Global Machine Learning in Utilities Market Overview

Machine Learning in Utilities Market size is projected to reach xxxx units by 2025 from an estimated xxxx unit in 2019, growing at a CAGR of xx% globally.

Introspective Market Research provides the newest industry data about Machine Learning in Utilities Market and industry future trends, allowing you to identify the products and end users driving revenue growth and profitability. The Machine Learning in Utilities industry report lists the leading competitors and provides the insights strategic industry Analysis of the key factors influencing the market.

Scope of the Machine Learning in Utilities Market

The main goal of this report is to help users understand the Machine Learning in Utilities market in terms of its definition, segmentation, market potential, influential trends, and challenges facing the market. In-depth research and analysis took place while preparing the report. Readers will find this report very helpful to in-depth understanding of the market.

Market Segmentation



Players Covered in Machine Learning in Utilities market are :

  • Baidu
  • Hewlett Packard Enterprise Development LP
  • SAS Institute Inc.
  • IBM
  • Microsoft
  • Nvidia
  • Amazon Web Services
  • Oracle
  • SAP
  • BigML Inc.
  • Fair Isaac Corporation
  • Intel Corporation
  • Google LLC
  • H2o.AI
  • Alpiq

Machine Learning in Utilities Market - Current Analysis by Market Share
Segmentations by Type
  • Hardware
  • Software
  • Service
by Application
  • Renewable Energy Management
  • Demand Forecast
  • Safety and Security
  • Infrastructure
  • Other
by Region
  • North America (U.S., Canada, Mexico)
  • Europe (Germany, U.K., France, Italy, Russia, Spain, Rest of Europe)
  • Asia-Pacific (China, India, Japan, Southeast Asia, Rest of APAC)
  • Middle East & Africa (GCC Countries, South Africa, Rest of MEA)
  • South America (Brazil, Argentina, Rest of South America)
Chapter 1: Introduction
 1.1 Research Objectives
 1.2 Research Methodology
 1.3 Research Process
 1.4 Scope and Coverage
  1.4.1 Market Definition
  1.4.2 Key Questions Answered
 1.5 Market Segmentation

Chapter 2:Executive Summary

Chapter 3:Growth Opportunities By Segment
 3.1 By Type
 3.2 By Application

Chapter 4: Market Landscape
 4.1 Porter's Five Forces Analysis
  4.1.1 Bargaining Power of Supplier
  4.1.2 Threat of New Entrants
  4.1.3 Threat of Substitutes
  4.1.4 Competitive Rivalry
  4.1.5 Bargaining Power Among Buyers
 4.2 Industry Value Chain Analysis
 4.3 Market Dynamics
  3.5.1 Drivers
  3.5.2 Restraints
  3.5.3 Opportunities
  3.5.4 Challenges
 4.4 Pestle Analysis
 4.5 Technological Roadmap
 4.6 Regulatory Landscape
 4.7 SWOT Analysis
 4.8 Price Trend Analysis
 4.9 Patent Analysis
 4.10 Analysis of the Impact of Covid-19
  4.10.1 Impact on the Overall Market
  4.10.2 Impact on the Supply Chain
  4.10.3 Impact on the Key Manufacturers
  4.10.4 Impact on the Pricing

Chapter 4: Machine Learning in Utilities Market by Type
 4.1 Machine Learning in Utilities Market Overview Snapshot and Growth Engine
 4.2 Machine Learning in Utilities Market Overview
 4.3 Hardware
  4.3.1 Introduction and Market Overview
  4.3.2 Historic and Forecasted Market Size (2016-2028F)
  4.3.3 Key Market Trends, Growth Factors and Opportunities
  4.3.4 Hardware: Grographic Segmentation
 4.4 Software
  4.4.1 Introduction and Market Overview
  4.4.2 Historic and Forecasted Market Size (2016-2028F)
  4.4.3 Key Market Trends, Growth Factors and Opportunities
  4.4.4 Software: Grographic Segmentation
 4.5 Service
  4.5.1 Introduction and Market Overview
  4.5.2 Historic and Forecasted Market Size (2016-2028F)
  4.5.3 Key Market Trends, Growth Factors and Opportunities
  4.5.4 Service: Grographic Segmentation

Chapter 5: Machine Learning in Utilities Market by Application
 5.1 Machine Learning in Utilities Market Overview Snapshot and Growth Engine
 5.2 Machine Learning in Utilities Market Overview
 5.3 Renewable Energy Management
  5.3.1 Introduction and Market Overview
  5.3.2 Historic and Forecasted Market Size (2016-2028F)
  5.3.3 Key Market Trends, Growth Factors and Opportunities
  5.3.4 Renewable Energy Management: Grographic Segmentation
 5.4 Demand Forecast
  5.4.1 Introduction and Market Overview
  5.4.2 Historic and Forecasted Market Size (2016-2028F)
  5.4.3 Key Market Trends, Growth Factors and Opportunities
  5.4.4 Demand Forecast: Grographic Segmentation
 5.5 Safety and Security
  5.5.1 Introduction and Market Overview
  5.5.2 Historic and Forecasted Market Size (2016-2028F)
  5.5.3 Key Market Trends, Growth Factors and Opportunities
  5.5.4 Safety and Security: Grographic Segmentation
 5.6 Infrastructure
  5.6.1 Introduction and Market Overview
  5.6.2 Historic and Forecasted Market Size (2016-2028F)
  5.6.3 Key Market Trends, Growth Factors and Opportunities
  5.6.4 Infrastructure: Grographic Segmentation
 5.7 Other
  5.7.1 Introduction and Market Overview
  5.7.2 Historic and Forecasted Market Size (2016-2028F)
  5.7.3 Key Market Trends, Growth Factors and Opportunities
  5.7.4 Other: Grographic Segmentation

Chapter 6: Company Profiles and Competitive Analysis
 6.1 Competitive Landscape
  6.1.1 Competitive Positioning
  6.1.2 Machine Learning in Utilities Sales and Market Share By Players
  6.1.3 Industry BCG Matrix
  6.1.4 Ansoff Matrix
  6.1.5 Machine Learning in Utilities Industry Concentration Ratio (CR5 and HHI)
  6.1.6 Top 5 Machine Learning in Utilities Players Market Share
  6.1.7 Mergers and Acquisitions
  6.1.8 Business Strategies By Top Players
 6.2 BAIDU
  6.2.1 Company Overview
  6.2.2 Key Executives
  6.2.3 Company Snapshot
  6.2.4 Operating Business Segments
  6.2.5 Product Portfolio
  6.2.6 Business Performance
  6.2.7 Key Strategic Moves and Recent Developments
  6.2.8 SWOT Analysis
 6.3 HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
 6.4 SAS INSTITUTE INC.
 6.5 IBM
 6.6 MICROSOFT
 6.7 NVIDIA
 6.8 AMAZON WEB SERVICES
 6.9 ORACLE
 6.10 SAP
 6.11 BIGML INC.
 6.12 FAIR ISAAC CORPORATION
 6.13 INTEL CORPORATION
 6.14 GOOGLE LLC
 6.15 H2O.AI
 6.16 ALPIQ

Chapter 7: Global Machine Learning in Utilities Market Analysis, Insights and Forecast, 2016-2028
 7.1 Market Overview
 7.2 Historic and Forecasted Market Size By Type
  7.2.1 Hardware
  7.2.2 Software
  7.2.3 Service
 7.3 Historic and Forecasted Market Size By Application
  7.3.1 Renewable Energy Management
  7.3.2 Demand Forecast
  7.3.3 Safety and Security
  7.3.4 Infrastructure
  7.3.5 Other

Chapter 8: North America Machine Learning in Utilities Market Analysis, Insights and Forecast, 2016-2028
 8.1 Key Market Trends, Growth Factors and Opportunities
 8.2 Impact of Covid-19
 8.3 Key Players
 8.4 Key Market Trends, Growth Factors and Opportunities
 8.4 Historic and Forecasted Market Size By Type
  8.4.1 Hardware
  8.4.2 Software
  8.4.3 Service
 8.5 Historic and Forecasted Market Size By Application
  8.5.1 Renewable Energy Management
  8.5.2 Demand Forecast
  8.5.3 Safety and Security
  8.5.4 Infrastructure
  8.5.5 Other
 8.6 Historic and Forecast Market Size by Country
  8.6.1 U.S.
  8.6.2 Canada
  8.6.3 Mexico

Chapter 9: Europe Machine Learning in Utilities Market Analysis, Insights and Forecast, 2016-2028
 9.1 Key Market Trends, Growth Factors and Opportunities
 9.2 Impact of Covid-19
 9.3 Key Players
 9.4 Key Market Trends, Growth Factors and Opportunities
 9.4 Historic and Forecasted Market Size By Type
  9.4.1 Hardware
  9.4.2 Software
  9.4.3 Service
 9.5 Historic and Forecasted Market Size By Application
  9.5.1 Renewable Energy Management
  9.5.2 Demand Forecast
  9.5.3 Safety and Security
  9.5.4 Infrastructure
  9.5.5 Other
 9.6 Historic and Forecast Market Size by Country
  9.6.1 Germany
  9.6.2 U.K.
  9.6.3 France
  9.6.4 Italy
  9.6.5 Russia
  9.6.6 Spain

Chapter 10: Asia-Pacific Machine Learning in Utilities Market Analysis, Insights and Forecast, 2016-2028
 10.1 Key Market Trends, Growth Factors and Opportunities
 10.2 Impact of Covid-19
 10.3 Key Players
 10.4 Key Market Trends, Growth Factors and Opportunities
 10.4 Historic and Forecasted Market Size By Type
  10.4.1 Hardware
  10.4.2 Software
  10.4.3 Service
 10.5 Historic and Forecasted Market Size By Application
  10.5.1 Renewable Energy Management
  10.5.2 Demand Forecast
  10.5.3 Safety and Security
  10.5.4 Infrastructure
  10.5.5 Other
 10.6 Historic and Forecast Market Size by Country
  10.6.1 China
  10.6.2 India
  10.6.3 Japan
  10.6.4 Southeast Asia

Chapter 11: South America Machine Learning in Utilities Market Analysis, Insights and Forecast, 2016-2028
 11.1 Key Market Trends, Growth Factors and Opportunities
 11.2 Impact of Covid-19
 11.3 Key Players
 11.4 Key Market Trends, Growth Factors and Opportunities
 11.4 Historic and Forecasted Market Size By Type
  11.4.1 Hardware
  11.4.2 Software
  11.4.3 Service
 11.5 Historic and Forecasted Market Size By Application
  11.5.1 Renewable Energy Management
  11.5.2 Demand Forecast
  11.5.3 Safety and Security
  11.5.4 Infrastructure
  11.5.5 Other
 11.6 Historic and Forecast Market Size by Country
  11.6.1 Brazil
  11.6.2 Argentina

Chapter 12: Middle East & Africa Machine Learning in Utilities Market Analysis, Insights and Forecast, 2016-2028
 12.1 Key Market Trends, Growth Factors and Opportunities
 12.2 Impact of Covid-19
 12.3 Key Players
 12.4 Key Market Trends, Growth Factors and Opportunities
 12.4 Historic and Forecasted Market Size By Type
  12.4.1 Hardware
  12.4.2 Software
  12.4.3 Service
 12.5 Historic and Forecasted Market Size By Application
  12.5.1 Renewable Energy Management
  12.5.2 Demand Forecast
  12.5.3 Safety and Security
  12.5.4 Infrastructure
  12.5.5 Other
 12.6 Historic and Forecast Market Size by Country
  12.6.1 Saudi Arabia
  12.6.2 South Africa

Chapter 13 Investment Analysis

Chapter 14 Analyst Viewpoint and Conclusion
Machine Learning in Utilities Market - Current Analysis by Market Share
Segmentations by Type
  • Hardware
  • Software
  • Service
by Application
  • Renewable Energy Management
  • Demand Forecast
  • Safety and Security
  • Infrastructure
  • Other
by Region
  • North America (U.S., Canada, Mexico)
  • Europe (Germany, U.K., France, Italy, Russia, Spain, Rest of Europe)
  • Asia-Pacific (China, India, Japan, Southeast Asia, Rest of APAC)
  • Middle East & Africa (GCC Countries, South Africa, Rest of MEA)
  • South America (Brazil, Argentina, Rest of South America)
LIST OF TABLES

TABLE 001. EXECUTIVE SUMMARY
TABLE 002. MACHINE LEARNING IN UTILITIES MARKET BARGAINING POWER OF SUPPLIERS
TABLE 003. MACHINE LEARNING IN UTILITIES MARKET BARGAINING POWER OF CUSTOMERS
TABLE 004. MACHINE LEARNING IN UTILITIES MARKET COMPETITIVE RIVALRY
TABLE 005. MACHINE LEARNING IN UTILITIES MARKET THREAT OF NEW ENTRANTS
TABLE 006. MACHINE LEARNING IN UTILITIES MARKET THREAT OF SUBSTITUTES
TABLE 007. MACHINE LEARNING IN UTILITIES MARKET BY TYPE
TABLE 008. HARDWARE MARKET OVERVIEW (2016-2028)
TABLE 009. SOFTWARE MARKET OVERVIEW (2016-2028)
TABLE 010. SERVICE MARKET OVERVIEW (2016-2028)
TABLE 011. MACHINE LEARNING IN UTILITIES MARKET BY APPLICATION
TABLE 012. RENEWABLE ENERGY MANAGEMENT MARKET OVERVIEW (2016-2028)
TABLE 013. DEMAND FORECAST MARKET OVERVIEW (2016-2028)
TABLE 014. SAFETY AND SECURITY MARKET OVERVIEW (2016-2028)
TABLE 015. INFRASTRUCTURE MARKET OVERVIEW (2016-2028)
TABLE 016. OTHER MARKET OVERVIEW (2016-2028)
TABLE 017. NORTH AMERICA MACHINE LEARNING IN UTILITIES MARKET, BY TYPE (2016-2028)
TABLE 018. NORTH AMERICA MACHINE LEARNING IN UTILITIES MARKET, BY APPLICATION (2016-2028)
TABLE 019. N MACHINE LEARNING IN UTILITIES MARKET, BY COUNTRY (2016-2028)
TABLE 020. EUROPE MACHINE LEARNING IN UTILITIES MARKET, BY TYPE (2016-2028)
TABLE 021. EUROPE MACHINE LEARNING IN UTILITIES MARKET, BY APPLICATION (2016-2028)
TABLE 022. MACHINE LEARNING IN UTILITIES MARKET, BY COUNTRY (2016-2028)
TABLE 023. ASIA PACIFIC MACHINE LEARNING IN UTILITIES MARKET, BY TYPE (2016-2028)
TABLE 024. ASIA PACIFIC MACHINE LEARNING IN UTILITIES MARKET, BY APPLICATION (2016-2028)
TABLE 025. MACHINE LEARNING IN UTILITIES MARKET, BY COUNTRY (2016-2028)
TABLE 026. MIDDLE EAST & AFRICA MACHINE LEARNING IN UTILITIES MARKET, BY TYPE (2016-2028)
TABLE 027. MIDDLE EAST & AFRICA MACHINE LEARNING IN UTILITIES MARKET, BY APPLICATION (2016-2028)
TABLE 028. MACHINE LEARNING IN UTILITIES MARKET, BY COUNTRY (2016-2028)
TABLE 029. SOUTH AMERICA MACHINE LEARNING IN UTILITIES MARKET, BY TYPE (2016-2028)
TABLE 030. SOUTH AMERICA MACHINE LEARNING IN UTILITIES MARKET, BY APPLICATION (2016-2028)
TABLE 031. MACHINE LEARNING IN UTILITIES MARKET, BY COUNTRY (2016-2028)
TABLE 032. BAIDU: SNAPSHOT
TABLE 033. BAIDU: BUSINESS PERFORMANCE
TABLE 034. BAIDU: PRODUCT PORTFOLIO
TABLE 035. BAIDU: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 035. HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP: SNAPSHOT
TABLE 036. HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP: BUSINESS PERFORMANCE
TABLE 037. HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP: PRODUCT PORTFOLIO
TABLE 038. HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 038. SAS INSTITUTE INC.: SNAPSHOT
TABLE 039. SAS INSTITUTE INC.: BUSINESS PERFORMANCE
TABLE 040. SAS INSTITUTE INC.: PRODUCT PORTFOLIO
TABLE 041. SAS INSTITUTE INC.: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 041. IBM: SNAPSHOT
TABLE 042. IBM: BUSINESS PERFORMANCE
TABLE 043. IBM: PRODUCT PORTFOLIO
TABLE 044. IBM: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 044. MICROSOFT: SNAPSHOT
TABLE 045. MICROSOFT: BUSINESS PERFORMANCE
TABLE 046. MICROSOFT: PRODUCT PORTFOLIO
TABLE 047. MICROSOFT: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 047. NVIDIA: SNAPSHOT
TABLE 048. NVIDIA: BUSINESS PERFORMANCE
TABLE 049. NVIDIA: PRODUCT PORTFOLIO
TABLE 050. NVIDIA: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 050. AMAZON WEB SERVICES: SNAPSHOT
TABLE 051. AMAZON WEB SERVICES: BUSINESS PERFORMANCE
TABLE 052. AMAZON WEB SERVICES: PRODUCT PORTFOLIO
TABLE 053. AMAZON WEB SERVICES: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 053. ORACLE: SNAPSHOT
TABLE 054. ORACLE: BUSINESS PERFORMANCE
TABLE 055. ORACLE: PRODUCT PORTFOLIO
TABLE 056. ORACLE: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 056. SAP: SNAPSHOT
TABLE 057. SAP: BUSINESS PERFORMANCE
TABLE 058. SAP: PRODUCT PORTFOLIO
TABLE 059. SAP: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 059. BIGML INC.: SNAPSHOT
TABLE 060. BIGML INC.: BUSINESS PERFORMANCE
TABLE 061. BIGML INC.: PRODUCT PORTFOLIO
TABLE 062. BIGML INC.: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 062. FAIR ISAAC CORPORATION: SNAPSHOT
TABLE 063. FAIR ISAAC CORPORATION: BUSINESS PERFORMANCE
TABLE 064. FAIR ISAAC CORPORATION: PRODUCT PORTFOLIO
TABLE 065. FAIR ISAAC CORPORATION: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 065. INTEL CORPORATION: SNAPSHOT
TABLE 066. INTEL CORPORATION: BUSINESS PERFORMANCE
TABLE 067. INTEL CORPORATION: PRODUCT PORTFOLIO
TABLE 068. INTEL CORPORATION: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 068. GOOGLE LLC: SNAPSHOT
TABLE 069. GOOGLE LLC: BUSINESS PERFORMANCE
TABLE 070. GOOGLE LLC: PRODUCT PORTFOLIO
TABLE 071. GOOGLE LLC: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 071. H2O.AI: SNAPSHOT
TABLE 072. H2O.AI: BUSINESS PERFORMANCE
TABLE 073. H2O.AI: PRODUCT PORTFOLIO
TABLE 074. H2O.AI: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 074. ALPIQ: SNAPSHOT
TABLE 075. ALPIQ: BUSINESS PERFORMANCE
TABLE 076. ALPIQ: PRODUCT PORTFOLIO
TABLE 077. ALPIQ: KEY STRATEGIC MOVES AND DEVELOPMENTS

LIST OF FIGURES

FIGURE 001. YEARS CONSIDERED FOR ANALYSIS
FIGURE 002. SCOPE OF THE STUDY
FIGURE 003. MACHINE LEARNING IN UTILITIES MARKET OVERVIEW BY REGIONS
FIGURE 004. PORTER'S FIVE FORCES ANALYSIS
FIGURE 005. BARGAINING POWER OF SUPPLIERS
FIGURE 006. COMPETITIVE RIVALRYFIGURE 007. THREAT OF NEW ENTRANTS
FIGURE 008. THREAT OF SUBSTITUTES
FIGURE 009. VALUE CHAIN ANALYSIS
FIGURE 010. PESTLE ANALYSIS
FIGURE 011. MACHINE LEARNING IN UTILITIES MARKET OVERVIEW BY TYPE
FIGURE 012. HARDWARE MARKET OVERVIEW (2016-2028)
FIGURE 013. SOFTWARE MARKET OVERVIEW (2016-2028)
FIGURE 014. SERVICE MARKET OVERVIEW (2016-2028)
FIGURE 015. MACHINE LEARNING IN UTILITIES MARKET OVERVIEW BY APPLICATION
FIGURE 016. RENEWABLE ENERGY MANAGEMENT MARKET OVERVIEW (2016-2028)
FIGURE 017. DEMAND FORECAST MARKET OVERVIEW (2016-2028)
FIGURE 018. SAFETY AND SECURITY MARKET OVERVIEW (2016-2028)
FIGURE 019. INFRASTRUCTURE MARKET OVERVIEW (2016-2028)
FIGURE 020. OTHER MARKET OVERVIEW (2016-2028)
FIGURE 021. NORTH AMERICA MACHINE LEARNING IN UTILITIES MARKET OVERVIEW BY COUNTRY (2016-2028)
FIGURE 022. EUROPE MACHINE LEARNING IN UTILITIES MARKET OVERVIEW BY COUNTRY (2016-2028)
FIGURE 023. ASIA PACIFIC MACHINE LEARNING IN UTILITIES MARKET OVERVIEW BY COUNTRY (2016-2028)
FIGURE 024. MIDDLE EAST & AFRICA MACHINE LEARNING IN UTILITIES MARKET OVERVIEW BY COUNTRY (2016-2028)
FIGURE 025. SOUTH AMERICA MACHINE LEARNING IN UTILITIES MARKET OVERVIEW BY COUNTRY (2016-2028)

Frequently Asked Questions :

What would be forecast period in the market research report?
The forecast period in the market research report is 2021-2025.
Who are the key players in Machine Learning in Utilities market?
The key players mentioned are Baidu, Hewlett Packard Enterprise Development LP, SAS Institute Inc., IBM, Microsoft, Nvidia, Amazon Web Services, Oracle, SAP, BigML Inc., Fair Isaac Corporation, Intel Corporation, Google LLC, H2o.AI, Alpiq.
What are the segments of Machine Learning in Utilities market?
The Machine Learning in Utilities market is segmented into application type, product type and region. By Application type, the market is categorized into Renewable Energy Management, Demand Forecast, Safety and Security, Infrastructure, Other. By product type, it is classified into Hardware, Software, Service and others. By region, it is analysed across North America (U.S.; Canada; Mexico), Europe (Germany; U.K.; France; Italy; Russia; Spain etc.), Asia-Pacific (China; India; Japan; Southeast Asia etc.), South America (Brazil; Argentina etc.), Middle East & Africa (Saudi Arabia; South Africa etc.).
What is the Machine Learning in Utilities market?
Machine Learning in Utilities Market size is projected to reach xxxx units by 2025 from an estimated xxxx unit in 2020, growing at a CAGR of xx% globally.
How big is the Machine Learning in Utilities market?
The global Machine Learning in Utilities market size was estimated at USD XX billion in 2020 and is expected to reach USD XX billion in 2025.