Global Machine Learning in Utilities Market Research Report 2023

Publication ID:

PB503236062

Publication Date:

March 10, 2023

Pages:

102

Publisher:

MRRPB5

Countries:

Global [1]

USD 2,900.00

IT Services [2]

Computing & Electronics [3]

Description:

Highlights
The global Machine Learning in Utilities market was valued at US$ million in 2022 and is anticipated to reach US$ million by 2029, witnessing a CAGR of % during the forecast period 2023-2029. The influence of COVID-19 and the Russia-Ukraine War were considered while estimating market sizes.
North American market for Machine Learning in Utilities is estimated to increase from $ million in 2023 to reach $ million by 2029, at a CAGR of % during the forecast period of 2023 through 2029.
Asia-Pacific market for Machine Learning in Utilities is estimated to increase from $ million in 2023 to reach $ million by 2029, at a CAGR of % during the forecast period of 2023 through 2029.
The global market for Machine Learning in Utilities in Renewable Energy Management is estimated to increase from $ million in 2023 to $ million by 2029, at a CAGR of % during the forecast period of 2023 through 2029.
The key global companies of Machine Learning in Utilities include Baidu, Hewlett Packard Enterprise Development LP, SAS Institute, Inc., IBM, Microsoft, Nvidia, Amazon Web Services, Oracle and SAP, etc. In 2022, the world's top three vendors accounted for approximately % of the revenue.
Report Scope
This report aims to provide a comprehensive presentation of the global market for Machine Learning in Utilities, with both quantitative and qualitative analysis, to help readers develop business/growth strategies, assess the market competitive situation, analyze their position in the current marketplace, and make informed business decisions regarding Machine Learning in Utilities.
The Machine Learning in Utilities market size, estimations, and forecasts are provided in terms of and revenue ($ millions), considering 2022 as the base year, with history and forecast data for the period from 2018 to 2029. This report segments the global Machine Learning in Utilities market comprehensively. Regional market sizes, concerning products by type, by application, and by players, are also provided.
For a more in-depth understanding of the market, the report provides profiles of the competitive landscape, key competitors, and their respective market ranks. The report also discusses technological trends and new product developments.
The report will help the Machine Learning in Utilities companies, new entrants, and industry chain related companies in this market with information on the revenues for the overall market and the sub-segments across the different segments, by company, by type, by application, and by regions.
By Company
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
SmartCloud
Segment by Type
Hardware
Software
Service
Segment by Application
Renewable Energy Management
Demand Forecast
Safety and Security
Infrastructure
Other
By Region
North America
United States
Canada
Europe
Germany
France
UK
Italy
Russia
Nordic Countries
Rest of Europe
Asia-Pacific
China
Japan
South Korea
Southeast Asia
India
Australia
Rest of Asia
Latin America
Mexico
Brazil
Rest of Latin America
Middle East & Africa
Turkey
Saudi Arabia
UAE
Rest of MEA
Core Chapters
Chapter 1: Introduces the report scope of the report, executive summary of different market segments (by type, application, etc), including the market size of each market segment, future development potential, and so on. It offers a high-level view of the current state of the market and its likely evolution in the short to mid-term, and long term.
Chapter 2: Introduces executive summary of global market size, regional market size, this section also introduces the market dynamics, latest developments of the market, the driving factors and restrictive factors of the market, the challenges and risks faced by companies in the industry, and the analysis of relevant policies in the industry.
Chapter 3: Detailed analysis of Machine Learning in Utilities companies鈥� competitive landscape, revenue market share, latest development plan, merger, and acquisition information, etc.
Chapter 4: Provides the analysis of various market segments by type, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different market segments.
Chapter 5: Provides the analysis of various market segments by application, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different downstream markets.
Chapter 6, 7, 8, 9, 10: North America, Europe, Asia Pacific, Latin America, Middle East and Africa segment by country. It provides a quantitative analysis of the market size and development potential of each region and its main countries and introduces the market development, future development prospects, market space, and capacity of each country in the world.
Chapter 11: Provides profiles of key players, introducing the basic situation of the key companies in the market in detail, including product revenue, gross margin, product introduction, recent development, etc.
Chapter 12: The main points and conclusions of the report.

Please Note - This is an on demand report and will be delivered in 2 business days (48 hours) post payment.

Table Of Contents:

1 Report Overview
1.1 Study Scope
1.2 Market Analysis by Type
1.2.1 Global Machine Learning in Utilities Market Size Growth Rate by Type: 2018 VS 2022 VS 2029
1.2.2 Hardware
1.2.3 Software
1.2.4 Service
1.3 Market by Application
1.3.1 Global Machine Learning in Utilities Market Growth by Application: 2018 VS 2022 VS 2029
1.3.2 Renewable Energy Management
1.3.3 Demand Forecast
1.3.4 Safety and Security
1.3.5 Infrastructure
1.3.6 Other
1.4 Study Objectives
1.5 Years Considered
1.6 Years Considered
2 Global Growth Trends
2.1 Global Machine Learning in Utilities Market Perspective (2018-2029)
2.2 Machine Learning in Utilities Growth Trends by Region
2.2.1 Global Machine Learning in Utilities Market Size by Region: 2018 VS 2022 VS 2029
2.2.2 Machine Learning in Utilities Historic Market Size by Region (2018-2023)
2.2.3 Machine Learning in Utilities Forecasted Market Size by Region (2024-2029)
2.3 Machine Learning in Utilities Market Dynamics
2.3.1 Machine Learning in Utilities Industry Trends
2.3.2 Machine Learning in Utilities Market Drivers
2.3.3 Machine Learning in Utilities Market Challenges
2.3.4 Machine Learning in Utilities Market Restraints
3 Competition Landscape by Key Players
3.1 Global Top Machine Learning in Utilities Players by Revenue
3.1.1 Global Top Machine Learning in Utilities Players by Revenue (2018-2023)
3.1.2 Global Machine Learning in Utilities Revenue Market Share by Players (2018-2023)
3.2 Global Machine Learning in Utilities Market Share by Company Type (Tier 1, Tier 2, and Tier 3)
3.3 Players Covered: Ranking by Machine Learning in Utilities Revenue
3.4 Global Machine Learning in Utilities Market Concentration Ratio
3.4.1 Global Machine Learning in Utilities Market Concentration Ratio (CR5 and HHI)
3.4.2 Global Top 10 and Top 5 Companies by Machine Learning in Utilities Revenue in 2022
3.5 Machine Learning in Utilities Key Players Head office and Area Served
3.6 Key Players Machine Learning in Utilities Product Solution and Service
3.7 Date of Enter into Machine Learning in Utilities Market
3.8 Mergers & Acquisitions, Expansion Plans
4 Machine Learning in Utilities Breakdown Data by Type
4.1 Global Machine Learning in Utilities Historic Market Size by Type (2018-2023)
4.2 Global Machine Learning in Utilities Forecasted Market Size by Type (2024-2029)
5 Machine Learning in Utilities Breakdown Data by Application
5.1 Global Machine Learning in Utilities Historic Market Size by Application (2018-2023)
5.2 Global Machine Learning in Utilities Forecasted Market Size by Application (2024-2029)
6 North America
6.1 North America Machine Learning in Utilities Market Size (2018-2029)
6.2 North America Machine Learning in Utilities Market Growth Rate by Country: 2018 VS 2022 VS 2029
6.3 North America Machine Learning in Utilities Market Size by Country (2018-2023)
6.4 North America Machine Learning in Utilities Market Size by Country (2024-2029)
6.5 United States
6.6 Canada
7 Europe
7.1 Europe Machine Learning in Utilities Market Size (2018-2029)
7.2 Europe Machine Learning in Utilities Market Growth Rate by Country: 2018 VS 2022 VS 2029
7.3 Europe Machine Learning in Utilities Market Size by Country (2018-2023)
7.4 Europe Machine Learning in Utilities Market Size by Country (2024-2029)
7.5 Germany
7.6 France
7.7 U.K.
7.8 Italy
7.9 Russia
7.10 Nordic Countries
8 Asia-Pacific
8.1 Asia-Pacific Machine Learning in Utilities Market Size (2018-2029)
8.2 Asia-Pacific Machine Learning in Utilities Market Growth Rate by Region: 2018 VS 2022 VS 2029
8.3 Asia-Pacific Machine Learning in Utilities Market Size by Region (2018-2023)
8.4 Asia-Pacific Machine Learning in Utilities Market Size by Region (2024-2029)
8.5 China
8.6 Japan
8.7 South Korea
8.8 Southeast Asia
8.9 India
8.10 Australia
9 Latin America
9.1 Latin America Machine Learning in Utilities Market Size (2018-2029)
9.2 Latin America Machine Learning in Utilities Market Growth Rate by Country: 2018 VS 2022 VS 2029
9.3 Latin America Machine Learning in Utilities Market Size by Country (2018-2023)
9.4 Latin America Machine Learning in Utilities Market Size by Country (2024-2029)
9.5 Mexico
9.6 Brazil
10 Middle East & Africa
10.1 Middle East & Africa Machine Learning in Utilities Market Size (2018-2029)
10.2 Middle East & Africa Machine Learning in Utilities Market Growth Rate by Country: 2018 VS 2022 VS 2029
10.3 Middle East & Africa Machine Learning in Utilities Market Size by Country (2018-2023)
10.4 Middle East & Africa Machine Learning in Utilities Market Size by Country (2024-2029)
10.5 Turkey
10.6 Saudi Arabia
10.7 UAE
11 Key Players Profiles
11.1 Baidu
11.1.1 Baidu Company Detail
11.1.2 Baidu Business Overview
11.1.3 Baidu Machine Learning in Utilities Introduction
11.1.4 Baidu Revenue in Machine Learning in Utilities Business (2018-2023)
11.1.5 Baidu Recent Development
11.2 Hewlett Packard Enterprise Development LP
11.2.1 Hewlett Packard Enterprise Development LP Company Detail
11.2.2 Hewlett Packard Enterprise Development LP Business Overview
11.2.3 Hewlett Packard Enterprise Development LP Machine Learning in Utilities Introduction
11.2.4 Hewlett Packard Enterprise Development LP Revenue in Machine Learning in Utilities Business (2018-2023)
11.2.5 Hewlett Packard Enterprise Development LP Recent Development
11.3 SAS Institute, Inc.
11.3.1 SAS Institute, Inc. Company Detail
11.3.2 SAS Institute, Inc. Business Overview
11.3.3 SAS Institute, Inc. Machine Learning in Utilities Introduction
11.3.4 SAS Institute, Inc. Revenue in Machine Learning in Utilities Business (2018-2023)
11.3.5 SAS Institute, Inc. Recent Development
11.4 IBM
11.4.1 IBM Company Detail
11.4.2 IBM Business Overview
11.4.3 IBM Machine Learning in Utilities Introduction
11.4.4 IBM Revenue in Machine Learning in Utilities Business (2018-2023)
11.4.5 IBM Recent Development
11.5 Microsoft
11.5.1 Microsoft Company Detail
11.5.2 Microsoft Business Overview
11.5.3 Microsoft Machine Learning in Utilities Introduction
11.5.4 Microsoft Revenue in Machine Learning in Utilities Business (2018-2023)
11.5.5 Microsoft Recent Development
11.6 Nvidia
11.6.1 Nvidia Company Detail
11.6.2 Nvidia Business Overview
11.6.3 Nvidia Machine Learning in Utilities Introduction
11.6.4 Nvidia Revenue in Machine Learning in Utilities Business (2018-2023)
11.6.5 Nvidia Recent Development
11.7 Amazon Web Services
11.7.1 Amazon Web Services Company Detail
11.7.2 Amazon Web Services Business Overview
11.7.3 Amazon Web Services Machine Learning in Utilities Introduction
11.7.4 Amazon Web Services Revenue in Machine Learning in Utilities Business (2018-2023)
11.7.5 Amazon Web Services Recent Development
11.8 Oracle
11.8.1 Oracle Company Detail
11.8.2 Oracle Business Overview
11.8.3 Oracle Machine Learning in Utilities Introduction
11.8.4 Oracle Revenue in Machine Learning in Utilities Business (2018-2023)
11.8.5 Oracle Recent Development
11.9 SAP
11.9.1 SAP Company Detail
11.9.2 SAP Business Overview
11.9.3 SAP Machine Learning in Utilities Introduction
11.9.4 SAP Revenue in Machine Learning in Utilities Business (2018-2023)
11.9.5 SAP Recent Development
11.10 BigML, Inc.
11.10.1 BigML, Inc. Company Detail
11.10.2 BigML, Inc. Business Overview
11.10.3 BigML, Inc. Machine Learning in Utilities Introduction
11.10.4 BigML, Inc. Revenue in Machine Learning in Utilities Business (2018-2023)
11.10.5 BigML, Inc. Recent Development
11.11 Fair Isaac Corporation
11.11.1 Fair Isaac Corporation Company Detail
11.11.2 Fair Isaac Corporation Business Overview
11.11.3 Fair Isaac Corporation Machine Learning in Utilities Introduction
11.11.4 Fair Isaac Corporation Revenue in Machine Learning in Utilities Business (2018-2023)
11.11.5 Fair Isaac Corporation Recent Development
11.12 Intel Corporation
11.12.1 Intel Corporation Company Detail
11.12.2 Intel Corporation Business Overview
11.12.3 Intel Corporation Machine Learning in Utilities Introduction
11.12.4 Intel Corporation Revenue in Machine Learning in Utilities Business (2018-2023)
11.12.5 Intel Corporation Recent Development
11.13 Google LLC
11.13.1 Google LLC Company Detail
11.13.2 Google LLC Business Overview
11.13.3 Google LLC Machine Learning in Utilities Introduction
11.13.4 Google LLC Revenue in Machine Learning in Utilities Business (2018-2023)
11.13.5 Google LLC Recent Development
11.14 H2o.AI
11.14.1 H2o.AI Company Detail
11.14.2 H2o.AI Business Overview
11.14.3 H2o.AI Machine Learning in Utilities Introduction
11.14.4 H2o.AI Revenue in Machine Learning in Utilities Business (2018-2023)
11.14.5 H2o.AI Recent Development
11.15 Alpiq
11.15.1 Alpiq Company Detail
11.15.2 Alpiq Business Overview
11.15.3 Alpiq Machine Learning in Utilities Introduction
11.15.4 Alpiq Revenue in Machine Learning in Utilities Business (2018-2023)
11.15.5 Alpiq Recent Development
11.16 SmartCloud
11.16.1 SmartCloud Company Detail
11.16.2 SmartCloud Business Overview
11.16.3 SmartCloud Machine Learning in Utilities Introduction
11.16.4 SmartCloud Revenue in Machine Learning in Utilities Business (2018-2023)
11.16.5 SmartCloud Recent Development
12 Analyst's Viewpoints/Conclusions
13 Appendix
13.1 Research Methodology
13.1.1 Methodology/Research Approach
13.1.2 Data Source
13.2 Disclaimer
13.3 Author Details

Companies Mentioned:

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
SmartCloud

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