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 IntelligenceÂ Table Of Contents 1 OPPORTUNITIES IN EDGE INTELLIGENCE
Executive Summary 1. Future of Intelligence in the Utility Industry 2. Grid Edge IntelligenceâPreparing for the Future 3. Executive SummaryâKey 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. ChallengeâTypical Edge Intelligence-related Concerns 2. ChallengeâTypical Edge Intelligence-related Concerns 3. DriverâSiloed to De-siloed Business Structure 4. DriverâRise of Operational Analytics 5. DriverâFocus on Maximizing Capabilities
Global Market Outlook 1. The Era of Edge Intelligence (2015-2020) 2. Rise of AnalyticsâFrom Data to Intelligence 3. Edge IntelligenceâGlobal Trends 4. Edge IntelligenceâGlobal Trends 5. Edge IntelligenceâGlobal Trends 6. Current and Future Outlook 7. Edge IntelligenceâStakeholders
Case Study 1. Case Study 1âGrid Edge Implementation: Duke Energy 2. Case Study 1âGrid Edge Implementation: Duke Energy 3. Case Study 2âAchieving Distributed Intelligence by Converting Smart Meters into A Grid Edge Computing Platform 4. Case Study 3âOpen Source Platforms And Software-defined Architecture Drive Distributed Grid Edge Intelligence
Conclusion 1. Conclusion 2. Legal Disclaimer
Appendix 1. Additional Sources of Information on Smart Plants 2. Partial List of Companies Interviewed