邊緣情報的市場機會:實現物連網的互相連接

邊緣情報為複合商業智慧與自動化之物，可識別大量的資料、合成、接近資料收集點作出決策。用途中有特定收益結構內的資料收集，分析，通訊，及達成無前例層級的可靠性和效率性的即時決策等。以前，監控系統收集資料，對中央的控制系統送出警報，生成獨立的報告展示給用戶。目前邊緣情報結構中，在資料集積點附近，設計成在中央系統以無敵擔任電網決策的主機角色。

本報告提供邊緣情報的影響需求的主要促進要素和課題分析。

摘要整理

• 公共產業的情報的今後
• 電網、邊緣情報─面向未來
• 摘要整理─主要調查結果

邊緣情報定義

• 關於邊緣情報
• 電網決策的演進
• 智慧電網對邊緣情報的重要性

課題和促進要素

• 課題─邊緣情報相關典型性的問題
• 促進要素─從Driver-Siloed到De-siloed的商務結構
• 促進要素─運營分析的登場
• 促進要素─對容量最大化的關注

全球市場的展望

• 邊緣情報的時代 (2015∼2020年)
• 分析的登場─從資料到情報
• 邊緣情報─全球的趨勢
• 現在和未來的展望
• 邊緣情報─相關利益者

案例研究

• 案例研究1─電網邊的實施:Duke Energy
• 案例研究2─轉換至智慧電表的網格邊緣、運算、平台的分配型情報的達成
• 案例研究3─開放原始碼、平台和軟體定義型結構活化的分配型網格邊緣情報

結論

• 結論
• 法律上的免責聲明

附屬資料

關於Frost＆Sullivan

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.

Table of Contents

1. OPPORTUNITIES IN EDGE INTELLIGENCE

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 (continued)
• 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 (continued)
• 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 (continued)
• 5. Edge Intelligence-Global Trends (continued)
• 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 (continued)
• 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

Conclusion

• 1. Conclusion
• 2. Legal Disclaimer

Appendix

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

The Frost & Sullivan Story

• 1. The Frost & Sullivan Story
• 2. Value Proposition: Future of Your Company & Career
• 3. Global Perspective
• 4. Industry Convergence
• 5. 360° Research Perspective
• 6. Implementation Excellence
• 7. Our Blue Ocean Strategy