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Global AI in Energy Market to reach USD 14.43 billion by the end of 2029

Global AI in Energy Market Size study & Forecast, by Component Type (Solutions, Services), By Application (Robotics, Renewables Management, Demand Forecasting, Safety and Security, Infrastructure, Others), By End user (Energy Transmission, Energy Generation, Energy Distribution, Utilities), By Deployment Type (On-premise, Cloud) and Regional Analysis, 2022-2029

Product Code: EPPGS-78416882
Publish Date: 8-02-2023
Page: 200

Global AI in Energy Market is valued approximately USD 4 billion in 2021 and is anticipated to grow with a healthy growth rate of more than 17.4% over the forecast period 2022-2029. AI based solution and services are used across the power and energy sector to assist better control and management of energy consumption, and for anticipating network malfunctions & optimization. Moreover, AI is utilized towards the optimization of energy grids by managing energy flows between homes, businesses, storage batteries, renewable energy sources, microgrids, and the power grid. Additionally, based on the Predictive analytics, energy companies can access the future energy demand and can also detect energy theft and illegal taping of energy from the grid. The increasing energy demand worldwide and growing adoption of smart meters and smart home solutions are key factors accelerating the market growth.

The global energy demand is rapidly increasing due to growing expansion of urban areas & industrial activity in developing regions, which in turn contributing towards the growth of the Global AI in Energy Market. For instance, as per Statista – in 2019, the global renewable energy consumption was estimated at 74 exajoules, and the renewable energy consumption is projected to grow to 247 exajoules in 2050. Moreover, another factor accelerating the market growth is increasing investment in smart grids. For instance, according to International Energy Agency (IEA) estimates – in 2019, the United States spent around USD 71 billion in modernization of grids, and this amount further increased to USD 84 billion in 2021. Additionally, in Feb 2022, India Renewable Energy Agency (IRENA) and State Grid Corporation of China (SGCC) announced investment of USD 350 billion between 2021 & 2025 to upgrade power grid & build new power systems. Also, increasing investment towards modernization of the energy sector and rising advancements in AI & ML technologies would create lucrative growth prospectus for the market over the forecast period. However, the high cost associated with AI based services & solutions stifles market growth throughout the forecast period of 2022-2029.

The key regions considered for the Global AI in Energy Market study includes Asia Pacific, North America, Europe, Latin America, and Rest of the World. North America dominated the market in terms of revenue, owing to the dominance of leading market players and increasing investment towards modernization of energy distribution equipment in the region. Whereas Asia Pacific is expected to grow with a highest CAGR during the forecast period, owing to factors such as rising energy demand from industrial sector & favorable initiatives from government authorities towards advancements in energy sector in the region.

Major market player included in this report are:
Alpiq AG
SmartCloud Inc.
General Electric
Siemens AG
Hazama Ando Corporation
AppOrchid Inc
Zen Robotics Ltd
Schneider Electric
ABB Group
Recent Developments in the Market:
 In October 2021, Tata Power, one the largest integrated power utility company signed a three-year commercial agreement with BluWave-ai. Under this partnership, BluWave-ai would deploy a cloud platform to generate intra-day and day-ahead dispatches for use in Tata Power’s power scheduling operations.

Global AI in Energy Market Report Scope:
Historical Data 2019-2020-2021
Base Year for Estimation 2021
Forecast period 2022-2029
Report Coverage Revenue forecast, Company Ranking, Competitive Landscape, Growth factors, and Trends
Segments Covered Component Type, Application, End User, Deployment Type, Region
Regional Scope North America; Europe; Asia Pacific; Latin America; Rest of the World
Customization Scope Free report customization (equivalent up to 8 analyst’s working hours) with purchase. Addition or alteration to country, regional & segment scope*

The objective of the study is to define market sizes of different segments & countries in recent years and to forecast the values to the coming years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within countries involved in the study.

The report also caters detailed information about the crucial aspects such as driving factors & challenges which will define the future growth of the market. Additionally, it also incorporates potential opportunities in micro markets for stakeholders to invest along with the detailed analysis of competitive landscape and product offerings of key players. The detailed segments and sub-segment of the market are explained below:
By Component Type
By Application
Renewables Management
Demand Forecasting
Safety and Security
By End user
Energy Transmission
Energy Generation
Energy Distribution
By Deployment Type

By Region:
North America
Asia Pacific
South Korea
Latin America
Rest of the World

Chapter 1. Executive Summary
1.1. Market Snapshot
1.2. Global & Segmental Market Estimates & Forecasts, 2019-2029 (USD Billion)
1.2.1. AI in Energy Market, by Region, 2019-2029 (USD Billion)
1.2.2. AI in Energy Market, by Component Type, 2019-2029 (USD Billion)
1.2.3. AI in Energy Market, by Application, 2019-2029 (USD Billion)
1.2.4. AI in Energy Market, by End User, 2019-2029 (USD Billion)
1.2.5. AI in Energy Market, by Deployment Type, 2019-2029 (USD Billion)
1.3. Key Trends
1.4. Estimation Methodology
1.5. Research Assumption
Chapter 2. Global AI in Energy Market Definition and Scope
2.1. Objective of the Study
2.2. Market Definition & Scope
2.2.1. Scope of the Study
2.2.2. Industry Evolution
2.3. Years Considered for the Study
2.4. Currency Conversion Rates
Chapter 3. Global AI in Energy Market Dynamics
3.1. AI in Energy Market Impact Analysis (2019-2029)
3.1.1. Market Drivers Increasing energy demand worldwide Growing adoption of smart meters and smart home solutions
3.1.2. Market Challenges High deployment cost associated with AI solutions
3.1.3. Market Opportunities Increasing investment towards modernization of the energy sector Rising advancements in AI & ML technologies
Chapter 4. Global AI in Energy Market Industry Analysis
4.1. Porter’s 5 Force Model
4.1.1. Bargaining Power of Suppliers
4.1.2. Bargaining Power of Buyers
4.1.3. Threat of New Entrants
4.1.4. Threat of Substitutes
4.1.5. Competitive Rivalry
4.2. Futuristic Approach to Porter’s 5 Force Model (2019-2029)
4.3. PEST Analysis
4.3.1. Political
4.3.2. Economical
4.3.3. Social
4.3.4. Technological
4.4. Top investment opportunity
4.5. Top winning strategies
4.6. Industry Experts Prospective
4.7. Analyst Recommendation & Conclusion
Chapter 5. Risk Assessment: COVID-19 Impact
5.1. Assessment of the overall impact of COVID-19 on the industry
5.2. Pre COVID-19 and post COVID-19 Market scenario
Chapter 6. Global AI in Energy Market, by Component Type
6.1. Market Snapshot
6.2. Global AI in Energy Market by Component Type, Performance – Potential Analysis
6.3. Global AI in Energy Market Estimates & Forecasts by Component Type 2019-2029 (USD Billion)
6.4. AI in Energy Market, Sub Segment Analysis
6.4.1. Solution
6.4.2. Services
Chapter 7. Global AI in Energy Market, by Application
7.1. Market Snapshot
7.2. Global AI in Energy Market by Application, Performance – Potential Analysis
7.3. Global AI in Energy Market Estimates & Forecasts by Application 2019-2029 (USD Billion)
7.4. AI in Energy Market, Sub Segment Analysis
7.4.1. Robotics
7.4.2. Renewables Management
7.4.3. Demand Forecasting
7.4.4. Safety and Security
7.4.5. Infrastructure
7.4.6. Others
Chapter 8. Global AI in Energy Market, by End User
8.1. Market Snapshot
8.2. Global AI in Energy Market by End User, Performance – Potential Analysis
8.3. Global AI in Energy Market Estimates & Forecasts by End User 2019-2029 (USD Billion)
8.4. AI in Energy Market, Sub Segment Analysis
8.4.1. Energy Transmission
8.4.2. Energy Generation
8.4.3. Energy Distribution
8.4.4. Utilities
Chapter 9. Global AI in Energy Market, by Deployment Type
9.1. Market Snapshot
9.2. Global AI in Energy Market by Deployment Type, Performance – Potential Analysis
9.3. Global AI in Energy Market Estimates & Forecasts by Deployment Type 2019-2029 (USD Billion)
9.4. AI in Energy Market, Sub Segment Analysis
9.4.1. On premise
9.4.2. Cloud
Chapter 10. Global AI in Energy Market, Regional Analysis
10.1. AI in Energy Market, Regional Market Snapshot
10.2. North America AI in Energy Market
10.2.1. U.S. AI in Energy Market Component Type breakdown estimates & forecasts, 2019-2029 Application breakdown estimates & forecasts, 2019-2029 End User breakdown estimates & forecasts, 2019-2029 Deployment Type breakdown estimates & forecasts, 2019-2029
10.2.2. Canada AI in Energy Market
10.3. Europe AI in Energy Market Snapshot
10.3.1. U.K. AI in Energy Market
10.3.2. Germany AI in Energy Market
10.3.3. France AI in Energy Market
10.3.4. Spain AI in Energy Market
10.3.5. Italy AI in Energy Market
10.3.6. Rest of Europe AI in Energy Market
10.4. Asia-Pacific AI in Energy Market Snapshot
10.4.1. China AI in Energy Market
10.4.2. India AI in Energy Market
10.4.3. Japan AI in Energy Market
10.4.4. Australia AI in Energy Market
10.4.5. South Korea AI in Energy Market
10.4.6. Rest of Asia Pacific AI in Energy Market
10.5. Latin America AI in Energy Market Snapshot
10.5.1. Brazil AI in Energy Market
10.5.2. Mexico AI in Energy Market
10.5.3. Rest of Latin America AI in Energy Market
10.6. Rest of The World AI in Energy Market

Chapter 11. Competitive Intelligence
11.1. Top Market Strategies
11.2. Company Profiles
11.2.1. Alpiq AG Key Information Overview Financial (Subject to Data Availability) Product Summary Recent Developments
11.2.2. SmartCloud Inc.
11.2.3. General Electric
11.2.4. Siemens AG
11.2.5. Hazama Ando Corporation
11.2.6. ATOS SE
11.2.7. AppOrchid Inc
11.2.8. Zen Robotics Ltd
11.2.9. Schneider Electric
11.2.10. ABB Group
Chapter 12. Research Process
12.1. Research Process
12.1.1. Data Mining
12.1.2. Analysis
12.1.3. Market Estimation
12.1.4. Validation
12.1.5. Publishing
12.2. Research Attributes
12.3. Research Assumption

At Bizwit Research and Consultancy, we employ a thorough and iterative research methodology with the goal of minimizing discrepancies, ensuring the provision of highly accurate estimates and predictions over the forecast period. Our approach involves a combination of bottom-up and top-down strategies to effectively segment and estimate quantitative aspects of the market, utilizing our proprietary data & AI tools. Our Proprietary Tools allow us for the creation of customized models specific to the research objectives. This enables us to develop tailored statistical models and forecasting algorithms to estimate market trends, future growth, or consumer behavior. The customization enhances the accuracy and relevance of the research findings.
We are dedicated to clearly communicating the purpose and objectives of each research project in the final deliverables. Our process begins by identifying the specific problem or challenge our client wishes to address, and from there, we establish precise research questions that need to be answered. To gain a comprehensive understanding of the subject matter and identify the most relevant trends and best practices, we conduct an extensive review of existing literature, industry reports, case studies, and pertinent academic research.
Critical elements of methodology employed for all our studies include:
Data Collection:
To determine the appropriate methods of data collection based on the research objectives, we consider both primary and secondary sources. Primary data collection involves gathering information directly from various industry experts in core and related fields, original equipment manufacturers (OEMs), vendors, suppliers, technology developers, alliances, and organizations. These sources encompass all segments of the value chain within the specific industry. Through in-depth interviews, we engage with key industry participants, subject-matter experts, C-level executives of major market players, industry consultants, and other relevant experts. This allows us to obtain and validate critical qualitative and quantitative information while evaluating market prospects. AI and Big Data are instrumental in our primary research, providing us with powerful tools to collect, analyze, and derive insights from data efficiently. These technologies contribute to the advancement of research methodologies, enabling us to make data-driven decisions and uncover valuable findings.
In addition to primary sources, we extensively utilize secondary sources to enhance our research. These include directories, databases, journals focusing on related industries, company newsletters, and information portals such as Bloomberg, D&B Hoovers, and Factiva. These secondary sources enable us to identify and gather valuable information for our comprehensive, technical, market-oriented, and commercial study of the market. Additionally, we utilize AI algorithms to automate the collection of vast amounts of data from various sources such as surveys, social media platforms, online transactions, and web scraping. And employ Big Data technologies for storage and processing of large datasets, ensuring that no valuable information is missed during the data collection process.
Data Analysis:
Our team of experts carefully examine the gathered data using suitable statistical techniques and qualitative analysis methods. For quantitative analysis, we employ descriptive statistics, regression analysis, and other advanced statistical methods, depending on the characteristics of the data. This analysis may also incorporate the utilization of AI tools and big data analysis techniques to extract meaningful insights.
To ensure the accuracy and reliability of our findings, we extensively leverage data science techniques, which help us minimize discrepancies and uncertainties in our analysis. We employ Data Science to clean and preprocess the data, ensuring its quality and reliability. This involves handling missing data, removing outliers, standardizing variables, and transforming data into suitable formats for analysis. The application of data science techniques enhances our accuracy, efficiency, and depth of analysis, enabling us to stay competitive in dynamic market environments.
Market Size Estimation:
Our proprietary data tools play a crucial role in deriving our market estimates and forecasts. Each study involves the creation of a unique and customized model. The model incorporates the gathered information on market dynamics, technology landscape, application development, and pricing trends. AI techniques, such as machine learning and deep learning, aid us to analyze patterns within the data to identify correlations, trends, and relationships. By recognizing patterns in consumer behavior, purchasing habits, or market dynamics, our AI algorithms aid us in more precise estimations of market size. These factors are simultaneously analyzed within the model, allowing for a comprehensive assessment. To quantify their impact over the forecast period, correlation, regression, and time series analysis are employed.
To estimate and validate the market size, we employ both top-down and bottom-up approaches. The preference is given to a bottom-up approach, where key regional markets are analyzed as separate entities. This data is then integrated to obtain global estimates. This approach is crucial as it provides a deep understanding of the industry and helps minimize errors.
In our forecasting process, we consider various parameters such as economic tools, technological analysis, industry experience, and domain expertise. By taking all these factors into account, we strive to produce accurate and reliable market forecasts. When forecasting, we take into consideration several parameters, which include:
Market driving trends and favorable economic conditions
Restraints and challenges that are expected to be encountered during the forecast period.
Anticipated opportunities for growth and development
Technological advancements and projected developments in the market
Consumer spending trends and dynamics
Shifts in consumer preferences and behaviors.
The current state of raw materials and trends in supply versus pricing
Regulatory landscape and expected changes or developments.
The existing capacity in the market and any expected additions or expansions up to the end of the forecast period.
To assess the market impact of these parameters, we assign weights to each one and utilize weighted average analysis. This process allows us to quantify their influence on the market and derive an expected growth rate for the forecasted period. By considering these various factors and applying a weighted analysis approach, we strive to provide accurate and reliable market forecasts.
Insight Generation & Report Presentation:
After conducting the research, our experts analyze the findings in relation to the research objectives and the specific needs of the client. They generate valuable insights and recommendations that directly address the client’s business challenges. These insights are carefully connected to the research findings to provide a comprehensive understanding.
Next, we create a well-structured research report that effectively communicates the research findings, insights, and recommendations to the client. To enhance clarity and comprehension, we utilize visual aids such as charts, graphs, and tables. These visual elements are employed to present the data in an engaging and easily understandable format, ensuring that the information is accessible and visually appealing to the client. Our aim is to deliver a clear and concise report that conveys the research findings effectively and provides actionable recommendations to meet the client’s specific needs.

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