$12.2Bn AI in Energy Management Market: Analysis by Type, Solution, Application, Technology, End-user & Region - Forecast to 2024
DUBLIN, Feb. 4, 2020 /PRNewswire/ -- The "AI in Energy Management Market Research Report: By Type, Solution, Application, Technology, End User - Industry Opportunity Analysis and Growth Forecast to 2024" report has been added to ResearchAndMarkets.com's offering.
From $4,439.1 million in 2018, the artificial intelligence (AI) in energy management market is projected to grow to $12,200.9 million in 2024, at a 19.8% CAGR during 2019-2024 (forecast period).
Based on end user, the market was dominated by the utility category, in 2018, as AI solutions are being deployed by numerous companies, including Dominion Energy and Duke Energy Corporation, to bridge the gap between the demand and supply of electricity.
The most significant trend being observed in the AI in energy management market is the integration of internet of things (IoT) in energy management systems (EMS). IoT provides remote control, software automation, data insights, and proactive monitoring, in EMSs. It uses smart sensors and meters, installed in production lines as well as machines, to alert users about the amount of energy being consumed. Additionally, with the complete package of services and software IoT offers, energy costs can be reduced substantially.
The use of AI to make the grid more efficient is a major AI in energy management market growth driver. A grid is a complex system of wires, transformers, and other infrastructure components, through which electricity is transported over long distance. Such infrastructure differs vastly in complexity and size; it can be for a single building, an entire nation, or one which distributes energy across countries and continents, in which case it is called a transcontinental grid.
The power in the grid comes from a variety of sources, including fossil-fuel-based power plants, solar panel installations, wind power plants, hydroelectricity plants, as well as nuclear power stations, which makes their operation a cumbersome process. With AI analyzing the massive volumes of data generated from their operations on a daily basis, they become stable and energy-efficient, thereby driving the AI in energy management market growth.
Key Topics Covered
Chapter 1. Research Background
1.1 Research Objectives
1.2 Market Definition
1.3 Research Scope
1.3.1 Market Segmentation by Deployment Type
1.3.2 Market Segmentation by Solution
1.3.3 Market Segmentation by Application
1.3.4 Market Segmentation by Technology
1.3.5 Market Segmentation by End-user
1.3.6 Market Segmentation by Region
1.3.7 Analysis Period
1.3.8 Market Data Reporting Unit
1.3.8.1 Value
1.4 Key Stakeholders
Chapter 2. Research Methodology
Chapter 3. Executive Summary
Chapter 4. Introduction
4.1 Definition of Market Segments
4.1.1 Market Segmentation by Deployment Type
4.1.1.1 Cloud
4.1.1.2 On-Premises
4.1.2 Market Segmentation by Solution
4.1.2.1 Renewable Management
4.1.2.2 Demand Management
4.1.2.3 Infrastructure Management
4.1.3 Market Segmentation by Application
4.1.3.1 Energy Generation
4.1.3.2 Energy Transmission
4.1.3.3 Energy Distribution
4.1.3.4 Energy Output Forecasting
4.1.3.4.1 Load Forecasting
4.1.3.4.2 Yield Optimization
4.1.3.4.3 Predictive Maintenance
4.1.3.4.4 Live Metering
4.1.4 Market Segmentation by Technology
4.1.4.1 Machine Learning
4.1.4.2 Natural Language Processing
4.1.4.3 Computer Vision
4.1.5 Market Segmentation by End-user
4.1.5.1 Manufacturing
4.1.5.2 Utility
4.1.5.3 Residential
4.1.5.4 Government
4.1.5.5 Retail
4.1.5.6 Healthcare
4.1.5.7 Education
4.2 Value Chain Analysis
4.3 Market Dynamics
4.3.1 Trends
4.3.1.1 Integration of IoT in Energy Management Systems (EMSs)
4.3.1.2 Increasing Usage of Smart Grid
4.3.2 Drivers
4.3.2.1 Leveraging AI to Improve Grid Stability
4.3.2.2 AI to Forecast Energy Usage and Make Energy-Saving Decisions
4.3.2.3 Increasing Adoption of AI for Low-Carbon Electricity Generation
4.3.2.4 Impact Analysis of Drivers on Market Forecast
4.3.3 Restraints
4.3.3.1 Energy Networks Vulnerable to Cyberattacks
4.3.3.2 Huge Costs of Deploying AI-Based EMSs
4.3.3.3 Impact Analysis of Restraints on Market Forecast
4.3.4 Opportunities
4.3.4.1 Use of AI-Enabled Robots for Site Inspection and Maintenance
4.4 Porter's Five Forces Analysis
Chapter 5. Global Market Size and Forecast
5.1 By Deployment Type
5.2 By Solution
5.3 By Application
5.3.1 Energy Output Forecasting, By Type
5.4 By Technology
5.5 By End-user
5.6 By Region
Chapter 6. North America Market Size and Forecast
Chapter 7. Europe Market Size and Forecast
Chapter 8. APAC Market Size and Forecast
Chapter 9. LATAM Market Size and Forecast
Chapter 10. MEA Market Size and Forecast
Chapter 11. Competitive Landscape
11.1 List of Key Players and Offerings
11.2 Market Share Analysis of Key Players
11.3 Competitive Benchmarking of Key Players
11.4 Strategic Developments of Key Players
11.4.1 Mergers and Acquisitions
11.4.2 Partnerships
11.4.3 Product Launches
11.4.4 Geographic Expansion
11.4.5 Client Wins
Chapter 12. Company Profiles
12.1 General Electric Company
12.1.1 Business Overview
12.1.2 Product and Service Offerings
12.1.3 Key Financial Summary
12.2 ABB Ltd.
12.3 Siemens AG
12.4 Schneider Electric SE
12.5 Mitsubishi Electric Corporation
12.6 Eaton Corporation plc
12.7 Alphabet Inc.
12.8 Honeywell International Inc.
12.9 International Business Machines (IBM) Corporation
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