solutions to manage complex, large-scale grid operations efficiently. Their need for enhanced grid reliability, regulatory compliance, and improved operational efficiency drives significant investments in smart grid analytics. Additionally, large enterprises often lead in adopting cutting-edge technologies, further cementing their dominant market share as they implement comprehensive, integrated analytics solutions to optimize their grid systems.
By region, Asia Pacific is set to experience the fastest growth rate during the forecast period.
The Asia Pacific region is projected to experience the fastest growth in the smart grid analytics market due to several key factors. Rapid urbanization and industrialization across countries like China, India, Japan, and South Korea are driving substantial increases in energy demand. Governments in the region are investing heavily in smart grid technologies to modernize aging infrastructure, improve energy efficiency, and integrate renewable energy sources. Additionally, supportive regulatory frameworks and initiatives aimed at reducing carbon emissions are encouraging the adoption of advanced grid analytics. The region's focus on technological innovation and the growing number of smart city projects further contribute to the accelerated adoption of smart grid analytics solutions.
Key Market Players
The smart grid analytics solution and service providers have implemented several types of organic and inorganic growth strategies, such as new product launches, product upgrades, partnerships, and agreements, business expansions, and mergers and acquisitions to strengthen their offerings in the market. Some major players in the smart grid analytics market include Siemens (Germany), Oracle (US), GE Vernova (US), Schneider Electric (US), Landis+Gyr (Switzerland), along with SMEs and startups such as Autogrid Systems (US), eSmart Systems (Norway), Innwatts (US), Amperon (US), and Kevala (US).
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Report Metrics |
Details |
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Market size available for years |
2019–2029 |
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Base year considered |
2023 |
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Forecast period |
2024–2029 |
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Forecast units |
USD (Billion) |
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Segments Covered |
Offering, Organization Size, Application, Anlytics Type, and Region |
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Geographies covered |
North America, Europe, Asia Pacific, Middle East & Africa, and Latin America |
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List of Companies covered |
Siemens (Germany), IBM (US), Oracle (US), Schneider Electric (France), GE Vernova (US), Landis+Gyr (Switzerland), Capgemini (France), Infosys (India), SAP (Germany), Honeywell (US), Accenture (Ireland), SAS Institute (US), Itron (US), Autogrid Systems (US), Hive Power (Switzerland), eSmart Systems (Norway), SteamaCo (UK), Grid4C (US), Globema (Poland), SparkMeter (US), Innwatts (US), Amperon (US), Kevala (US), GridPoint (US), Safegrid (Finland), Sentient Energy (US). |
This research report categorizes the smart grid analytics market based on offering, organization size, application, analytics type, and region:
By Offering:
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Software
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Software, By Type
- Meter Analytics
- Operational Analytics
- Reliability Analytics
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Software, By Deployment Mode
- Cloud
- On-Premises
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Software, By Type
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Services
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Professional Services
- Training & Consulting Services
- System Integration & Deployment Services
- Support & Maintenance Services
- Managed Services
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Professional Services
By Organization Size
- Small and Medium Businesses (SMBs)
- Large Enterprises
By Application
- AMI Analytics
- Load Forecasting and Demand Response
- Grid Optimization, Monitoring, and Management
- Distributed Energy Resources Management Systems (DERMS)
- Predictive Maintenance
- Energy Theft Detection and Cybersecurity
- Voltage, Frequency, and Stability Management
- Customer Engagement and Analytics
- Visual Analytics
- Outage Management and Fault Detection
- Other Applications
By Vertical
- Descriptive Analytics
- Diagnostic Analytics
- Predictive Analytics
- Prescriptive Analytics
By Region
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North America
- United States
- Canada
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Europe
- UK
- Germany
- France
- Italy
- Spain
- Rest of Europe
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Asia Pacific
- China
- India
- Japan
- South Korea
- Australia and New Zealand (ANZ)
- Rest of Asia Pacific
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Middle East and Africa
- Saudi Arabia
- UAE
- South Africa
- Rest of Middle East and Africa
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Latin America
- Brazil
- Mexico
- Rest of Latin America
Recent Developments:
- In May 2024, GE Vernova launched Autonomous Inspection, a cloud-based computer vision software solution designed to automate the manual inspection and monitoring of industrial assets by utilizing image capture devices and artificial intelligence/machine learning (AI/ML) algorithms.
- In May 2024, Energinet signed an official partnership with Siemens Energy on a comprehensive expansion of the power grid in Western Denmark. By 2029, Denmark must quadruple its electricity generation from wind and solar power and use modern analytics and integrated technologies, with more to come in the following decades.
- In May 2024, Honeywell partnered with Enel North America to enhance building automation and demand response solutions for commercial and industrial organizations by using automation to control and regulate energy loads to help stabilize the power grid.
- In March 2024, Itron, Inc. expanded its Grid Edge Intelligence portfolio through the acquisition of Elpis Squared, an innovative provider of software and services for utility grid operations, effective immediately. The acquisition of Elpis Squared allows Itron to embed real-time, high-resolution grid edge data into the grid planning, operations, and engineering process – an industry first.
- In February 2024, SPAN and Landis+Gyr formed a strategic partnership to cost-effectively advance electrification, create grid flexibility, and build resilience. The partnership brings together two energy industry leaders to help utilities improve utilization of existing assets, unlock Distributed Energy Resource (DER) flexibility management, and enhance customer engagement.
- In February 2024, Itron, Inc. and Schneider Electric collaborated to improve energy and grid management for utilities as homeowners and businesses increasingly adopt distributed energy resources (DER)—like rooftop solar, battery energy storage, electric vehicles and microgrids—at the grid edge.
Frequently Asked Questions (FAQ):
What is Smart grid analytics?
Smart grid analytics refers to the application of advanced data analytics, artificial intelligence, and machine learning techniques to enhance the efficiency, reliability, and sustainability of electric power grids. It involves the analysis of large volumes of data generated by smart meters, sensors, and other grid components to optimize grid operations, predict equipment failures, and manage distributed energy resources effectively. Smart grid analytics enables utilities to improve grid performance, reduce operational costs, and meet regulatory requirements, thereby transforming traditional power grids into more intelligent and responsive systems.
What is the total CAGR expected to be recorded for the Smart grid analytics market during 2024-2029?
The Smart grid analytics market is expected to record a CAGR of 12.4% from 2024-2029.
How can smart grid analytics be used to integrate renewable energy sources?
Smart grid analytics facilitate the integration of renewable energy sources by providing real-time monitoring and forecasting of energy output, optimizing demand response strategies, and ensuring grid stability through predictive maintenance. These analytics also manage energy storage systems efficiently, storing excess renewable energy and dispatching it as needed. Additionally, smart grid analytics coordinate distributed energy resources and ensure regulatory compliance through accurate reporting of renewable energy usage. These capabilities enable a smoother integration and maximization of renewable energy within the grid, promoting sustainability and resilience.
Which are the key drivers supporting the growth of the smart grid analytics market?
The key factors driving the growth of the smart grid analytics market include rising importance of efficient and sustainable energy use, increasing focus on the replacement of old grid infrastructure, and proliferation of smart meter installations around the globe.
Which are the top 3 applications prevailing in the smart grid analytics market?
Grid optimization, monitoring, & management, distributed energy resources management systems, and load forecasting & demand responseare the top three applications in the smart grid analytics market. These applications are crucial for enhancing grid reliability and efficiency, integrating renewable energy sources, and balancing supply and demand. They enable real-time monitoring, predictive maintenance, and effective management of distributed energy resources, ensuring a resilient and sustainable energy grid.
Who are the key vendors in the smart grid analytics market?
Some major players in the smart grid analytics market include Siemens (Germany), IBM (US), Oracle (US), Schneider Electric (France), GE Vernova (US), Landis+Gyr (Switzerland), Capgemini (France), Infosys (India), SAP (Germany), Honeywell (US), Accenture (Ireland), SAS Institute (US), Itron (US), Autogrid Systems (US), Hive Power (Switzerland), eSmart Systems (Norway), SteamaCo (UK), Grid4C (US), Globema (Poland), SparkMeter (US), Innwatts (US), Amperon (US), Kevala (US), GridPoint (US), Safegrid (Finland), Sentient Energy (US). .
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The smart grid analytics market research study involved extensive secondary sources, directories, journals, and paid databases. Primary sources were mainly industry experts from the core and related industries, preferred smart grid analytics providers, third-party service providers, consulting service providers, end users, and other commercial enterprises. In-depth interviews were conducted with various primary respondents, including key industry participants and subject matter experts, to obtain and verify critical qualitative and quantitative information, and assess the market’s prospects.
Secondary Research
In the secondary research process, various sources were referred to, for identifying and collecting information for this study. Secondary sources included annual reports, press releases, and investor presentations of companies; white papers, journals, and certified publications; and articles from recognized authors, directories, and databases. The data was also collected from other secondary sources, such as journals, government websites, blogs, and vendors websites. Additionally, smart grid analytics spending of various countries was extracted from the respective sources. Secondary research was mainly used to obtain key information related to the industry’s value chain and supply chain to identify key players based on solutions, services, market classification, and segmentation according to offerings of major players, industry trends related to software, hardware, services, technology, applications, warehouse sizes, verticals, and regions, and key developments from both market- and technology-oriented perspectives.
Primary Research
In the primary research process, various primary sources from both supply and demand sides were interviewed to obtain qualitative and quantitative information on the market. The primary sources from the supply side included various industry experts, including Chief Experience Officers (CXOs); Vice Presidents (VPs); directors from business development, marketing, and smart grid analytics expertise; related key executives from smart grid analytics solution vendors, SIs, professional service providers, and industry associations; and key opinion leaders.
Primary interviews were conducted to gather insights, such as market statistics, revenue data collected from solutions and services, market breakups, market size estimations, market forecasts, and data triangulation. Primary research also helped in understanding various trends related to technologies, applications, deployments, and regions. Stakeholders from the demand side, such as Chief Information Officers (CIOs), Chief Technology Officers (CTOs), Chief Strategy Officers (CSOs), and end users using smart grid analytics solutions, were interviewed to understand the buyer’s perspective on suppliers, products, service providers, and their current usage of smart grid analytics solutions and services, which would impact the overall smart grid analytics market.
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Market Size Estimation
Multiple approaches were adopted for estimating and forecasting the smart grid analytics market. The first approach involves estimating the market size by summation of companies’ revenue generated through the sale of solutions and services.
Market Size Estimation Methodology-Top-down approach
In the top-down approach, an exhaustive list of all the vendors offering solutions and services in the smart grid analytics market was prepared. The revenue contribution of the market vendors was estimated through annual reports, press releases, funding, investor presentations, paid databases, and primary interviews. Each vendor's offerings were evaluated based on breadth of software and services according to analytics type, applications, deployment modes, and organization size. The aggregate of all the companies’ revenue was extrapolated to reach the overall market size. Each subsegment was studied and analyzed for its global market size and regional penetration. The markets were triangulated through both primary and secondary research. The primary procedure included extensive interviews for key insights from industry leaders, such as CIOs, CEOs, VPs, directors, and marketing executives. The market numbers were further triangulated with the existing MarketsandMarkets’ repository for validation.
Market Size Estimation Methodology-Bottom-up approach
In the bottom-up approach, the adoption rate of smart grid analytics solutions and services among different end users in key countries with respect to their regions contributing the most to the market share was identified. For cross-validation, the adoption of smart grid analytics solutions and services among different end users, along with different use cases with respect to their regions, was identified and extrapolated. Weightage was given to use cases identified in different regions for the market size calculation.
Based on the market numbers, the regional split was determined by primary and secondary sources. The procedure included the analysis of the market’s regional penetration. Based on secondary research, the regional spending on Information and Communications Technology (ICT), socio-economic analysis of each country, strategic vendor analysis of major smart grid analytics providers, and organic and inorganic business development activities of regional and global players were estimated. With the data triangulation procedure and data validation through primary interviews, the exact values of the overall smart grid analytics market size and segments’ size were determined and confirmed using the study.
Global Smart grid analytics Market Size: Bottom-Up and Top-Down Approach:
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Data Triangulation
After arriving at the overall market size using the market size estimation processes as explained above, the market was split into several segments and subsegments. To complete the overall market engineering process and arrive at the exact statistics of each market segment and subsegment, data triangulation and market breakup procedures were employed, wherever applicable. The overall market size was then used in the top-down procedure to estimate the size of other individual markets via percentage splits of the market segmentation.
Market Definition
Smart grid analytics refers to the application of advanced data analytics, artificial intelligence, and machine learning techniques to enhance the efficiency, reliability, and sustainability of electric power grids. It involves the analysis of large volumes of data generated by smart meters, sensors, and other grid components to optimize grid operations, predict equipment failures, and manage distributed energy resources effectively. Smart grid analytics enables utilities to improve grid performance, reduce operational costs, and meet regulatory requirements, thereby transforming traditional power grids into more intelligent and responsive systems.
Stakeholders
- Smart grid analytics software developers
- Cybersecurity firms
- Business analysts
- Cloud service providers
- Consulting service providers
- Enterprise end-users
- Distributors and Value-added Resellers (VARs)
- Government agencies
- Independent Software Vendors (ISV)
- Managed service providers
- Market research and consulting firms
- Support & maintenance service providers
- System Integrators (SIs)/migration service providers
- Technology providers
Report Objectives
- To define, describe, and forecast the smart grid analytics market, by offering (software, and services), organization size, application, and analytics type
- To provide detailed information related to major factors (drivers, restraints, opportunities, and industry-specific challenges) influencing the market growth
- To analyze the micro markets with respect to individual growth trends, prospects, and their contribution to the total market
- To analyze the opportunities in the market for stakeholders by identifying the high-growth segments of the market
- To analyze opportunities in the market and provide details of the competitive landscape for stakeholders and market leaders
- To forecast the market size of segments for five main regions: North America, Europe, Asia Pacific, the Middle East & Africa, and Latin America
- To profile the key players and comprehensively analyze their market ranking and core competencies
- To analyze competitive developments, such as partnerships, product launches, and mergers and acquisitions, in the smart grid analytics market
- To analyze the impact of recession in the smart grid analytics market across all the regions
Available customizations
With the given market data, MarketsandMarkets offers customizations as per your company’s specific needs. The following customization options are available for the report:
Product Analysis
- Product quadrant, which gives a detailed comparison of the product portfolio of each company.
Geographic Analysis
- Further breakup of the North American smart grid analytics market
- Further breakup of the European market
- Further breakup of the Asia Pacific smart grid analytics market
- Further breakup of the Middle Eastern & African smart grid analytics market
- Further breakup of the Latin America smart grid analytics market
Company Information
- Detailed analysis and profiling of additional market players (up to five)
Growth opportunities and latent adjacency in Smart Grid Analytics Market