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Opportunities in Edge Intelligence 

  • July 2015
  • -
  • Frost & Sullivan
  • -
  • 44 pages

Opportunities in Edge Intelligence : Enabling the Interconnection of the Grid of Things

Edge intelligence is the combination of business intelligence and automation that can sense and synthesize massive volumes of data and make decisions close to the data collection point. Applications include collecting, analyzing, and communicating data within the specified ecosystem and making real-time decisions to achieve unprecedented levels of reliability and efficiency. Earlier, monitoring systems used to gather data and communicate the same to the central control system to trigger alerts or generate standalone reports and displays for users. In today's edge intelligence architecture, the grid is designed to perform a host of functions, including decision making, close to the point of data collection, at speeds that centralized systems cannot match. This study includes a discussion of key drivers and challenges that influence the demand for edge intelligence.

Executive Summary—Key Findings

Edge intelligence uptake is being driven by the increasing preference of renewables and utilities to leverage technologies to modernize grid infrastructure

By 2020, global annual investment in smart grids will reach $ billion. Distribution automation, network communication platforms, software, and cyber security will be some of the major areas of investment over the next years. North America and Europe will remain strong markets for investments in the short to medium term. In the long term, growth in Asia-Pacific will counter the slowdown in the growth rates of North America and Europe. Next-generation grid infrastructure will replace purpose-built grid devices to multi-purpose computing devices with the ability to communicate with peers in an open platform.

Table Of Contents

Opportunities in Edge Intelligence 
Table Of Contents

Executive Summary
1. Future of Intelligence in the Utility Industry
2. Grid Edge Intelligence—Preparing for the Future
3. Executive Summary—Key Findings

Definition of Edge Intelligence
1. Introduction to Edge Intelligence
2. Evolution of Grid Decision Making
3. Evolution of Grid Decision Making
4. How and why is Edge Intelligence So Important in a Smart Grid?

Challenges and Drivers
1. Challenge—Typical Edge Intelligence-related Concerns
2. Challenge—Typical Edge Intelligence-related Concerns
3. Driver—Siloed to De-siloed Business Structure
4. Driver—Rise of Operational Analytics
5. Driver—Focus on Maximizing Capabilities

Global Market Outlook
1. The Era of Edge Intelligence (2015-2020)
2. Rise of Analytics—From Data to Intelligence
3. Edge Intelligence—Global Trends
4. Edge Intelligence—Global Trends
5. Edge Intelligence—Global Trends
6. Current and Future Outlook
7. Edge Intelligence—Stakeholders

Case Study
1. Case Study 1—Grid Edge Implementation: Duke Energy
2. Case Study 1—Grid Edge Implementation: Duke Energy
3. Case Study 2—Achieving Distributed Intelligence by Converting Smart Meters into A Grid Edge Computing Platform
4. Case Study 3—Open Source Platforms And Software-defined Architecture Drive Distributed Grid Edge Intelligence

1. Conclusion
2. Legal Disclaimer

1. Additional Sources of Information on Smart Plants
2. Partial List of Companies Interviewed

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