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市場調查報告書

AI (人工智能) 和高級分析的全球市場:發電資產分析,電網運用分析,電網資產分析,客戶營運分析,需求方面分析,智慧城市分析

AI and Advanced Analytics Overview: Generation Asset Analytics, Grid Operations Analytics, Grid Asset Analytics, Customer Operations Analytics, Demand Side Analytics, and Smart City Analytics

出版商 Navigant Research 商品編碼 898094
出版日期 內容資訊 英文 68 Pages; 19 Tables, Charts & Figures
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AI (人工智能) 和高級分析的全球市場:發電資產分析,電網運用分析,電網資產分析,客戶營運分析,需求方面分析,智慧城市分析 AI and Advanced Analytics Overview: Generation Asset Analytics, Grid Operations Analytics, Grid Asset Analytics, Customer Operations Analytics, Demand Side Analytics, and Smart City Analytics
出版日期: 2019年07月30日內容資訊: 英文 68 Pages; 19 Tables, Charts & Figures
簡介

本報告涵括AI (人工智能) 高級分析的全球市場,尤其以能源雲端中的電力公司經營者,能源服務供應商,商業大樓所有者/營運者,以及城市/地方政府為焦點,透過對相關領域的AI和高級分析的市場成長促進因素,各地區市場趨勢,以及技術性課題等的廣泛調查,來進行市場現狀分析以及未來預測。

第1章 摘要整理

第2章 市場相關資料

  • AI與高級分析定義
  • AI和分析的市場成長促進因素
    • 商務促進因素
    • 技術性促進因素
    • 未來的經營模式的革新

第3章 技術課題

  • AI的開發不再是線性發展
  • 機器學習
  • AI規劃
  • 認識自動化
  • NLP (自然語言處理)
  • 語音分析,語音辨識、文字轉錄,文本宣讀
  • 人工視覺及視訊分析
  • 人工移情
  • 能源雲端的使用案例
  • 公共事業產業規模的發電事業
    • 機器學習
    • 人工視覺
  • 輸配電網
  • 能源供給/能源服務
  • 智慧家庭
  • 智慧建築
  • 智慧城市
  • 交通運輸

第4章 主要企業

  • 企業、分析、供應商企業
    • Teradata
    • Nokia
    • IBM
    • eSmart Systems
    • Oracle
    • SAS
    • Schneider Electric
    • OSIsoft
    • SparkCognition
    • SAP
    • TROVE
    • Itron
    • Grid4C
    • C3.ai
    • GE
    • ABB
  • 數位助手、供應商企業
    • Amazon的Alexa
    • Apple的Siri
    • Google Assistant
    • Microsoft的Cortana
  • 大樓管理分析、供應商企業
    • Demand Logic
    • EnergyAi
  • 自動駕駛車
    • Amazon
    • Tesla
    • 豐田汽車株式會社/日野汽車株式會社
    • Waymo

第5章 市場預測

  • 全球市場概要
  • 北美
  • 歐洲
  • 亞太地區
  • 南美
  • 中東、非洲

第6章 建議

  • AI不是萬能藥,今後也不成
  • 關聯的各種技術是必須的
  • 善加應對員工對AI的反感
  • 分析只是廣泛策略中的一部分
  • 資料管理
  • 偏見

第7章 簡稱一覽

第8章 目錄

第9章 附表、附圖一覽

第10章 調查範圍,資料來源,及調查手法,註記

目錄
Product Code: MO-AIAA-19

Artificial intelligence (AI) helps organizations work smarter. Each new deployed Internet of Things (IoT) device improves an organization's visibility into business or customer operations. Each new development in analytics allows companies to gain deeper insights from data, opening new market opportunities or improving existing business processes. Each new development in data management allows companies to access more complex datasets and gain insights more quickly, and increases competitive edge.

Many industries are experiencing the same issues: pressure to improve profits through cost-cutting, increased competition, digitization of business processes created by the mass deployment of connected sensors and control equipment, new business model creation, and more. AI-along with advancements in computer processing, cloud, and edge computing-can help enterprises address these issues. There are many applications of AI across the Energy Cloud, including predictive maintenance in wind and solar farms, vegetation management in grid operations, optimization of customers' distributed energy resources (DER) investments, digital assistants to control smart homes, and improved efficiency of transportation systems.

This Navigant Research report provides forecasts for enterprise spend on analytics within the Energy Cloud. The study focuses on electricity utilities, energy service providers, commercial building owners and operators, and cities/local governments. Global market forecasts, segmented by analytics type and region, extend through 2028. Asia Pacific is expected to become the largest region by 2026. This report also identifies key industry players in several applications.

Key Questions Addressed:

  • What are artificial intelligence (AI) and advanced analytics?
  • How is AI applied in the Energy Cloud?
  • What are the benefits of using analytics?
  • What are the different value propositions, market drivers, and barriers for AI?
  • How is the analytics market expected to grow over the next decade?
  • How will this growth vary by region and technology?
  • Who are the key players in the analytics market?

Who Needs This Report:

  • AI and analytics vendors
  • Generation asset owners
  • Grid asset owners
  • Electricity suppliers
  • Smart home vendors
  • Smart building vendors
  • Smart cities
  • Investor community

Table of Contents

1. Executive Summary

2. Market Issues

  • 2.1. Artificial Intelligence and Advanced Analytics Defined
  • 2.2. Drivers for AI and Analytics
    • 2.2.1. Business Drivers
    • 2.2.2. Technological Drivers
    • 2.2.3. Future Business Model Innovation

3. Technology Issues

  • 3.1. AI Development Is No Longer a Linear Progression
  • 3.2. Machine Learning
  • 3.3. AI Planning
  • 3.4. Cognitive Automation
  • 3.5. NLP
  • 3.6. Voice Analytics, Speech to Text, and Text to Speech
  • 3.7. Artificial Vision and Video Analytics
  • 3.8. Artificial Empathy
  • 3.9. Use Cases in the Energy Cloud
  • 3.10. Utility Scale Generation
    • 3.10.1. Machine Learning
    • 3.10.2. Artificial Vision
  • 3.11. T&D Networks
    • 3.11.1. Machine Learning
    • 3.11.2. AI Planning
    • 3.11.3. Artificial Vision
  • 3.12. Energy Supply/Energy Services
    • 3.12.1. Machine Learning
    • 3.12.2. RPA
    • 3.12.3. NLP
    • 3.12.4. Artificial Empathy
  • 3.13. Smart Home
    • 3.13.1. Machine Learning
    • 3.13.2. NLP and Voice Analytics
    • 3.13.3. Artificial Vision
    • 3.13.4. Artificial Empathy
  • 3.14. Smart Buildings
    • 3.14.1. Machine Learning
  • 3.15. Smart Cities
    • 3.15.1. Machine Learning
    • 3.15.2. AI Planning
  • 3.16. Transport
    • 3.16.1. Machine Learning
    • 3.16.2. Voice Analytics
    • 3.16.3. Artificial Vision

4. Key Industry Players

  • 4.1. Enterprise Analytics Vendors
    • 4.1.1. Teradata
    • 4.1.2. Nokia
    • 4.1.3. IBM
    • 4.1.4. eSmart Systems
    • 4.1.5. Oracle
    • 4.1.6. SAS
    • 4.1.7. Schneider Electric
    • 4.1.8. OSIsoft
    • 4.1.9. SparkCognition
    • 4.1.10. SAP
    • 4.1.11. TROVE
    • 4.1.12. Itron
    • 4.1.13. Grid4C
    • 4.1.14. C3.ai
    • 4.1.15. GE
    • 4.1.16. ABB
  • 4.2. Digital Assistant Vendors
    • 4.2.1. Amazon's Alexa
    • 4.2.2. Apple's Siri
    • 4.2.3. Google Assistant
    • 4.2.4. Microsoft's Cortana
  • 4.3. Building Management Analytics Vendors
    • 4.3.1. Demand Logic
    • 4.3.2. EnergyAi
  • 4.4. Automated Vehicles
    • 4.4.1. Amazon
    • 4.4.2. Tesla
    • 4.4.3. Toyota/Hino Motors
    • 4.4.4. Waymo

5. Market Forecasts

  • 5.1. Global Overview
  • 5.2. North America
  • 5.3. Europe
  • 5.4. Asia Pacific
  • 5.5. Latin America
  • 5.6. The Middle East & Africa

6. Recommendations

  • 6.1. AI Is Not, and Never Will Be, a Panacea
  • 6.2. Relevant Skills Are Needed
  • 6.3. Manage Employees' Antipathy to AI
  • 6.4. Analytics Is Only Part of a Wider Strategy
  • 6.5. Data Management
  • 6.6. Bias

7. Acronym and Abbreviation List

8. Table of Contents

9. Table of Charts and Figures

10. Scope of Study, Sources and Methodology, Notes

List of Charts and Figures

  • Analytics Revenue by Region, World Markets: 2019-2028
  • Analytics Revenue by Segment, World Markets: 2019-2028
  • Analytics Revenue by Segment, North America: 2019-2028
  • Analytics Revenue by Segment, Europe: 2019-2028
  • Analytics Revenue by Segment, Asia Pacific: 2019-2028
  • Analytics Revenue by Segment, Latin America: 2019-2028
  • Analytics Revenue by Segment, Middle East & Africa: 2019-2028
  • AI Permeates the Energy Cloud
  • Linear Evolution of Analytics and Branches of AI
  • Cognitive Processes of AI
  • The Chihuahua or Muffin Test
  • Heatmap of AI types in the Energy Cloud

List of Tables

  • Analytics Revenue by Region, World Markets: 2019-2028
  • Analytics Revenue by Segment, World Markets: 2019-2028
  • Analytics Revenue by Segment, North America: 2019-2028
  • Analytics Revenue by Segment, Europe: 2019-2028
  • Analytics Revenue by Segment, Asia Pacific: 2019-2028
  • Analytics Revenue by Segment, Latin America: 2019-2028
  • Analytics Revenue by Segment, Middle East & Africa: 2019-2028
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