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세계의 인공지능(AI)과 고급분석(Advanced Analytics) 시장 : 발전 자산 분석, 그리드 운영 분석, 그리드 자산 분석, 고객 오퍼레이션 분석, 수요측 분석, 스마트 시티 분석

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

리서치사 Navigant Research
발행일 2019년 07월 상품 코드 898094
페이지 정보 영문 68 Pages; 19 Tables, Charts & Figures
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US $ 4,950 ₩ 5,942,000 PDF & Excel by E-mail (Basic License)
US $ 7,425 ₩ 8,913,000 PDF & Excel by E-mail (Enterprise License)


세계의 인공지능(AI)과 고급분석(Advanced Analytics) 시장 : 발전 자산 분석, 그리드 운영 분석, 그리드 자산 분석, 고객 오퍼레이션 분석, 수요측 분석, 스마트 시티 분석 AI and Advanced Analytics Overview: Generation Asset Analytics, Grid Operations Analytics, Grid Asset Analytics, Customer Operations Analytics, Demand Side Analytics, and Smart City Analytics
발행일 : 2019년 07월 페이지 정보 : 영문 68 Pages; 19 Tables, Charts & Figures

세계의 AI(인공지능)와 고급분석(Advanced Analytics) 시장을 분석했으며, 특히 에너지 클라우드내 전력 유틸리티 사업자, 에너지 서비스 프로바이더, 상업 빌딩 소유주/운영자, 도시/지방 정부에 초점을 맞추고 관련 분야의 AI와 고급분석 시장의 성장 촉진요인, 지역별 시장 동향, 기술적 과제 등 폭넓은 조사를 기반으로 현행 시장 분석과 예측을 정리하여 전해드립니다.

제1장 개요

제2장 시장 이슈

  • AI와 고급분석의 정의
  • AI와 분석 시장 성장 촉진요인
    • 비지니스 촉진요인
    • 기술적 촉진요인
    • 미래의 비지니스 모델 혁신

제3장 기술적 과제

  • AI의 개발은 더 이상 일직선 방향이 아니다.
  • 머신러닝
  • AI 플래닝
  • 인지 자동화
  • NLP(자연언어처리)
  • 음성 분석, STT(Speech to Text), 문자 음성 변환
  • 인공 시각 및 비디오 분석
  • 인공 공감
  • 에너지 클라우드의 사용 사례
  • 유틸리티 산업 규모의 발전 사업
    • 머신러닝
    • 인공 시각
  • 송배전망
  • 에너지 공급/에너지 서비스
  • 스마트홈
  • 스마트 빌딩
  • 스마트 시티
  • 교통 운송

제4장 주요 기업

  • 기업 분석 벤더
    • Teradata
    • Nokia
    • IBM
    • eSmart Systems
    • Oracle
    • SAS
    • Schneider Electric
    • OSIsoft
    • SparkCognition
    • SAP
    • TROVE
    • Itron
    • Grid4C
    • C3.ai
    • GE
    • ABB
  • 디지털 비서 벤더
    • Amazon의 Alexa
    • Apple의 Siri
    • Google Assistant
    • Microsoft의 Cortana
  • 빌딩 관리 분석 벤더
    • Demand Logic
    • EnergyAi
  • 자율주행차
    • Amazon
    • Tesla
    • Toyota/Hino Motors
    • Waymo

제5장 시장 예측

  • 세계 시장 개요
  • 북미
  • 유럽
  • 아시아태평양
  • 라틴아메리카
  • 중동·아프리카

제6장 제안

  • AI는 만능약이 아니며, 앞으로도 그렇지 않다.
  • 관련된 각종 스킬이 필요하다.
  • 직원의 AI에 대한 반감에 잘 대처할 것
  • 애널리틱스는 광범위한 전략의 일부에 불과하다.
  • 데이터 관리
  • 편견

제7장 약어 리스트

제8장 목차

제9장 부표·도표

제10장 조사 범위, 데이터 소스 및 조사 방법, 주석

KSA 19.08.08

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

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

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