3Q 2019

AI and Advanced Analytics Overview

Generation Asset Analytics, Grid Operations Analytics, Grid Asset Analytics, Customer Operations Analytics, Demand Side Analytics, and Smart City Analytics

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.

Pages 68
Tables | Charts | Figures 19
  • 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?
  • AI and analytics vendors
  • Generation asset owners
  • Grid asset owners
  • Electricity suppliers
  • Smart home vendors
  • Smart building vendors
  • Smart cities
  • Investor community

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

  • 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
  • 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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