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Artificial Intelligence in Energy Market: Global Opportunity and Trend Analysis, 2020-2030

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    September 2020

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Overview

Our estimates suggest that the global AI in energy market is likely to be worth USD 2.1 billion in 2020, and this value is projected to reach USD 30.8 billion by 2030, growing at a CAGR of 30.6%. AI is the ability of a system to think like humans, discover the meaning, and execute the tasks. The term was coined in 1955 by John McCarthy and has gained wide popularity till date. Over the years, AI-driven technologies have continued to evolve rapidly, with several industries increasingly deploying such solutions across key aspects of value chain. The growing demand for energy, coupled to the need to identify sustainable solutions, is presently encouraging the adoption of AI based products in the energy sector. At present, AI is used in the energy industry for electricity trading, smart grids, or sector coupling of electricity, heat and transport. In addition, this technology is assisting the energy sector to become more efficient and secure, by analyzing and evaluating huge volumes of data. Specifically, in residential and commercial sectors, AI is being used in energy management systems, thus, helping in enabling reduction of electricity bills. Therefore, growing demand for energy efficient solutions that enable reduction in operational costs, along with the need to reduce global carbon footprint are some of the factors presently driving the growth within this market. However, lack of awareness and outdated IT infrastructure, coupled to cybersecurity concerns are believed to acts as hindrances.

In addition, the recent COVID-19 pandemic had an immediate negative impact on the global oil and gas and energy sector. The sudden dip in the demand for electricity and fall in oil prices are also a cause of concern. As per industry experts and veterans, most companies have kept their ongoing projects on hold for an indefinite period. However, it is expected that the industry will recover by the end of 2020 or early 2021. Further, the market for AI solutions intended for renewable management and demand management are anticipated to witness significant growth in the foreseen future. Moreover, it is predicted that hardware market will grow rapidly in the coming years owing to the rise in demand for high computational computing systems.

Based on geography, the global AI in Energy market has been segmented into four regions, namely North America, Europe, Asia-Pacific and Rest of the world.  As can be observed in the figure, currently, Asia-Pacific is the fastest growing segment of this market. This can be attributed to the high adoption of AI solutions to improve operational efficiencies, as well as upgrade existing infrastructure, across countries, such as India, China and Japan.

It is worth mentioning that partnerships emerged as the key business strategy adopted by players engaged in this domain in order to promote future growth. Examples of recent partnership agreements include (in reverse chronological order) those signed between ExxonMobil and Princeton University (July 2020), and Xcel Energy with eSmart and EDM (January 2020). The former agreement was inked to develop novel technologies that can help meet the dual challenge of growing energy demand, while also reducing emissions. The latter deal was signed for the development of AI solutions for transmission grid inspection.

Scope of the Report

The Artificial Intelligence in Energy Market: Global Opportunity and Trend Analysis, 2020-2030 report features an extensive study of the current landscape of industry players engaged in the development of AI based solutions for the energy sector. Amongst other elements, the report features:

  • A detailed, quantitative analysis of the current and future market opportunity, over the period 2020-2030.
  • A qualitative analysis of key market trends, growth drivers, constraints and upcoming opportunity areas within the AI in energy market 
  • Informed insights related to the current competitive market landscape.

Contents

Table Of Contents

1. PREFACE
1.1. Scope of the Report
1.2. Research Methodology
1.3. Market Segmentation

2. EXECUTIVE SUMMARY
2.1. Key Insights

3. MARKET OVERVIEW          
3.1. Chapter Overview
3.1.1. AI in Energy, Base Case Scenario
3.1.2. AI in Energy, Conservative Scenario
3.1.3. AI in Energy, Optimistic Scenario

3.2. Market Dynamics
3.2.1. Growth Drivers
3.2.1.1. Growing Need for Automation
3.2.1.2. Significant Increase in The Venture Capital Funds
3.2.2. Market Restraints
3.2.2.1. Low of Awareness and Outdated IT Infrastructure
3.2.2.2. Cybersecurity Concerns
3.2.3. Opportunities
3.2.3.1. Predictive Analytics to Reduce Downtime

4. AI IN ENERGY MARKET: DISTRIBUTION BY TYPE OF COMPONENTS
4.1. Market Overview
4.1.1. Market Size and Analysis
4.2. Hardware
4.2.1. Key Insights
4.2.2. Market Size and Analysis
4.3. Software
4.3.1. Key Insights
4.3.2. Market Size and Analysis
4.4. Services
4.4.1. Key Insights
4.4.2. Market Size and Analysis

5. AI IN ENERGY MARKET: DISTRIBUTION BY TYPE OF APPLICATION
5.1. Market Overview
5.1.1. Market Size and Analysis
5.2. Renewable Management
5.2.1. Key Insights
5.2.2. Market Size and Analysis
5.3. Demand Management
5.3.1. Key Insights
5.3.2. Market Size and Analysis
5.4. Infrastructure Management
5.4.1. Key Insights
5.4.2. Market Size and Analysis

6. AI IN ENERGY MARKET: DISTRIBUTION BY GEOGRAPHY
6.1. Market Overview
6.1.1. Market Size and Analysis
6.2. North America
6.2.1. Key Market Trends and Growth Opportunities
6.2.2. Market Size and Analysis
6.2.3. United States
6.2.3.1. Market Size and Analysis
6.2.4. Canada
6.2.4.1. Market Size and Analysis
6.3. Europe
6.3.1. Key Market Trends and Growth Opportunities
6.3.2. Market Size and Analysis
6.3.3. The UK
6.3.3.1. Market Size and Analysis
6.3.4. Germany
6.3.4.1. Market Size and Analysis
6.3.5. France
6.3.5.1. Market Size and Analysis
6.3.6. Rest of Europe
6.3.6.1. Market Size and Analysis
6.4. Asia-Pacific
6.4.1. Key Market Trends and Growth Opportunities
6.4.2. Market Size and Analysis
6.4. India
6.4.3.1. Market Size and Analysis
6.4.4. China
6.4.4.1. Market Size and Analysis
6.4.5. Japan
6.4.5.1. Market Size and Analysis
6.4.6. Rest of APAC
6.4.6.1. Market Size and Analysis
6.5. RoW
6.5.1. Key Market Trends and Growth Opportunities
6.5.2. Market Size and Analysis
6.5.3. Middle East
6.5.3.1. Market Size and Analysis
6.5.4. Africa
6.5.4.1. Market Size and Analysis
6.5.5. Latin America
6.5.5.1. Market Size and Analysis

7. COMPANY PROFILES
7.1. Microsoft
7.1.1. Company Overview
7.1.2. Financial Overview
7.1.3. Key Developments and Strategies

7.2. Oracle
7.2.1. Company Overview
7.2.2. Financial Overview
7.2.3. Key Developments and Strategies

7.3. IBM
7.3.1. Company Overview
7.3.2. Financial Overview
7.3.3. Key Developments and Strategies

7.4. Siemens
7.4.1. Company Overview
7.4.2. Financial Overview
7.4.3. Key Developments and Strategies

7.5. GE Digital
7.5.1. Company Overview
7.5.2. Financial Overview
7.5.3. Key Developments and Strategies

7.6. ABB
7.6.1. Company Overview
7.6.2. Financial Overview
7.6.3. Key Developments and Strategies

7.7. Energsoft
7.7.1. Company Overview
7.7.2. Financial Overview
7.7.3. Key Developments and Strategies

7.8. Upside Energy
7.8.1. Company Overview
7.8.2. Financial Overview
7.8.3. Key Developments and Strategies

7.9. Xcel Energy
7.9.1. Company Overview
7.9.2. Financial Overview
7.9.3. Key Developments and Strategies

7.10. C3.AI
7.10.1. Company Overview
7.10.2. Financial Overview
7.10.3. Key Developments and Strategies

8. COMPETITIVE LANDSCAPE
8.1. Key Initiatives Analysis,2017-2019

8.1.1. Royal Dutch Shell
8.1.1.1. Company Overview
8.1.1.2. Key Initiatives

8.1.2. ExxonMobil
8.1.2.1. Company Overview
8.1.2.2. Key Initiatives

8.1.3. OJSC Gazprom
8.1.3.1. Company Overview
8.1.3.2. Key Initiatives

8.1.4. Equinor
8.1.4.1. Company Overview
8.1.4.2. Key Initiatives

8.1.5. Chevron
8.1.5.1. Company Overview
8.1.5.2. Key Initiatives

8.2. Patent Analysis

8.3. Startup Analysis

9. CXO PERSPECTIVE          
9.1. Primary Insights

List Of Figures

Figure 3.1 Research Methodology: AI spending in the Oil and Gas industry
Figure 3.2 AI in Energy Market, Base Case Scenario
Figure 3.3 AI in Energy Market, Conservative Scenario
Figure 3.4 AI in Energy Market, Optimistic Scenario
Figure 3.5 Cyberattack Incidents,2014-2019
Figure 4.1 AI in Energy Market, Distribution by Components: Key Insights
Figure 4.2 AI in Energy Market, Distribution by Hardware, 2019-2030 (USD Billion)
Figure 4.3 AI in Energy Market, Distribution by Software, 2019-2030 (USD Billion)
Figure 4.4 AI in Energy Market, Distribution by Services, 2019-2030 (USD Billion)
Figure 5.1 AI in Energy Market, Distribution by Application: Key Insights
Figure 5.2 AI in Energy Market, Distribution by Renewable Management, 2019-2030 (USD Billion)
Figure 5.3 AI in Energy Market, Distribution by Demand Management, 2019-2030 (USD Billion)
Figure 5.4 AI in Energy Market, Distribution by Infrastructure Management, 2019-2030 (USD Billion)
Figure 6.1 AI in Energy Market: Key Insights, By Region (2016-2020)
Figure 6.2 North America AI in Energy Market, 2019-2030 (USD Billion)
Figure 6.3 US AI in Energy Market, 2019-2030 (USD Billion)
Figure 6.4 Canada AI in Energy Market, 2019-2030 (USD Billion)
Figure 6.5 Europe AI in Energy Market, 2019-2030 (USD Billion)
Figure 6.6 UK AI in Energy Market, 2019-2030 (USD Billion)
Figure 6.7 Germany AI in Energy Market, 2019-2030 (USD Billion)
Figure 6.8 France AI in Energy Market, 2019-2030 (USD Billion)
Figure 6.9 Rest of Europe AI in Energy Market, 2019-2030 (USD Billion)
Figure 6.10 Asia Pacific AI in Energy Market, 2019-2030 (USD Billion)
Figure 6.11 China AI in Energy Market, 2019-2030 (USD Billion)
Figure 6.12 Japan AI in Energy Market, 2019-2030 (USD Billion)
Figure 6.13 Australia AI in Energy Market, 2019-2030 (USD Billion)
Figure 6.14 Rest of the APAC AI in Energy Market,2019-2030 (USD Billion)
Figure 6.15 Rest of the World AI in Energy Market,2019-2030 (USD Billion)
Figure 6.16 Middle East AI in Energy Market, 2019-2030 (USD Billion)
Figure 6.17 Latin America in Energy Market, 2019-2030 (USD Billion)
Figure 6.18 Africa AI in Energy Market, 2019-2030 (USD Billion)
Figure 7.1 Microsoft: Revenues, 2017-2019 (USD Billion)
Figure 7.2 Microsoft: Revenues by Operating Segment, 2018-2019 (%)
Figure 7.3 Microsoft: Revenues by Region, 2019-2018 (%)
Figure 7.4 Oracle: Revenues, 2017-2019 (USD Billion)
Figure 7.5 Oracle: Revenues by Operating Segment, 2018-2019 (%)
Figure 7.6 Oracle: Revenues by Region, 2019-2018 (%)
Figure 7.7 IBM: Revenues, 2017-2019 (USD Billion)
Figure 7.8 IBM: Revenues by Operating Segment, 2018-2019 (%)
Figure 7.9 IBM: Revenues by Region, 2019-2018 (%)
Figure 7.10 Siemens: Revenues, 2017-2019 (USD Billion)
Figure 7.11 Siemens: Revenues by Operating Segment, 2018-2019 (%)
Figure 7.12 Siemens: Revenues by Region, 2019-2018 (%)
Figure 7.13 ABB: Revenues, 2017-2019 (USD Billion)
Figure 7.14 ABB: Revenues by Operating Segment, 2018-2019 (%)
Figure 7.15 ABB: Revenues by Region, 2019-2018 (%)
Figure 7.16 Xcel Energy, 2017-2019 (EUR Billion)
Figure 7.17 Xcel Energy: Revenues by Operating Segment, 2018-2019 (%)
Figure 8.1 Initiatives Analysis, 2017-2020 (%)
Figure 8.2 Patent Analysis, based on countries
Figure 8.3 Patent Analysis, based on companies
Figure 8.4 AI start up Analysis

List Of Tables

Table 2.1 AI in Energy Market: Summary of Revenue Distribution across Key Market Segments
Table 2.2 AI in Energy Market: Summary of Revenue Distribution across Key Geographies
Table 4.1 AI in Energy Market, Distribution by Type of Components, 2019-2030 (USD Billion)
Table 4.2 AI in Energy Market, Distribution by Hardware, by Geography, 2019-2030 (USD Billion)
Table 4.3. AI in Energy Market Distribution by Software, by Geography, 2019-2030(USD Billion)
Table 4.4 AI in Energy Market Distribution by Services, by Geography, 2019-2030 (USD Billion)
Table 5.1 AI in Energy Market, Distribution By Type Of Application, 2019-2030 (USD Billion)
Table 5.2 AI in Energy Market, Distribution by Renewable Management, by Region, 2019-2030 (USD Billion)
Table 5.3 AI in Energy Market, Distribution by Demand Management, by Region, 2019-2030 (USD Billion)
Table 5.4 AI in Energy Market, Distribution by Demand Management, by Region, 2019-2030 (USD Billion)
Table 6.1 AI in Energy Market, Distribution by Region, 2019-2030 (USD Billion)
Table 6.2 AI in Energy Market, Distribution by Type of Components, 2019-2030 (USD Billion)
Table 6.3 AI in Energy Market, Distribution by Type of Application, 2019-2030 (USD Billion)
Table 6.4 North America AI in Energy Market, by Countries,2019-2030 (USD Billion)
Table 6.5 North America AI in Energy Market, Distribution by Type of Components,2019- 2030 (USD Billion)
Table 6.6 North America AI in Energy Market, Distribution by Type of Applications, 2019- 2030 (USD Billion)
Table 6.7 US AI in Energy Market Distribution by Type of Components,2019-2030 (USD Billion)
Table 6.8 Canada AI in Energy Market Distribution by Type of Components,2019-2030 (USD Billion)
Table 6.9 Europe AI in Energy Market, by Countries,2019-2030 (USD Billion)
Table 6.10 Europe AI in Energy Market, Distribution by Type of Components,2019-2030 (USD Billion)
Table 6.11 Europe AI in Energy Market, Distribution by Type of Applications, 2019-2030 (USD Billion)
Table 6.12 UK AI in Energy Market, Distribution by Type of Components,2019-2030 (USD Billion)
Table 6.13 Germany AI in Energy Market, Distribution by Type of Components,2019-2030 (USD Billion)
Table 6.14 France AI in Energy Market, Distribution by Type of Components,2019-2030 (USD Billion)
Table 6.15 Rest of Europe AI in Energy Market, Distribution by Type of Components,2019-2030 (USD Billion)
Table 6.16 Asia Pacific AI in Energy Market, by Countries,2019-2030 (USD Billion)
Table 6.17 Asia Pacific AI in Energy Market, Distribution by Type of Components,2019-2030 (USD Billion)
Table 6.18 Asia Pacific AI in Energy Market, Distribution by Type of Applications, 2019-2030 (USD Billion)
Table 6.19 China AI in Energy Market, Distribution by Type of Components,2019-2030 (USD Billion)
Table 6.20 Japan AI in Energy Market, Distribution by Type of Components,2019-2030 (USD Billion)
Table 6.21 Australia AI in Energy Market, Distribution by Type of Components,2019-2030 (USD Billion)
Table 6.22 Rest of Asia Pacific AI in Energy Market, Distribution by Type of Components,2019-2030 (USD Billion)
Table 6.23 Rest of the World AI in Energy Market, by Countries,2019-2030 (USD Billion)
Table 6.24 Rest of the World AI in Energy Market, Distribution by Type of Components,2019-2030 (USD Billion)
Table 6.25 Rest of the World AI in Energy Market, Distribution by Type of Applications, 2019-2030 (USD Billion)
Table 6.26 Middle East in Energy Market, Distribution by Type of Components,2019-2030 (USD Billion)
Table 6.27 Latin America in Energy Market, Distribution by Type of Components,2019-2030 (USD Billion)
Table 6.28 Africa AI in Energy Market, Distribution by Type of Components,2019-2030 (USD Billion)
Table 7.1 Microsoft: Company Snapshot
Table 7.2 Microsoft: Key Developments and Strategies, 2018-2020
Table 7.3 Oracle: Company Snapshot
Table 7.4 Oracle: Key Development and Strategies, 2019-2020
Table 7.5 IBM: Company Snapshot
Table 7.6 IBM: Key Development and Strategies, 2018-2019
Table 7.7 Siemens: Company Snapshot
Table 7.8 Siemens: Key Development and Strategies, 2019-2020
Table 7.9 GE Digital: Company Snapshot
Table 7.10 GE Digital: Key Development and Strategies, 2019
Table 7.11 ABB: Company Snapshot
Table 7.12 ABB: Key Development and Strategies, 2018-2020
Table 7.13 Energsoft: Company Snapshot
Table 7.14 Energsoft: Funding Overview
Table 7.15 Upside Energy: Company Snapshot
Table 7.16 Upside Energy: Funding Overview
Table 7.17 Upside Energy: Key Development and Strategies, 2018
Table 7.18 Xcel Energy: Company Snapshot
Table 7.10 Xcel Energy: Key Development and Strategies, 2020
Table 7.11 C3.AI: Company Snapshot
Table 7.12 C3.AI: Key Development and Strategies, 2019-2020
Table 8.1 Royal Dutch Shell: Company Snapshot
Table 8.2 Royal Dutch Shell: Key Initiatives and Strategies,2017-2019
Table 8.3 ExxonMobil: Company Snapshot
Table 8.4 ExxonMobil: Key Initiatives and Strategies,2019-2020
Table 8.5 OJSC Gazprom: Company Snapshot
Table 8.6 OJSC Gazprom: Key Initiatives and Strategies,2019-2020
Table 8.7 Equinor: Company Snapshot
Table 8.8 Equinor: Key Initiatives and Strategies,2019-2020
Table 8.9 Chevron: Company Snapshot
Table 8.10 Chevron: Key Initiatives and Strategies,2019-2020

Segmentation

Distribution by Type of Component

  • Hardware
  • Software 
  • Services

Distribution by Type of Application

  • Renewable Management
  • Demand Management
  • Infrastructure Management

Distribution by Geography

  • North America
  • US
  • Canada
  • Europe
  • UK
  • Germany
  • France
  • Rest of Europe
  • Asia-Pacific
  • India
  • China
  • Japan
  • Rest of APAC
  • Rest of the World (RoW)
  • Latin America
  • Middle East
  • Africa

The research covers profiles of key players (listed below) engaged in developing AI based solution for the energy sector:

  • ABB
  • C3.AI
  • Energsoft
  • GE Digital
  • IBM
  • Microsoft
  • Oracle
  • Siemens
  • Upside Energy
  • Xcel Energy

In addition, the report features key AI specific initiatives undertaken by some of the leading players (listed below) engaged in the energy segment.

  • Chevron
  • Equinor
  • ExxonMobil
  • OJSC Gazprom
  • Royal Dutch Shell

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