[250 Pages Report]Smart Grid Data Analytics Market analysis report by Future Market Insights shows that global sales of Smart Grid Data Analytics Market in 2021 was held at US$ 4.3 Billion. With the projected growth of 12.3% during 2022 to 2032, the market is expected to reach a valuation of US$ 14.9 Billion by 2032. Cloud-based Smart Grid Data Analytics is expected to be the highest revenue generating segment, projected to grow at a CAGR of around 14.9% during 2022 to 2032.
| Attributes | Details |
|---|---|
| Global Smart Grid Data Analytics Market Size (2022) | US$ 4.7 Billion |
| Global Smart Grid Data Analytics Market Size (2032) | US$ 14.9 Billion |
| Global Smart Grid Data Analytics Market CAGR (2022 to 2032) | 12.3% |
| USA Smart Grid Data Analytics Market Size (2032) | US$ 2.5 Billion |
| Key Companies Covered |
|
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As per the Smart Grid Data Analytics Market research by Future Market Insights - a market research and competitive intelligence provider, historically, from 2015 to 2021, market value of the Smart Grid Data Analytics Market increased at around 14% CAGR.
The global smart grid data analytics market is primarily driven by rising smart grid investments, a rapid increase in the pace of renewable energy integration into existing systems, and technical innovation.
The rise in smart meter installation is projected to expand the revenue for smart grid data analytics market. AMI or Advanced Metering Infrastructure is a structured system that incorporates smart meters, communications networks, and information management framework to provide a two-way digital interface among customers and utilities. AMI offers a variety of operational benefits that help to reduce utility costs and offer convenience to customers.
AMI decreases operational costs considerably by remotely reading meters, connecting/disconnecting services, recognizing outages by issuing more exact bills faster, and enabling utilities to offer consumers with digital access to their use information. Thus, rising smart meter installation and adoption of improved metering infrastructure are likely to drive the worldwide smart grid data analytics industry throughout the forecast period.
Improved grid reliability, outstanding inherent operational performance, and effective outage are expected to drive market growth. The grid feature technologies lead to problem detection and allow the network to self-heal automatically. With the continuous disruption detection, the enhanced technology provides real-time support to energy management services, increasing situational awareness in smart grid distribution control. For instance, Networked Energy Services Corporation, one of the global smart grid solutions and service provider with the industry's major Energy Applications Platform (EAPTM), confirmed in May 2021 the strengthening of its security products with threat identification and response, with plans to deploy over 1 Mn smart meters for its new grid watch solution by mid-2021.
Several government schemes and regulations are likely to promote market expansion. Different administrations are gradually investing in these innovations and technologies in the expectation that they would assist them in meeting their greenhouse emission reduction objectives and enabling long-term economic success. Furthermore, some nations currently have net energy measuring protocols and equipment, while others are still researching the innovation and its operation, which is likely to generate attractive market potential. Countries such as United States and China have seen widespread deployment of smart meters, owing mostly to the ongoing backing of their respective governments. These considerations are projected to boost growth for analytical solutions to manage the massive amounts of data generated by smart meters. Private utility providers in the United States, such as ConEd and Duke, have seen considerable increase in smart meter deployments. This is demonstrated by the estimates of Edison Foundation Institute for Electric Innovation that smart meters installed by utilities in the United States totalled around 98 Mn at the end of 2019 and are expected to reach 107 Mn by the end of 2020.
In another example, the Indian Finance Minister declared in 2020 that the government intends to replace all traditional power meters with smart electricity meters within three years. These government efforts conducted by governments throughout the world are driving market growth.
Principal Consultant
During the projected period, North America is expected to hold the largest share in global Smart Grid Data Analytics Market with a projected market size of nearly US$ 3 Bn by 2032. The smart grid data analytics market in North America is expected to witness high adoption of smart grid technologies due to huge expenditures in new grid developments and smart city initiatives. The region has promising government policies in place to promote sustainable energy generation. Furthermore, the increased demand for efficient energy in power generation is likely to drive the regional outlook.
Growing distribution automation investments, as well as the increasing complexity of electricity distribution infrastructure, are expected to boost the market outlook as well. For instance, the GridWise Alliance announced large investments in the transmission and distribution infrastructure system of the United States in July 2021. The project will spend US$ 5 Bn in grid adaptability technology including controls, sensors, and storage, and US$ 8.5 Bn in grid connection technologies like improved metering infrastructure and energy monitoring systems.
Other regions including as Asia Pacific and Europe, continue to account for a sizable market share due to broad acceptance of these solutions and a greater emphasis on sustainable and renewable energy development. For instance, in February 2020, the Government of India under the Smart Meter National Programme, declared that 1 Mn smart meters will be placed across the country (SMNP). In May 2020, the U.K government's smart metering project implementation programme showed around 26.6 Mn electricity meters operated by major energy firms in private households across the country.
The United States is expected to have the highest smart grid data analytics market share of US$ 2.5 Bn by the end of 2032 with an expected CAGR of 10% by the end of 2032. Due to the increasing severity and frequency of natural catastrophes, as well as unscheduled power outages, the United States will experience significant growth. Furthermore, increased government activities aimed at addressing climate change and ensuring mutual energy security, as well as the introduction of renewable energy into power networks, will augment the demand commercial sector.
Rising urbanization, theft prevention facilities, and lower management and operations costs for services are some of the key drivers encouraging smart grid data analytics market growth in the region. The integration of owner and customer power generation systems, including sustainable energy, allows environmental regulations and objectives to be met. Expanding partnerships, joint ventures, and inorganic strategic partnerships will benefit the industry environment.
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Generalized Solutions segment is forecasted to grow at the highest CAGR of around 14% during 2022-2032. To develop a stronger decision support system, smart grid data analytics solutions are installed at the grid owners' end. The generalized solutions are designed to evaluate data provided by various smart grid components such as smart meters, automated distribution systems, smart appliances, and other sensing devices. The gathered data is relayed utilizing the grid's two-way communication network for additional predictive analysis.
Revenue through cloud-based smart grid data analytics is forecasted to grow at the highest CAGR of around 14.9% during 2022-2032. The advent of cloud deployment choices for smart grid data analytics systems has increased demand across multiple industries, like IT and telecom, BFSI, and media & entertainment. The new companies are offering cloud-based solutions to provide cost-effective solutions to SMEs.
For businesses, an on-premises system can be restrictive as the result of being a real-time solution requiring significant implementation. This is avoided with an offsite system, having favorable operational enhancements because of its simplicity and lower expenses from reduced implementation. Another advantage is a broader array of resources that can be connected. Besides, cloud-based technologies are less expensive to implement, as compared to on-premise deployment.
In February 2022, IBM and SAP have increased their collaboration to assist clients in migrating operations from SAP Solutions to Cloud. IBM announced a collaboration with SAP to provide technology and consulting services to help clients embrace a hybrid cloud strategy and migrate mission-critical activities from SAP Solutions to cloud in regulated and non-regulated sectors.
The sector for smart grid data analytics is very much competitive and fragmented. The market has been experiencing fierce competition due to the introduction of new start-ups offering a diversified variety of creative solutions catering to different industrial requirements. Some of the key smart grid data analytics companies include Itron Inc., Siemens AG, and IBM Corporation
Some of the key recent market developments include:
Similarly, recent developments related to companies Smart Grid Data Analytics Market have been tracked by the team at Future Market Insights, which are available in the full report.
The global Smart Grid Data Analytics Market is worth more than US$ 4.3 Bn at present.
The value of Smart Grid Data Analytics Market is projected to increase at a CAGR of around 12.3% during 2022 – 2032.
The value of Smart Grid Data Analytics Market increased at a CAGR of around 14% during 2015 – 2021.
Utility companies’ increasing usage of smart grid data analytics to analyse load behavior, optimise grid operations, reduce power outages, and make smarter choices has been one of the key trends in smart grid data analytics market.
The market for Smart Grid Data Analytics Market in US is projected to expand at a CAGR of around 10% during 2022 – 2032.
1. Executive Summary
1.1. Global Market Outlook
1.2. Demand-side Trends
1.3. Supply-side Trends
1.4. Technology Roadmap Analysis
1.5. Analysis and Recommendations
2. Market Overview
2.1. Market Coverage / Taxonomy
2.2. Market Definition / Scope / Limitations
3. Market Background
3.1. Market Dynamics
3.1.1. Drivers
3.1.2. Restraints
3.1.3. Opportunity
3.1.4. Trends
3.2. Scenario Forecast
3.2.1. Demand in Optimistic Scenario
3.2.2. Demand in Likely Scenario
3.2.3. Demand in Conservative Scenario
3.3. Opportunity Map Analysis
3.4. Investment Feasibility Matrix
3.5. PESTLE and Porter’s Analysis
3.6. Regulatory Landscape
3.6.1. By Key Regions
3.6.2. By Key Countries
4. Global Market Analysis 2017-2021 and Forecast, 2022-2032
4.1. Historical Market Size Value (US$ Mn) Analysis, 2017-2021
4.2. Current and Future Market Size Value (US$ Mn) Projections, 2022-2032
4.2.1. Y-o-Y Growth Trend Analysis
4.2.2. Absolute $ Opportunity Analysis
5. Global Market Analysis 2017-2021 and Forecast 2022-2032, By Component
5.1. Introduction / Key Findings
5.2. Historical Market Size Value (US$ Mn) Analysis By Component, 2017-2021
5.3. Current and Future Market Size Value (US$ Mn) Analysis and Forecast By Component, 2022-2032
5.3.1. Solution
5.3.1.1. AMI Analytics
5.3.1.2. Demand Response Analytics
5.3.1.3. Grid Optimization
5.3.1.4. Asset Management
5.3.1.5. Others
5.4. Y-o-Y Growth Trend Analysis By Component, 2017-2021
5.5. Absolute $ Opportunity Analysis By Component, 2022-2032
6. Global Market Analysis 2017-2021 and Forecast 2022-2032, By Deployment Model
6.1. Introduction / Key Findings
6.2. Historical Market Size Value (US$ Mn) Analysis By Deployment Model, 2017-2021
6.3. Current and Future Market Size Value (US$ Mn) Analysis and Forecast By Deployment Model, 2022-2032
6.3.1. On-premise
6.3.2. Cloud-based
6.3.3. Hybrid
6.4. Y-o-Y Growth Trend Analysis By Deployment Model, 2017-2021
6.5. Absolute $ Opportunity Analysis By Deployment Model, 2022-2032
7. Global Market Analysis 2017-2021 and Forecast 2022-2032, By End-User
7.1. Introduction / Key Findings
7.2. Historical Market Size Value (US$ Mn) Analysis By End-User, 2017-2021
7.3. Current and Future Market Size Value (US$ Mn) Analysis and Forecast By End-User, 2022-2032
7.3.1. Small/ Medium Enterprises
7.3.2. Large Enterprises
7.3.3. Public Sector
7.4. Y-o-Y Growth Trend Analysis By End-User, 2017-2021
7.5. Absolute $ Opportunity Analysis By End-User, 2022-2032
8. Global Market Analysis 2017-2021 and Forecast 2022-2032, By IT Solution
8.1. Introduction / Key Findings
8.2. Historical Market Size Value (US$ Mn) Analysis By IT Solution, 2017-2021
8.3. Current and Future Market Size Value (US$ Mn) Analysis and Forecast By IT Solution, 2022-2032
8.3.1. Specialized Solutions (for Back-end)
8.3.1.1. CRM
8.3.1.2. Billing
8.3.1.3. Customer Care
8.3.1.4. Business Intelligence
8.3.1.5. Others
8.3.2. Generalized Solutions (for Front-end)
8.3.2.1. CRM
8.3.2.2. Billing
8.3.2.3. Customer Care
8.3.2.4. Business Intelligence
8.3.2.5. Others
8.4. Y-o-Y Growth Trend Analysis By IT Solution, 2017-2021
8.5. Absolute $ Opportunity Analysis By IT Solution, 2022-2032
9. Global Market Analysis 2017-2021 and Forecast 2022-2032, By Region
9.1. Introduction
9.2. Historical Market Size Value (US$ Mn) Analysis By Region, 2017-2021
9.3. Current Market Size Value (US$ Mn) Analysis and Forecast By Region, 2022-2032
9.3.1. North America
9.3.2. Latin America
9.3.3. Europe
9.3.4. Asia Pacific
9.3.5. MEA
9.4. Market Attractiveness Analysis By Region
10. North America Market Analysis 2017-2021 and Forecast 2022-2032, By Country
10.1. Historical Market Size Value (US$ Mn) Trend Analysis By Market Taxonomy, 2017-2021
10.2. Market Size Value (US$ Mn) Forecast By Market Taxonomy, 2022-2032
10.2.1. By Country
10.2.1.1. U.S.
10.2.1.2. Canada
10.2.2. By Component
10.2.3. By Deployment Model
10.2.4. By End-User
10.2.5. By IT Solution
10.3. Market Attractiveness Analysis
10.3.1. By Country
10.3.2. By Component
10.3.3. By Deployment Model
10.3.4. By End-User
10.3.5. By IT Solution
10.4. Key Takeaways
11. Latin America Market Analysis 2017-2021 and Forecast 2022-2032, By Country
11.1. Historical Market Size Value (US$ Mn) Trend Analysis By Market Taxonomy, 2017-2021
11.2. Market Size Value (US$ Mn) Forecast By Market Taxonomy, 2022-2032
11.2.1. By Country
11.2.1.1. Brazil
11.2.1.2. Mexico
11.2.1.3. Rest of Latin America
11.2.2. By Component
11.2.3. By Deployment Model
11.2.4. By End-User
11.2.5. By IT Solution
11.3. Market Attractiveness Analysis
11.3.1. By Country
11.3.2. By Component
11.3.3. By Deployment Model
11.3.4. By End-User
11.3.5. By IT Solution
11.4. Key Takeaways
12. Europe Market Analysis 2017-2021 and Forecast 2022-2032, By Country
12.1. Historical Market Size Value (US$ Mn) Trend Analysis By Market Taxonomy, 2017-2021
12.2. Market Size Value (US$ Mn) Forecast By Market Taxonomy, 2022-2032
12.2.1. By Country
12.2.1.1. Germany
12.2.1.2. Italy
12.2.1.3. France
12.2.1.4. U.K.
12.2.1.5. Spain
12.2.1.6. Russia
12.2.1.7. BENELUX
12.2.1.8. Rest of Europe
12.2.2. By Component
12.2.3. By Deployment Model
12.2.4. By End-User
12.2.5. By IT Solution
12.3. Market Attractiveness Analysis
12.3.1. By Country
12.3.2. By Component
12.3.3. By Deployment Model
12.3.4. By End-User
12.3.5. By IT Solution
12.4. Key Takeaways
13. Asia Pacific Market Analysis 2017-2021 and Forecast 2022-2032, By Country
13.1. Historical Market Size Value (US$ Mn) Trend Analysis By Market Taxonomy, 2017-2021
13.2. Market Size Value (US$ Mn) Forecast By Market Taxonomy, 2022-2032
13.2.1. By Country
13.2.1.1. China
13.2.1.2. Japan
13.2.1.3. South Korea
13.2.1.4. India
13.2.1.5. Rest of Asia Pacific
13.2.2. By Component
13.2.3. By Deployment Model
13.2.4. By End-User
13.2.5. By IT Solution
13.3. Market Attractiveness Analysis
13.3.1. By Country
13.3.2. By Component
13.3.3. By Deployment Model
13.3.4. By End-User
13.3.5. By IT Solution
13.4. Key Takeaways
14. MEA Market Analysis 2017-2021 and Forecast 2022-2032, By Country
14.1. Historical Market Size Value (US$ Mn) Trend Analysis By Market Taxonomy, 2017-2021
14.2. Market Size Value (US$ Mn) Forecast By Market Taxonomy, 2022-2032
14.2.1. By Country
14.2.1.1. GCC
14.2.1.2. Rest of MEA
14.2.2. By Component
14.2.3. By Deployment Model
14.2.4. By End-User
14.2.5. By IT Solution
14.3. Market Attractiveness Analysis
14.3.1. By Country
14.3.2. By Component
14.3.3. By Deployment Model
14.3.4. By End-User
14.3.5. By IT Solution
14.4. Key Takeaways
15. Key Countries Market Analysis
15.1. U.S.
15.1.1. Pricing Analysis
15.1.2. Market Share Analysis, 2021
15.1.2.1. By Component
15.1.2.2. By Deployment Model
15.1.2.3. By End-User
15.1.2.4. By IT Solution
15.2. Canada
15.2.1. Pricing Analysis
15.2.2. Market Share Analysis, 2021
15.2.2.1. By Component
15.2.2.2. By Deployment Model
15.2.2.3. By End-User
15.2.2.4. By IT Solution
15.3. Brazil
15.3.1. Pricing Analysis
15.3.2. Market Share Analysis, 2021
15.3.2.1. By Component
15.3.2.2. By Deployment Model
15.3.2.3. By End-User
15.3.2.4. By IT Solution
15.4. Mexico
15.4.1. Pricing Analysis
15.4.2. Market Share Analysis, 2021
15.4.2.1. By Component
15.4.2.2. By Deployment Model
15.4.2.3. By End-User
15.4.2.4. By IT Solution
15.5. Argentina
15.5.1. Pricing Analysis
15.5.2. Market Share Analysis, 2021
15.5.2.1. By Component
15.5.2.2. By Deployment Model
15.5.2.3. By End-User
15.5.2.4. By IT Solution
15.6. Germany
15.6.1. Pricing Analysis
15.6.2. Market Share Analysis, 2021
15.6.2.1. By Component
15.6.2.2. By Deployment Model
15.6.2.3. By End-User
15.6.2.4. By IT Solution
15.7. Italy
15.7.1. Pricing Analysis
15.7.2. Market Share Analysis, 2021
15.7.2.1. By Component
15.7.2.2. By Deployment Model
15.7.2.3. By End-User
15.7.2.4. By IT Solution
15.8. France
15.8.1. Pricing Analysis
15.8.2. Market Share Analysis, 2021
15.8.2.1. By Component
15.8.2.2. By Deployment Model
15.8.2.3. By End-User
15.8.2.4. By IT Solution
15.9. U.K.
15.9.1. Pricing Analysis
15.9.2. Market Share Analysis, 2021
15.9.2.1. By Component
15.9.2.2. By Deployment Model
15.9.2.3. By End-User
15.9.2.4. By IT Solution
15.10. Spain
15.10.1. Pricing Analysis
15.10.2. Market Share Analysis, 2021
15.10.2.1. By Component
15.10.2.2. By Deployment Model
15.10.2.3. By End-User
15.10.2.4. By IT Solution
15.11. Russia
15.11.1. Pricing Analysis
15.11.2. Market Share Analysis, 2021
15.11.2.1. By Component
15.11.2.2. By Deployment Model
15.11.2.3. By End-User
15.11.2.4. By IT Solution
15.12. BENELUX
15.12.1. Pricing Analysis
15.12.2. Market Share Analysis, 2021
15.12.2.1. By Component
15.12.2.2. By Deployment Model
15.12.2.3. By End-User
15.12.2.4. By IT Solution
15.13. China
15.13.1. Pricing Analysis
15.13.2. Market Share Analysis, 2021
15.13.2.1. By Component
15.13.2.2. By Deployment Model
15.13.2.3. By End-User
15.13.2.4. By IT Solution
15.14. Japan
15.14.1. Pricing Analysis
15.14.2. Market Share Analysis, 2021
15.14.2.1. By Component
15.14.2.2. By Deployment Model
15.14.2.3. By End-User
15.14.2.4. By IT Solution
15.15. South Korea
15.15.1. Pricing Analysis
15.15.2. Market Share Analysis, 2021
15.15.2.1. By Component
15.15.2.2. By Deployment Model
15.15.2.3. By End-User
15.15.2.4. By IT Solution
15.16. India
15.16.1. Pricing Analysis
15.16.2. Market Share Analysis, 2021
15.16.2.1. By Component
15.16.2.2. By Deployment Model
15.16.2.3. By End-User
15.16.2.4. By IT Solution
15.17. GCC Countries
15.17.1. Pricing Analysis
15.17.2. Market Share Analysis, 2021
15.17.2.1. By Component
15.17.2.2. By Deployment Model
15.17.2.3. By End-User
15.17.2.4. By IT Solution
16. Market Structure Analysis
16.1. Competition Dashboard
16.2. Competition Benchmarking
16.3. Market Share Analysis of Top Players
16.3.1. By Regional
16.3.2. By Component
16.3.3. By Deployment Model
16.3.4. By End-User
16.3.5. By IT Solution
17. Competition Analysis
17.1. Competition Deep Dive
17.1.1. Accenture, Plc.
17.1.1.1. Overview
17.1.1.2. Product Portfolio
17.1.1.3. Profitability by Market Segment
17.1.1.4. Sales Footprint
17.1.1.5. Strategy Overview
17.1.1.5.1. Marketing Strategy
17.1.2. Capgemini S.A., Inc.
17.1.2.1. Overview
17.1.2.2. Product Portfolio
17.1.2.3. Profitability by Market Segment
17.1.2.4. Sales Footprint
17.1.2.5. Strategy Overview
17.1.2.5.1. Marketing Strategy
17.1.3. Dell EMC
17.1.3.1. Overview
17.1.3.2. Product Portfolio
17.1.3.3. Profitability by Market Segment
17.1.3.4. Sales Footprint
17.1.3.5. Strategy Overview
17.1.3.5.1. Marketing Strategy
17.1.4. IBM Corporation
17.1.4.1. Overview
17.1.4.2. Product Portfolio
17.1.4.3. Profitability by Market Segment
17.1.4.4. Sales Footprint
17.1.4.5. Strategy Overview
17.1.4.5.1. Marketing Strategy
17.1.5. Oracle Corporation
17.1.5.1. Overview
17.1.5.2. Product Portfolio
17.1.5.3. Profitability by Market Segment
17.1.5.4. Sales Footprint
17.1.5.5. Strategy Overview
17.1.5.5.1. Marketing Strategy
17.1.6. Schneider Electric
17.1.6.1. Overview
17.1.6.2. Product Portfolio
17.1.6.3. Profitability by Market Segment
17.1.6.4. Sales Footprint
17.1.6.5. Strategy Overview
17.1.6.5.1. Marketing Strategy
17.1.7. Itron Inc.
17.1.7.1. Overview
17.1.7.2. Product Portfolio
17.1.7.3. Profitability by Market Segment
17.1.7.4. Sales Footprint
17.1.7.5. Strategy Overview
17.1.7.5.1. Marketing Strategy
17.1.8. AES Ohio
17.1.8.1. Overview
17.1.8.2. Product Portfolio
17.1.8.3. Profitability by Market Segment
17.1.8.4. Sales Footprint
17.1.8.5. Strategy Overview
17.1.8.5.1. Marketing Strategy
18. Assumptions & Acronyms Used
19. Research Methodology
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Smart Grid Data Analytics Market
