Ai In Energy Market
AI in Energy Market Forecasts to 2030 - Global Analysis By Component Type (Hardware, Solutions and Services), Deployment Type (On-premise and Cloud-based), Application, End User and by Geography
According to Stratistics MRC, the Global AI in Energy Market is accounted for $6.81 billion in 2024 and is expected to reach $19.73 billion by 2030 growing at a CAGR of 19.4% during the forecast period. Artificial intelligence (AI) is transforming the energy industry through cost reduction, efficiency enhancement, and process optimization. Artificial intelligence (AI) technologies are being used to better manage distribution networks, forecast energy demand, and maximize energy production. AI is able to forecast patterns of energy consumption and make real-time adjustments to supply by analyzing large amounts of data from sensors and smart grids using sophisticated algorithms and machine learning. Furthermore, by controlling their variability and guaranteeing a steady supply of energy, AI plays a crucial role in the integration of renewable energy sources into the grid.
According to the International Energy Agency (IEA), the adoption of AI in the energy sector could lead to significant improvements in energy efficiency, enabling smarter energy systems that can adapt to changing demand and supply conditions in real-time.
Market Dynamics:
Driver:
Growing interest in energy efficiency
The demand for more effective energy management is growing as the world's energy consumption keeps rising. Leading the way in meeting this demand are artificial intelligence (AI) technologies, which provide tools to forecast patterns in energy consumption, maximize energy output, and cut down on needless energy spending. Artificial intelligence (AI) has the ability to recognize inefficiencies in energy systems, suggest modifications, and initiate automated reactions to variations in demand using machine learning algorithms. Moreover, by making the best use of the resources at hand, this not only lowers operating costs for energy providers but also helps the global effort to cut greenhouse gas emissions.
Restraint:
Exorbitant implementation expenses
The energy sector can benefit greatly from artificial intelligence (AI), but many organizations—especially smaller utilities and energy companies—may find the initial costs of implementing AI technologies to be unaffordable. Considerable investment in software, hardware, and qualified labor is needed for the integration of AI. Upgrading current infrastructure, investing in hiring or training data scientists and AI specialists, and buying cutting-edge sensors and data processing equipment are all possible needs for businesses. Additionally, AI algorithms must be customized for particular energy applications, which means that creating and maintaining them can be expensive.
Opportunity:
Creating AI-powered predictive maintenance systems
The energy sector has a lot of potential when it comes to AI-driven predictive maintenance. Through constant monitoring of the state of energy infrastructure, including power plants, transmission lines, and renewable energy installations, artificial intelligence (AI) can anticipate maintenance needs before a breakdown happens. In addition to lowering maintenance costs, this increases asset lifespan and decreases downtime. Furthermore, in addition to increasing operational effectiveness, the use of AI in predictive maintenance also increases safety and dependability in the generation and delivery of energy.
Threat:
Threats and risks to cybersecurity
There are major cybersecurity risks associated with the energy sector's growing reliance on AI. Artificial intelligence (AI) systems are becoming increasingly important for controlling power plants, distribution networks, and energy grids. Should an AI-driven energy system be successfully attacked, there could be widespread blackouts, harm to vital infrastructure, and even threats to national security. Hackers may be able to alter AI algorithms to cause equipment malfunctions, compromise energy distribution, or pilfer confidential information. Moreover, the attack surface grows as energy systems become more digitally integrated and dependent, increasing the difficulty of defending against cyber attacks.
Covid-19 Impact:
The COVID-19 pandemic had a significant effect on artificial intelligence (AI) in the energy market. It caused supply chain disruptions, project delays, and a brief decline in energy demand as a result of lockdowns and slower economic growth. But as energy companies looked to streamline operations, improve remote monitoring capabilities, and fortify themselves against future shocks, the pandemic also hastened the adoption of digital technologies, including artificial intelligence (AI). Additionally, interest in AI solutions increased during the crisis as the need for more effective energy management and the integration of renewable energy sources became even more imperative.
The Hardware segment is expected to be the largest during the forecast period
In the AI in Energy market, the hardware segment is projected to hold the largest share. Parts like sensors, CPUs, storage, and other vital infrastructure are included in this segment that is necessary for implementing AI systems. Because AI applications in energy management, smart grids, and renewable energy integration require reliable data collection, real-time processing, and storage capabilities, there is an increasing need for sophisticated hardware. Furthermore, energy companies are now the dominant segment in the market due to their increasing adoption of AI-driven solutions, which is driving up demand for sophisticated and high-performance hardware.
The Cloud-based segment is expected to have the highest CAGR during the forecast period
The AI in Energy market's cloud-based solutions segment has the highest CAGR. The growing popularity of cloud computing due to its affordability, scalability, and flexibility is the main driver of this growth. Energy companies can now use large amounts of data and sophisticated algorithms without requiring a lot of on-premise infrastructure owing to cloud-based AI platforms. Moreover, cloud solutions support collaboration across geographical boundaries and enable the integration of disparate data sources, which makes them especially appealing for managing complex energy systems and fostering innovation in fields like energy optimization and predictive maintenance.
Region with largest share:
In the AI in Energy market, North America has the largest share. A well-established energy sector, significant investments in research and development, and the region's cutting-edge technological infrastructure are all credited for this dominance. The adoption of AI technologies is leading in North America, especially the US, owing to the substantial funding from the public and private sectors, as well as the strong presence of large technology companies and creative start-ups. Additionally, AI solutions are in high demand because of the region's emphasis on modernizing infrastructure, integrating renewable energy sources, and increasing energy efficiency.
Region with highest CAGR:
The AI in Energy market is growing at the highest CAGR in the Asia-Pacific region. The region's growing industrialization, rising energy infrastructure investment, and major government programs to improve energy efficiency and incorporate renewable energy sources are the main drivers of this fast growth. In order to meet their increasing energy demands and update their energy systems, nations like China and India are setting the standard for the adoption of AI technologies. Furthermore, the adoption of AI in the region is also accelerating due to the development of smart grids, urbanization, and the push for sustainable energy practices.
Key players in the market
Some of the key players in AI in Energy market include Siemens AG, Hazama Ando Corporation, Amazon Web Services, Inc., Informatec Ltd., FlexGen Power Systems, Inc., Schneider Electric, ABB Group, General Electric, SmartCloud Inc, AppOrchid Inc, Origami Energy Ltd., Zen Robotics Ltd and Alpiq AG.
Key Developments:
In July 2024, Boson Energy and Siemens AG have signed a Memorandum of Understanding (MoU) to facilitate collaboration on technology that converts non-recyclable waste into clean energy. The collaboration aims to advance sustainable, local energy security, enabling hydrogen-powered electric vehicle charging infrastructure without compromising grid stability or impacting consumer prices.
In November 2023, Battery storage system integrator FlexGen and battery manufacturer Hithium could be supplying each other with complementary technologies for large-scale battery energy storage system (BESS) projects. FlexGen would buy up to 10GWh of Hithium battery capacity in that time, while the Chinese manufacturer would use FlexGen’s energy management system (EMS) in a combined 15GWh of projects.
In November 2023, Schneider Electric, the leader in the digital transformation of energy management and automation, today announced at its Capital Markets Day meeting with investors a $3 billion multi-year agreement with Compass Datacenters. The agreement extends the companies' existing relationship that integrates their respective supply chains to manufacture and deliver prefabricated modular data center solutions.
Component Types Covered:
• Hardware
• Solutions
• Services
Deployment Types Covered:
• On-premise
• Cloud-based
Applications Covered:
• Robotics
• Energy Management
• Renewables Management
• Demand Forecasting
• Predictive Maintenance
• Grid Optimization
• Safety and Security
• Infrastructure
• Other Applications
End Users Covered:
• Power Generation
• Oil & Gas
• Renewable Energy
• Utilities
• Other End Users
Regions Covered:
• North America
o US
o Canada
o Mexico
• Europe
o Germany
o UK
o Italy
o France
o Spain
o Rest of Europe
• Asia Pacific
o Japan
o China
o India
o Australia
o New Zealand
o South Korea
o Rest of Asia Pacific
• South America
o Argentina
o Brazil
o Chile
o Rest of South America
• Middle East & Africa
o Saudi Arabia
o UAE
o Qatar
o South Africa
o Rest of Middle East & Africa
What our report offers:
- Market share assessments for the regional and country-level segments
- Strategic recommendations for the new entrants
- Covers Market data for the years 2022, 2023, 2024, 2026, and 2030
- Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
- Strategic recommendations in key business segments based on the market estimations
- Competitive landscaping mapping the key common trends
- Company profiling with detailed strategies, financials, and recent developments
- Supply chain trends mapping the latest technological advancements
Free Customization Offerings:
All the customers of this report will be entitled to receive one of the following free customization options:
• Company Profiling
o Comprehensive profiling of additional market players (up to 3)
o SWOT Analysis of key players (up to 3)
• Regional Segmentation
o Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
• Competitive Benchmarking
Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances
Table of Contents
1 Executive Summary
2 Preface
2.1 Abstract
2.2 Stake Holders
2.3 Research Scope
2.4 Research Methodology
2.4.1 Data Mining
2.4.2 Data Analysis
2.4.3 Data Validation
2.4.4 Research Approach
2.5 Research Sources
2.5.1 Primary Research Sources
2.5.2 Secondary Research Sources
2.5.3 Assumptions
3 Market Trend Analysis
3.1 Introduction
3.2 Drivers
3.3 Restraints
3.4 Opportunities
3.5 Threats
3.6 Application Analysis
3.7 End User Analysis
3.8 Emerging Markets
3.9 Impact of Covid-19
4 Porters Five Force Analysis
4.1 Bargaining power of suppliers
4.2 Bargaining power of buyers
4.3 Threat of substitutes
4.4 Threat of new entrants
4.5 Competitive rivalry
5 Global AI in Energy Market, By Component Type
5.1 Introduction
5.2 Hardware
5.3 Solutions
5.4 Services
6 Global AI in Energy Market, By Deployment Type
6.1 Introduction
6.2 On-premise
6.3 Cloud-based
7 Global AI in Energy Market, By Application
7.1 Introduction
7.2 Robotics
7.3 Energy Management
7.4 Renewables Management
7.5 Demand Forecasting
7.6 Predictive Maintenance
7.7 Grid Optimization
7.8 Safety and Security
7.9 Infrastructure
7.10 Other Applications
8 Global AI in Energy Market, By End User
8.1 Introduction
8.2 Power Generation
8.3 Oil & Gas
8.4 Renewable Energy
8.5 Utilities
8.6 Other End Users
9 Global AI in Energy Market, By Geography
9.1 Introduction
9.2 North America
9.2.1 US
9.2.2 Canada
9.2.3 Mexico
9.3 Europe
9.3.1 Germany
9.3.2 UK
9.3.3 Italy
9.3.4 France
9.3.5 Spain
9.3.6 Rest of Europe
9.4 Asia Pacific
9.4.1 Japan
9.4.2 China
9.4.3 India
9.4.4 Australia
9.4.5 New Zealand
9.4.6 South Korea
9.4.7 Rest of Asia Pacific
9.5 South America
9.5.1 Argentina
9.5.2 Brazil
9.5.3 Chile
9.5.4 Rest of South America
9.6 Middle East & Africa
9.6.1 Saudi Arabia
9.6.2 UAE
9.6.3 Qatar
9.6.4 South Africa
9.6.5 Rest of Middle East & Africa
10 Key Developments
10.1 Agreements, Partnerships, Collaborations and Joint Ventures
10.2 Acquisitions & Mergers
10.3 New Product Launch
10.4 Expansions
10.5 Other Key Strategies
11 Company Profiling
11.1 Siemens AG
11.2 Hazama Ando Corporation
11.3 Amazon Web Services, Inc.
11.4 Informatec Ltd.
11.5 FlexGen Power Systems, Inc.
11.6 Schneider Electric
11.7 ABB Group
11.8 General Electric
11.9 SmartCloud Inc
11.10 AppOrchid Inc
11.11 Origami Energy Ltd.
11.12 Zen Robotics Ltd
11.13 Alpiq AG
List of Tables
1 Global AI in Energy Market Outlook, By Region (2022-2030) ($MN)
2 Global AI in Energy Market Outlook, By Component Type (2022-2030) ($MN)
3 Global AI in Energy Market Outlook, By Hardware (2022-2030) ($MN)
4 Global AI in Energy Market Outlook, By Solutions (2022-2030) ($MN)
5 Global AI in Energy Market Outlook, By Services (2022-2030) ($MN)
6 Global AI in Energy Market Outlook, By Deployment Type (2022-2030) ($MN)
7 Global AI in Energy Market Outlook, By On-premise (2022-2030) ($MN)
8 Global AI in Energy Market Outlook, By Cloud-based (2022-2030) ($MN)
9 Global AI in Energy Market Outlook, By Application (2022-2030) ($MN)
10 Global AI in Energy Market Outlook, By Robotics (2022-2030) ($MN)
11 Global AI in Energy Market Outlook, By Energy Management (2022-2030) ($MN)
12 Global AI in Energy Market Outlook, By Renewables Management (2022-2030) ($MN)
13 Global AI in Energy Market Outlook, By Demand Forecasting (2022-2030) ($MN)
14 Global AI in Energy Market Outlook, By Predictive Maintenance (2022-2030) ($MN)
15 Global AI in Energy Market Outlook, By Grid Optimization (2022-2030) ($MN)
16 Global AI in Energy Market Outlook, By Safety and Security (2022-2030) ($MN)
17 Global AI in Energy Market Outlook, By Infrastructure (2022-2030) ($MN)
18 Global AI in Energy Market Outlook, By Other Applications (2022-2030) ($MN)
19 Global AI in Energy Market Outlook, By End User (2022-2030) ($MN)
20 Global AI in Energy Market Outlook, By Power Generation (2022-2030) ($MN)
21 Global AI in Energy Market Outlook, By Oil & Gas (2022-2030) ($MN)
22 Global AI in Energy Market Outlook, By Renewable Energy (2022-2030) ($MN)
23 Global AI in Energy Market Outlook, By Utilities (2022-2030) ($MN)
24 Global AI in Energy Market Outlook, By Other End Users (2022-2030) ($MN)
25 North America AI in Energy Market Outlook, By Country (2022-2030) ($MN)
26 North America AI in Energy Market Outlook, By Component Type (2022-2030) ($MN)
27 North America AI in Energy Market Outlook, By Hardware (2022-2030) ($MN)
28 North America AI in Energy Market Outlook, By Solutions (2022-2030) ($MN)
29 North America AI in Energy Market Outlook, By Services (2022-2030) ($MN)
30 North America AI in Energy Market Outlook, By Deployment Type (2022-2030) ($MN)
31 North America AI in Energy Market Outlook, By On-premise (2022-2030) ($MN)
32 North America AI in Energy Market Outlook, By Cloud-based (2022-2030) ($MN)
33 North America AI in Energy Market Outlook, By Application (2022-2030) ($MN)
34 North America AI in Energy Market Outlook, By Robotics (2022-2030) ($MN)
35 North America AI in Energy Market Outlook, By Energy Management (2022-2030) ($MN)
36 North America AI in Energy Market Outlook, By Renewables Management (2022-2030) ($MN)
37 North America AI in Energy Market Outlook, By Demand Forecasting (2022-2030) ($MN)
38 North America AI in Energy Market Outlook, By Predictive Maintenance (2022-2030) ($MN)
39 North America AI in Energy Market Outlook, By Grid Optimization (2022-2030) ($MN)
40 North America AI in Energy Market Outlook, By Safety and Security (2022-2030) ($MN)
41 North America AI in Energy Market Outlook, By Infrastructure (2022-2030) ($MN)
42 North America AI in Energy Market Outlook, By Other Applications (2022-2030) ($MN)
43 North America AI in Energy Market Outlook, By End User (2022-2030) ($MN)
44 North America AI in Energy Market Outlook, By Power Generation (2022-2030) ($MN)
45 North America AI in Energy Market Outlook, By Oil & Gas (2022-2030) ($MN)
46 North America AI in Energy Market Outlook, By Renewable Energy (2022-2030) ($MN)
47 North America AI in Energy Market Outlook, By Utilities (2022-2030) ($MN)
48 North America AI in Energy Market Outlook, By Other End Users (2022-2030) ($MN)
49 Europe AI in Energy Market Outlook, By Country (2022-2030) ($MN)
50 Europe AI in Energy Market Outlook, By Component Type (2022-2030) ($MN)
51 Europe AI in Energy Market Outlook, By Hardware (2022-2030) ($MN)
52 Europe AI in Energy Market Outlook, By Solutions (2022-2030) ($MN)
53 Europe AI in Energy Market Outlook, By Services (2022-2030) ($MN)
54 Europe AI in Energy Market Outlook, By Deployment Type (2022-2030) ($MN)
55 Europe AI in Energy Market Outlook, By On-premise (2022-2030) ($MN)
56 Europe AI in Energy Market Outlook, By Cloud-based (2022-2030) ($MN)
57 Europe AI in Energy Market Outlook, By Application (2022-2030) ($MN)
58 Europe AI in Energy Market Outlook, By Robotics (2022-2030) ($MN)
59 Europe AI in Energy Market Outlook, By Energy Management (2022-2030) ($MN)
60 Europe AI in Energy Market Outlook, By Renewables Management (2022-2030) ($MN)
61 Europe AI in Energy Market Outlook, By Demand Forecasting (2022-2030) ($MN)
62 Europe AI in Energy Market Outlook, By Predictive Maintenance (2022-2030) ($MN)
63 Europe AI in Energy Market Outlook, By Grid Optimization (2022-2030) ($MN)
64 Europe AI in Energy Market Outlook, By Safety and Security (2022-2030) ($MN)
65 Europe AI in Energy Market Outlook, By Infrastructure (2022-2030) ($MN)
66 Europe AI in Energy Market Outlook, By Other Applications (2022-2030) ($MN)
67 Europe AI in Energy Market Outlook, By End User (2022-2030) ($MN)
68 Europe AI in Energy Market Outlook, By Power Generation (2022-2030) ($MN)
69 Europe AI in Energy Market Outlook, By Oil & Gas (2022-2030) ($MN)
70 Europe AI in Energy Market Outlook, By Renewable Energy (2022-2030) ($MN)
71 Europe AI in Energy Market Outlook, By Utilities (2022-2030) ($MN)
72 Europe AI in Energy Market Outlook, By Other End Users (2022-2030) ($MN)
73 Asia Pacific AI in Energy Market Outlook, By Country (2022-2030) ($MN)
74 Asia Pacific AI in Energy Market Outlook, By Component Type (2022-2030) ($MN)
75 Asia Pacific AI in Energy Market Outlook, By Hardware (2022-2030) ($MN)
76 Asia Pacific AI in Energy Market Outlook, By Solutions (2022-2030) ($MN)
77 Asia Pacific AI in Energy Market Outlook, By Services (2022-2030) ($MN)
78 Asia Pacific AI in Energy Market Outlook, By Deployment Type (2022-2030) ($MN)
79 Asia Pacific AI in Energy Market Outlook, By On-premise (2022-2030) ($MN)
80 Asia Pacific AI in Energy Market Outlook, By Cloud-based (2022-2030) ($MN)
81 Asia Pacific AI in Energy Market Outlook, By Application (2022-2030) ($MN)
82 Asia Pacific AI in Energy Market Outlook, By Robotics (2022-2030) ($MN)
83 Asia Pacific AI in Energy Market Outlook, By Energy Management (2022-2030) ($MN)
84 Asia Pacific AI in Energy Market Outlook, By Renewables Management (2022-2030) ($MN)
85 Asia Pacific AI in Energy Market Outlook, By Demand Forecasting (2022-2030) ($MN)
86 Asia Pacific AI in Energy Market Outlook, By Predictive Maintenance (2022-2030) ($MN)
87 Asia Pacific AI in Energy Market Outlook, By Grid Optimization (2022-2030) ($MN)
88 Asia Pacific AI in Energy Market Outlook, By Safety and Security (2022-2030) ($MN)
89 Asia Pacific AI in Energy Market Outlook, By Infrastructure (2022-2030) ($MN)
90 Asia Pacific AI in Energy Market Outlook, By Other Applications (2022-2030) ($MN)
91 Asia Pacific AI in Energy Market Outlook, By End User (2022-2030) ($MN)
92 Asia Pacific AI in Energy Market Outlook, By Power Generation (2022-2030) ($MN)
93 Asia Pacific AI in Energy Market Outlook, By Oil & Gas (2022-2030) ($MN)
94 Asia Pacific AI in Energy Market Outlook, By Renewable Energy (2022-2030) ($MN)
95 Asia Pacific AI in Energy Market Outlook, By Utilities (2022-2030) ($MN)
96 Asia Pacific AI in Energy Market Outlook, By Other End Users (2022-2030) ($MN)
97 South America AI in Energy Market Outlook, By Country (2022-2030) ($MN)
98 South America AI in Energy Market Outlook, By Component Type (2022-2030) ($MN)
99 South America AI in Energy Market Outlook, By Hardware (2022-2030) ($MN)
100 South America AI in Energy Market Outlook, By Solutions (2022-2030) ($MN)
101 South America AI in Energy Market Outlook, By Services (2022-2030) ($MN)
102 South America AI in Energy Market Outlook, By Deployment Type (2022-2030) ($MN)
103 South America AI in Energy Market Outlook, By On-premise (2022-2030) ($MN)
104 South America AI in Energy Market Outlook, By Cloud-based (2022-2030) ($MN)
105 South America AI in Energy Market Outlook, By Application (2022-2030) ($MN)
106 South America AI in Energy Market Outlook, By Robotics (2022-2030) ($MN)
107 South America AI in Energy Market Outlook, By Energy Management (2022-2030) ($MN)
108 South America AI in Energy Market Outlook, By Renewables Management (2022-2030) ($MN)
109 South America AI in Energy Market Outlook, By Demand Forecasting (2022-2030) ($MN)
110 South America AI in Energy Market Outlook, By Predictive Maintenance (2022-2030) ($MN)
111 South America AI in Energy Market Outlook, By Grid Optimization (2022-2030) ($MN)
112 South America AI in Energy Market Outlook, By Safety and Security (2022-2030) ($MN)
113 South America AI in Energy Market Outlook, By Infrastructure (2022-2030) ($MN)
114 South America AI in Energy Market Outlook, By Other Applications (2022-2030) ($MN)
115 South America AI in Energy Market Outlook, By End User (2022-2030) ($MN)
116 South America AI in Energy Market Outlook, By Power Generation (2022-2030) ($MN)
117 South America AI in Energy Market Outlook, By Oil & Gas (2022-2030) ($MN)
118 South America AI in Energy Market Outlook, By Renewable Energy (2022-2030) ($MN)
119 South America AI in Energy Market Outlook, By Utilities (2022-2030) ($MN)
120 South America AI in Energy Market Outlook, By Other End Users (2022-2030) ($MN)
121 Middle East & Africa AI in Energy Market Outlook, By Country (2022-2030) ($MN)
122 Middle East & Africa AI in Energy Market Outlook, By Component Type (2022-2030) ($MN)
123 Middle East & Africa AI in Energy Market Outlook, By Hardware (2022-2030) ($MN)
124 Middle East & Africa AI in Energy Market Outlook, By Solutions (2022-2030) ($MN)
125 Middle East & Africa AI in Energy Market Outlook, By Services (2022-2030) ($MN)
126 Middle East & Africa AI in Energy Market Outlook, By Deployment Type (2022-2030) ($MN)
127 Middle East & Africa AI in Energy Market Outlook, By On-premise (2022-2030) ($MN)
128 Middle East & Africa AI in Energy Market Outlook, By Cloud-based (2022-2030) ($MN)
129 Middle East & Africa AI in Energy Market Outlook, By Application (2022-2030) ($MN)
130 Middle East & Africa AI in Energy Market Outlook, By Robotics (2022-2030) ($MN)
131 Middle East & Africa AI in Energy Market Outlook, By Energy Management (2022-2030) ($MN)
132 Middle East & Africa AI in Energy Market Outlook, By Renewables Management (2022-2030) ($MN)
133 Middle East & Africa AI in Energy Market Outlook, By Demand Forecasting (2022-2030) ($MN)
134 Middle East & Africa AI in Energy Market Outlook, By Predictive Maintenance (2022-2030) ($MN)
135 Middle East & Africa AI in Energy Market Outlook, By Grid Optimization (2022-2030) ($MN)
136 Middle East & Africa AI in Energy Market Outlook, By Safety and Security (2022-2030) ($MN)
137 Middle East & Africa AI in Energy Market Outlook, By Infrastructure (2022-2030) ($MN)
138 Middle East & Africa AI in Energy Market Outlook, By Other Applications (2022-2030) ($MN)
139 Middle East & Africa AI in Energy Market Outlook, By End User (2022-2030) ($MN)
140 Middle East & Africa AI in Energy Market Outlook, By Power Generation (2022-2030) ($MN)
141 Middle East & Africa AI in Energy Market Outlook, By Oil & Gas (2022-2030) ($MN)
142 Middle East & Africa AI in Energy Market Outlook, By Renewable Energy (2022-2030) ($MN)
143 Middle East & Africa AI in Energy Market Outlook, By Utilities (2022-2030) ($MN)
144 Middle East & Africa AI in Energy Market Outlook, By Other End Users (2022-2030) ($MN)
List of Figures
RESEARCH METHODOLOGY
We at ‘Stratistics’ opt for an extensive research approach which involves data mining, data validation, and data analysis. The various research sources include in-house repository, secondary research, competitor’s sources, social media research, client internal data, and primary research.
Our team of analysts prefers the most reliable and authenticated data sources in order to perform the comprehensive literature search. With access to most of the authenticated data bases our team highly considers the best mix of information through various sources to obtain extensive and accurate analysis.
Each report takes an average time of a month and a team of 4 industry analysts. The time may vary depending on the scope and data availability of the desired market report. The various parameters used in the market assessment are standardized in order to enhance the data accuracy.
Data Mining
The data is collected from several authenticated, reliable, paid and unpaid sources and is filtered depending on the scope & objective of the research. Our reports repository acts as an added advantage in this procedure. Data gathering from the raw material suppliers, distributors and the manufacturers is performed on a regular basis, this helps in the comprehensive understanding of the products value chain. Apart from the above mentioned sources the data is also collected from the industry consultants to ensure the objective of the study is in the right direction.
Market trends such as technological advancements, regulatory affairs, market dynamics (Drivers, Restraints, Opportunities and Challenges) are obtained from scientific journals, market related national & international associations and organizations.
Data Analysis
From the data that is collected depending on the scope & objective of the research the data is subjected for the analysis. The critical steps that we follow for the data analysis include:
- Product Lifecycle Analysis
- Competitor analysis
- Risk analysis
- Porters Analysis
- PESTEL Analysis
- SWOT Analysis
The data engineering is performed by the core industry experts considering both the Marketing Mix Modeling and the Demand Forecasting. The marketing mix modeling makes use of multiple-regression techniques to predict the optimal mix of marketing variables. Regression factor is based on a number of variables and how they relate to an outcome such as sales or profits.
Data Validation
The data validation is performed by the exhaustive primary research from the expert interviews. This includes telephonic interviews, focus groups, face to face interviews, and questionnaires to validate our research from all aspects. The industry experts we approach come from the leading firms, involved in the supply chain ranging from the suppliers, distributors to the manufacturers and consumers so as to ensure an unbiased analysis.
We are in touch with more than 15,000 industry experts with the right mix of consultants, CEO's, presidents, vice presidents, managers, experts from both supply side and demand side, executives and so on.
The data validation involves the primary research from the industry experts belonging to:
- Leading Companies
- Suppliers & Distributors
- Manufacturers
- Consumers
- Industry/Strategic Consultants
Apart from the data validation the primary research also helps in performing the fill gap research, i.e. providing solutions for the unmet needs of the research which helps in enhancing the reports quality.
For more details about research methodology, kindly write to us at info@strategymrc.com
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