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市场调查报告书

AI (人工智能) 和高级分析的全球市场:发电资产分析,电网运用分析,电网资产分析,客户营运分析,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 商品编码 898094
出版日期 内容资讯 英文 68 Pages; 19 Tables, Charts & Figures
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AI (人工智能) 和高级分析的全球市场:发电资产分析,电网运用分析,电网资产分析,客户营运分析,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日内容资讯: 英文 68 Pages; 19 Tables, Charts & Figures
简介

本报告涵括AI (人工智能) 高级分析的全球市场,尤其以能源云端中的电力公司经营者,能源服务供应商,商业大楼所有者/营运者,以及城市/地方政府为焦点,透过对相关领域的AI和高级分析的市场成长促进因素,各地区市场趋势,以及技术性课题等的广泛调查,来进行市场现状分析以及未来预测。

第1章 摘要整理

第2章 市场相关资料

  • AI与高级分析定义
  • AI和分析的市场成长促进因素
    • 商务促进因素
    • 技术性促进因素
    • 未来的经营模式的革新

第3章 技术课题

  • AI的开发不再是线性发展
  • 机器学习
  • AI规划
  • 认识自动化
  • NLP (自然语言处理)
  • 语音分析,语音辨识、文字转录,文本宣读
  • 人工视觉及视讯分析
  • 人工移情
  • 能源云端的使用案例
  • 公共事业产业规模的发电事业
    • 机器学习
    • 人工视觉
  • 输配电网
  • 能源供给/能源服务
  • 智慧家庭
  • 智慧建筑
  • 智慧城市
  • 交通运输

第4章 主要企业

  • 企业、分析、供应商企业
    • Teradata
    • Nokia
    • IBM
    • eSmart Systems
    • Oracle
    • SAS
    • Schneider Electric
    • OSIsoft
    • SparkCognition
    • SAP
    • TROVE
    • Itron
    • Grid4C
    • C3.ai
    • GE
    • ABB
  • 数位助手、供应商企业
    • Amazon的Alexa
    • Apple的Siri
    • Google Assistant
    • Microsoft的Cortana
  • 大楼管理分析、供应商企业
    • Demand Logic
    • EnergyAi
  • 自动驾驶车
    • Amazon
    • Tesla
    • 丰田汽车株式会社/日野汽车株式会社
    • Waymo

第5章 市场预测

  • 全球市场概要
  • 北美
  • 欧洲
  • 亚太地区
  • 南美
  • 中东、非洲

第6章 建议

  • AI不是万能药,今后也不成
  • 关联的各种技术是必须的
  • 善加应对员工对AI的反感
  • 分析只是广泛策略中的一部分
  • 资料管理
  • 偏见

第7章 简称一览

第8章 目录

第9章 附表、附图一览

第10章 调查范围,资料来源,及调查手法,注记

目录
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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