Artificial Intelligence in Energy Market by Application (Energy Demand Forecasting, Grid optimization & management, Energy Storage Optimization), End Use (Generation, Transmission, Distribution, Consumption) - Global Forecast to 2030
The AI in energy market is estimated at USD 8.91 billion in 2024 to USD 58.66 billion by 2030, at a Compound Annual Growth Rate (CAGR) of 36.9%. AI-based methods and ML techniques are expected to help buildings run more efficiently and provide greater comfort levels to occupants. Buildings and HVAC systems have been designed, constructed, and commissioned as fixed systems and with static environmental assumptions. This can lead to inefficiencies because building use, occupancy, and environmental factors change over time. AI can be applied to parse data collected by building systems and integrate with controls to continuously adjust setpoints to optimize HVAC performance while maintaining or improving occupant comfort. AI-based methods can provide additional. Controls to the operators, enabling increased load flexibility of buildings for participation in Virtual Power Plants (VPPs).
'By energy type, conventional energy segment to hold the largest market size during the forecast period.”
Artificial intelligence is increasingly being integrated into the more traditional energy sectors such as coal, oil, natural gas, and nuclear energy to make it much more efficient, safe, and sustainable. In fossil fuel-based energy generation, AI optimizes resource extraction, improves plant performance, and enables predictive maintenance that reduces downtime and operational costs. Using coal, oil, and natural gas, AI systems can forecast demand fluctuations, adjust supply levels, and monitor emissions, helping operators comply with environmental regulations. With nuclear energy, AI ensures safety by monitoring reactor conditions and predicting anomalies while automating response mechanisms, hence increasing the overall plant reliability. In addition, AI use supports the development of better extracting processes and fewer operational risks in other conventional energy sources, such as peat, oil shale, and tar sands, toward sustainability in energy production. In doing so, AI is redefining the conventional energy landscape, ensuring it is more efficient, safe, and environmentally friendly.
“The services segment to register the fastest growth rate during the forecast period.”
In the AI-driven energy sector, services such as training, consulting, deploying, integrating systems, supporting, and maintenance are critical for operation optimization in generation, distribution, and consumption across an entire power system. Professional services aid energy companies in identifying specific needs using AI solutions, with potential expertise in grid optimization, energy forecasting, and smart grid management. Deployment and integration services guarantee the smooth integration of AI systems with existing energy infrastructures. Support and maintenance ensure that the AI-powered solutions stay up and running with swift troubleshooting and updates, ensuring maximum uptime. Managed services allow energy companies to step back from AI solutions, as external providers handle them to improve efficiency and minimize operational costs. Together, these services empower energy organizations to use AI technologies holistically to drive operational excellence and innovation across the value chain.
“Asia Pacific to hold the highest market growth rate during the forecast period.”
In October 2023, BluWave-ai expanded its business in the Japanese market using AI-driven energy optimization technology. BluWave-ai introduced its technology from global AI deployments to enable the energy transition in Japan by optimizing energy at industrial grid-attached plants with solar generation and battery storage. It partnered with Japanese engineering companies and completed a project at an industrial R&D center. The work included optimization of rooftop solar, battery storage, and biomass generation systems. The Smart Grid Optimizer did some incredible feats such as 20% peak demand reduction, 100% utilization of renewable energy without reverse power flow and significant savings in energy costs. By November 2024, ZTE Corporation and China Mobile developed an AI-driven Green Telco Cloud that dynamically adjusts computing resources using load-based network adjustments toward making energy use in telecommunications networks optimal. In China in November 2024, ZTE Corporation and China Mobile developed an AI-driven Green Telco Cloud that makes energy use in telecommunications networks optimal with load-based network adjustments dynamically adjusting computing resources.
In-depth interviews have been conducted with chief executive officers (CEOs), Directors, and other executives from various key organizations operating in the AI in energy market.
Research Coverage
The market study covers the AI in energy market size across different segments. It aims at estimating the market size and the growth potential across various segments, including by offering (solutions and services (professional services, managed services) by energy type (conventional energy (fossil fuels, nuclear energy, other conventional energy types) renewable energy (solar, wind, hydropower, biomass, other renewable energy types) by type (Generative AI, other AI), by application (energy demand forecasting, grid optimization & management, energy storage optimization , renewables integration , energy trading & market forecasting, energy sustainability management, disaster resilience and recovery, other applications (energy theft detection and customer management)) by end use (generation, transmission , distribution, consumption(commercial, industrial)) and Region (North America, Europe, Asia Pacific, Middle East & Africa, and Latin America). The study includes an in-depth competitive analysis of the leading market players, their company profiles, key observations related to product and business offerings, recent developments, and market strategies.
Key Benefits of Buying the Report
The report will help the market leaders/new entrants with information on the closest approximations of the global AI in energy market’s revenue numbers and subsegments. This report will help stakeholders understand the competitive landscape and gain more insights to position their businesses better and plan suitable go-to-market strategies. Moreover, the report will provide insights for stakeholders to understand the market’s pulse and provide them with information on key market drivers, restraints, challenges, and opportunities.
The report provides insights on the following pointers:
Analysis of key drivers (energy market volatility and risk management, rising consumer demand for smart energy solutions, AI-Powered robots increasing energy sector worker safety), restraints (data privacy and security, high implementation cost) opportunities (increasing shift towards carbon emission reduction and sustainability, renewable energy integration), and challenges (insufficient real-time energy data limiting the training and deployment of AI models, lack of skilled professionals in AI and energy analytics.) influencing the growth of the AI in energy market.
Product Development/Innovation: Detailed insights on upcoming technologies, research & development activities, and new product & service launches in the AI in energy market.
Market Development: The report provides comprehensive information about lucrative markets and analyses the AI in energy market across various regions.
Market Diversification: Exhaustive information about new products & services, untapped geographies, recent developments, and investments in the AI in energy market.
Competitive Assessment: In-depth assessment of market shares, growth strategies and service offerings of leading include include Schneider Electric SE (France), GE Vernova (US), ABB Ltd (Switzerland), Honeywell International (US), Siemens AG (Germany), AWS (US), IBM (US), Microsoft (US), Oracle (US), Vestas Wind Systems A/S (Denmark), Atos zData (US), C3.ai (US), Tesla (US), Alpiq (Switzerland), Enel group (Italy), Origami Energy (UK), Innowatts (US), Irasus technologies (India), Grid4C (US), Uplight (US), GridBeyond (Ireland), eSmart Systems (Norway), Ndustrial (US), Datategy (France), Omdena (US).
'By energy type, conventional energy segment to hold the largest market size during the forecast period.”
Artificial intelligence is increasingly being integrated into the more traditional energy sectors such as coal, oil, natural gas, and nuclear energy to make it much more efficient, safe, and sustainable. In fossil fuel-based energy generation, AI optimizes resource extraction, improves plant performance, and enables predictive maintenance that reduces downtime and operational costs. Using coal, oil, and natural gas, AI systems can forecast demand fluctuations, adjust supply levels, and monitor emissions, helping operators comply with environmental regulations. With nuclear energy, AI ensures safety by monitoring reactor conditions and predicting anomalies while automating response mechanisms, hence increasing the overall plant reliability. In addition, AI use supports the development of better extracting processes and fewer operational risks in other conventional energy sources, such as peat, oil shale, and tar sands, toward sustainability in energy production. In doing so, AI is redefining the conventional energy landscape, ensuring it is more efficient, safe, and environmentally friendly.
“The services segment to register the fastest growth rate during the forecast period.”
In the AI-driven energy sector, services such as training, consulting, deploying, integrating systems, supporting, and maintenance are critical for operation optimization in generation, distribution, and consumption across an entire power system. Professional services aid energy companies in identifying specific needs using AI solutions, with potential expertise in grid optimization, energy forecasting, and smart grid management. Deployment and integration services guarantee the smooth integration of AI systems with existing energy infrastructures. Support and maintenance ensure that the AI-powered solutions stay up and running with swift troubleshooting and updates, ensuring maximum uptime. Managed services allow energy companies to step back from AI solutions, as external providers handle them to improve efficiency and minimize operational costs. Together, these services empower energy organizations to use AI technologies holistically to drive operational excellence and innovation across the value chain.
“Asia Pacific to hold the highest market growth rate during the forecast period.”
In October 2023, BluWave-ai expanded its business in the Japanese market using AI-driven energy optimization technology. BluWave-ai introduced its technology from global AI deployments to enable the energy transition in Japan by optimizing energy at industrial grid-attached plants with solar generation and battery storage. It partnered with Japanese engineering companies and completed a project at an industrial R&D center. The work included optimization of rooftop solar, battery storage, and biomass generation systems. The Smart Grid Optimizer did some incredible feats such as 20% peak demand reduction, 100% utilization of renewable energy without reverse power flow and significant savings in energy costs. By November 2024, ZTE Corporation and China Mobile developed an AI-driven Green Telco Cloud that dynamically adjusts computing resources using load-based network adjustments toward making energy use in telecommunications networks optimal. In China in November 2024, ZTE Corporation and China Mobile developed an AI-driven Green Telco Cloud that makes energy use in telecommunications networks optimal with load-based network adjustments dynamically adjusting computing resources.
In-depth interviews have been conducted with chief executive officers (CEOs), Directors, and other executives from various key organizations operating in the AI in energy market.
- By Company Type: Tier 1 – 40%, Tier 2 – 35%, and Tier 3 – 25%
- By Designation: Directors –25%, Managers – 35%, and Others – 40%
- By Region: North America – 37%, Europe – 42%, Asia Pacific – 21
Research Coverage
The market study covers the AI in energy market size across different segments. It aims at estimating the market size and the growth potential across various segments, including by offering (solutions and services (professional services, managed services) by energy type (conventional energy (fossil fuels, nuclear energy, other conventional energy types) renewable energy (solar, wind, hydropower, biomass, other renewable energy types) by type (Generative AI, other AI), by application (energy demand forecasting, grid optimization & management, energy storage optimization , renewables integration , energy trading & market forecasting, energy sustainability management, disaster resilience and recovery, other applications (energy theft detection and customer management)) by end use (generation, transmission , distribution, consumption(commercial, industrial)) and Region (North America, Europe, Asia Pacific, Middle East & Africa, and Latin America). The study includes an in-depth competitive analysis of the leading market players, their company profiles, key observations related to product and business offerings, recent developments, and market strategies.
Key Benefits of Buying the Report
The report will help the market leaders/new entrants with information on the closest approximations of the global AI in energy market’s revenue numbers and subsegments. This report will help stakeholders understand the competitive landscape and gain more insights to position their businesses better and plan suitable go-to-market strategies. Moreover, the report will provide insights for stakeholders to understand the market’s pulse and provide them with information on key market drivers, restraints, challenges, and opportunities.
The report provides insights on the following pointers:
Analysis of key drivers (energy market volatility and risk management, rising consumer demand for smart energy solutions, AI-Powered robots increasing energy sector worker safety), restraints (data privacy and security, high implementation cost) opportunities (increasing shift towards carbon emission reduction and sustainability, renewable energy integration), and challenges (insufficient real-time energy data limiting the training and deployment of AI models, lack of skilled professionals in AI and energy analytics.) influencing the growth of the AI in energy market.
Product Development/Innovation: Detailed insights on upcoming technologies, research & development activities, and new product & service launches in the AI in energy market.
Market Development: The report provides comprehensive information about lucrative markets and analyses the AI in energy market across various regions.
Market Diversification: Exhaustive information about new products & services, untapped geographies, recent developments, and investments in the AI in energy market.
Competitive Assessment: In-depth assessment of market shares, growth strategies and service offerings of leading include include Schneider Electric SE (France), GE Vernova (US), ABB Ltd (Switzerland), Honeywell International (US), Siemens AG (Germany), AWS (US), IBM (US), Microsoft (US), Oracle (US), Vestas Wind Systems A/S (Denmark), Atos zData (US), C3.ai (US), Tesla (US), Alpiq (Switzerland), Enel group (Italy), Origami Energy (UK), Innowatts (US), Irasus technologies (India), Grid4C (US), Uplight (US), GridBeyond (Ireland), eSmart Systems (Norway), Ndustrial (US), Datategy (France), Omdena (US).
1 INTRODUCTION
1.1 STUDY OBJECTIVES
1.2 MARKET DEFINITION
1.3 STUDY SCOPE
1.3.1 MARKET SEGMENTATION
1.3.2 INCLUSIONS AND EXCLUSIONS
1.4 YEARS CONSIDERED
1.5 CURRENCY CONSIDERED
1.6 STAKEHOLDERS
2 RESEARCH METHODOLOGY
2.1 RESEARCH DATA
2.1.1 SECONDARY DATA
2.1.2 PRIMARY DATA
2.1.2.1 Primary interviews with experts
2.1.2.2 Breakdown of primary profiles
2.1.2.3 Key insights from industry experts
2.2 MARKET SIZE ESTIMATION
2.2.1 TOP-DOWN APPROACH
2.2.2 BOTTOM-UP APPROACH
2.2.3 AI IN ENERGY MARKET ESTIMATION: DEMAND-SIDE ANALYSIS
2.3 DATA TRIANGULATION
2.4 LIMITATIONS AND RISK ASSESSMENT
2.5 RESEARCH ASSUMPTIONS
2.6 RESEARCH LIMITATIONS
3 EXECUTIVE SUMMARY
4 PREMIUM INSIGHTS
4.1 OPPORTUNITIES FOR KEY PLAYERS IN AI IN ENERGY MARKET
4.2 AI IN ENERGY MARKET, BY OFFERING
4.3 AI IN ENERGY MARKET, BY SERVICE
4.4 AI IN ENERGY MARKET, BY PROFESSIONAL SERVICE
4.5 AI IN ENERGY MARKET, BY APPLICATION
4.6 AI IN ENERGY MARKET, BY ENERGY TYPE
4.7 AI IN ENERGY MARKET, BY END USE
4.8 AI IN ENERGY MARKET, BY TYPE
4.9 NORTH AMERICA: AI IN ENERGY MARKET, BY OFFERING AND END USE
5 MARKET OVERVIEW AND INDUSTRY TRENDS
5.1 INTRODUCTION
5.2 MARKET DYNAMICS
5.2.1 DRIVERS
5.2.1.1 Energy market volatility and risk management
5.2.1.2 Rising consumer demand for smart energy solutions
5.2.1.3 AI-powered robots increasing energy sector worker safety
5.2.2 RESTRAINTS
5.2.2.1 Data privacy and security
5.2.2.2 High implementation costs
5.2.3 OPPORTUNITIES
5.2.3.1 Increasing shift toward carbon emission reduction and sustainability
5.2.3.2 Renewable energy integration
5.2.4 CHALLENGES
5.2.4.1 Insufficient real-time energy data limiting training and deployment of AI models
5.2.4.2 Lack of skilled professionals in AI and energy analytics
5.3 BRIEF HISTORY OF AI IN ENERGY MARKET
5.4 ECOSYSTEM ANALYSIS
5.5 CASE STUDY ANALYSIS
5.5.1 OPTIMIZING ENERGY EFFICIENCY ACROSS PORTFOLIOS: BLACKSTONE'S STRATEGIC PARTNERSHIP WITH SCHNEIDER ELECTRIC
5.5.2 C3 AI ENERGY MANAGEMENT PLATFORM HELPED LEADING PETROCHEMICAL COMPANY BOOST ENERGY EFFICIENCY AND ENVIRONMENTAL PERFORMANCE
5.5.3 ENVERUS INSTANT ANALYST ENABLED ENERGY COMPANIES IMPROVE DECISION-MAKING AND OPERATIONAL EFFICIENCY
5.5.4 AI-POWERED MICROGRIDS FACILITATED ENERGY RESILIENCE AND EQUITY IN REGIONAL COMMUNITIES
5.5.5 C3 AI ENERGY MANAGEMENT PLATFORM HELPED LEADING STEEL MANUFACTURER GAIN SUBSTANTIAL COST SAVINGS AND
OPERATIONAL IMPROVEMENTS
5.6 SUPPLY CHAIN ANALYSIS
5.7 TARIFF AND REGULATORY LANDSCAPE
5.7.1 TARIFF RELATED TO PROCESSORS AND CONTROLLERS (HSN: 854231)
5.7.2 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
5.7.3 KEY REGULATIONS: AI IN ENERGY
5.7.3.1 North America
5.7.3.1.1 SCR 17: Artificial Intelligence Bill (California)
5.7.3.1.2 S1103: Artificial Intelligence Automated Decision Bill (Connecticut)
5.7.3.1.3 National Artificial Intelligence Initiative Act (NAIIA)
5.7.3.1.4 The Artificial Intelligence and Data Act (AIDA) - Canada
5.7.3.2 Europe
5.7.3.2.1 European Union (EU) - Artificial Intelligence Act (AIA)
5.7.3.2.2 General Data Protection Regulation (Europe)
5.7.3.3 Asia Pacific
5.7.3.3.1 Interim Administrative Measures for Generative Artificial Intelligence Services (China)
5.7.3.3.2 National AI Strategy (Singapore)
5.7.3.3.3 Hiroshima AI Process Comprehensive Policy Framework (Japan)
5.7.3.4 Middle East & Africa
5.7.3.4.1 National Strategy for Artificial Intelligence (UAE)
5.7.3.4.2 National Artificial Intelligence Strategy (Qatar)
5.7.3.4.3 AI Ethics Principles and Guidelines (Dubai)
5.7.3.5 Latin America
5.7.3.5.1 Santiago Declaration (Chile)
5.7.3.5.2 Brazilian Artificial Intelligence Strategy (EBIA)
5.8 PRICING ANALYSIS
5.8.1 AVERAGE SELLING PRICE, BY RENEWABLE ENERGY TYPE
5.8.2 INDICATIVE PRICING ANALYSIS, BY OFFERING, 2024
5.9 TECHNOLOGY ANALYSIS
5.9.1 KEY TECHNOLOGIES
5.9.1.1 Conversational AI
5.9.1.2 Energy modeling and simulation tools
5.9.1.3 AutoML
5.9.1.4 MLOps
5.9.2 COMPLEMENTARY TECHNOLOGIES
5.9.2.1 Blockchain
5.9.2.2 Edge computing
5.9.2.3 Sensors and robotics
5.9.2.4 Cybersecurity
5.9.2.5 Big data
5.9.2.6 IoT
5.9.3 ADJACENT TECHNOLOGIES
5.9.3.1 Smart grids
5.9.3.2 Robotics
5.9.3.3 Geospatial technologies
5.10 PATENT ANALYSIS
5.10.1 LIST OF MAJOR PATENTS
5.11 PORTER’S FIVE FORCES ANALYSIS
5.11.1 THREAT OF NEW ENTRANTS
5.11.2 THREAT OF SUBSTITUTES
5.11.3 BARGAINING POWER OF BUYERS
5.11.4 BARGAINING POWER OF SUPPLIERS
5.11.5 INTENSITY OF COMPETITIVE RIVALRY
5.12 TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS
5.13 KEY STAKEHOLDERS AND BUYING CRITERIA
5.13.1 KEY STAKEHOLDERS IN BUYING PROCESS
5.13.2 BUYING CRITERIA
5.14 KEY CONFERENCES AND EVENTS, 2024–2025
5.15 TECHNOLOGY ROADMAP FOR AI IN ENERGY MARKET
5.15.1 SHORT-TERM ROADMAP (2023–2025)
5.15.2 MID-TERM ROADMAP (2026–2028)
5.15.3 LONG-TERM ROADMAP (2029–2030)
5.16 BEST PRACTICES IN AI IN ENERGY MARKET
5.16.1 ENSURE DATA QUALITY AND INTEGRATION
5.16.2 ADOPT AI-POWERED PREDICTIVE MAINTENANCE
5.16.3 FOSTER COLLABORATION AMONG STAKEHOLDERS
5.16.4 PRIORITIZE SCALABILITY AND FLEXIBILITY
5.16.5 FOCUS ON ETHICAL AI IMPLEMENTATION
5.16.6 INVEST IN AI-DRIVEN ENERGY TRADING PLATFORMS
5.16.7 IMPLEMENT AI FOR ENERGY FORECASTING AND LOAD MANAGEMENT
5.16.8 ENHANCE CUSTOMER ENGAGEMENT WITH AI SOLUTIONS
5.17 CURRENT AND EMERGING BUSINESS MODELS
5.17.1 ENERGY-AS-A-SERVICE (EAAS)
5.17.2 PREDICTIVE MAINTENANCE CONTRACTS
5.17.3 AI-DRIVEN TRADING PLATFORMS
5.17.4 GRID FLEXIBILITY SOLUTIONS
5.17.5 SUSTAINABILITY-AS-A-SERVICE
5.17.6 REMOTE ENERGY MONITORING AND MANAGEMENT
5.17.7 GREEN FINANCE AND AI-POWERED CREDIT SCORING
5.17.8 AI-BASED ENERGY EFFICIENCY AUDITS AND RETROFITTING SERVICES
5.18 AI IN ENERGY MARKET: TOOLS, FRAMEWORKS, AND TECHNIQUES
5.19 TRADE ANALYSIS (8542)
5.19.1 EXPORT SCENARIO OF PROCESSORS AND CONTROLLERS
5.19.2 IMPORT SCENARIO OF PROCESSORS AND CONTROLLERS
5.20 INVESTMENT AND FUNDING SCENARIO
5.21 IMPACT OF AI/GEN AI ON AI IN ENERGY MARKET
5.21.1 IMPACT OF AI/GEN AI ON ENERGY SECTOR
5.21.2 USE CASES OF GEN AI IN ENERGY SECTOR
6 AI IN ENERGY MARKET, BY OFFERING
6.1 INTRODUCTION
6.1.1 OFFERING: AI IN ENERGY MARKET DRIVERS
6.2 SOLUTIONS
6.2.1 AI IN ENERGY SOLUTIONS TO DRIVE EFFICIENCY, SUSTAINABILITY, AND INNOVATION
6.3 SERVICES
6.3.1 FOCUS ON CONTINUOUS MONITORING, MAINTENANCE, AND PERFORMANCE OPTIMIZATION TO BOOST MARKET
6.3.2 PROFESSIONAL SERVICES
6.3.2.1 Training & consulting
6.3.2.2 System integration & implementation
6.3.2.3 Support & maintenance
6.3.3 MANAGED SERVICES
7 AI IN ENERGY MARKET, BY ENERGY TYPE
7.1 INTRODUCTION
7.1.1 ENERGY TYPE: AI IN ENERGY MARKET DRIVERS
7.2 CONVENTIONAL ENERGY
7.2.1 ENHANCED MONITORING AND OPERATIONAL OPTIMIZATION TO PROPEL MARKET GROWTH
7.2.2 FOSSIL FUELS
7.2.2.1 Coal
7.2.2.2 Oil
7.2.2.3 Natural gas
7.2.3 NUCLEAR ENERGY
7.2.4 OTHER CONVENTIONAL ENERGY TYPES
7.3 RENEWABLE ENERGY
7.3.1 BETTER MAINTENANCE PRACTICES, RESOURCE ALLOCATION, AND INTEGRATION OF INNOVATIVE SOLUTIONS TO SUPPORT MARKET GROWTH
7.3.2 SOLAR
7.3.3 WIND
7.3.4 HYDROPOWER
7.3.5 BIOMASS
7.3.6 OTHER RENEWABLE ENERGY TYPES
8 AI IN ENERGY MARKET, BY TYPE
8.1 INTRODUCTION
8.1.1 TYPE: AI IN ENERGY MARKET DRIVERS
8.2 GENERATIVE AI
8.2.1 GENERATION OF SYNTHETIC DATA THAT MIMICS REAL-WORLD CONDITIONS TO DRIVE MARKET
8.3 OTHER AI
8.3.1 AI TECHNOLOGIES TO TRANSFORM ENERGY PROCESSES WITH SMARTER, FASTER, AND MORE ADAPTIVE SOLUTIONS
8.3.2 MACHINE LEARNING
8.3.3 NATURAL LANGUAGE PROCESSING
8.3.4 PREDICTIVE ANALYTICS
8.3.5 COMPUTER VISION
9 AI IN ENERGY MARKET, BY APPLICATION
9.1 INTRODUCTION
9.1.1 APPLICATION: AI IN ENERGY MARKET DRIVERS
9.2 ENERGY DEMAND FORECASTING
9.2.1 ALIGNING SUPPLY WITH ANTICIPATED DEMAND AND REAL-TIME DEMAND PREDICTIONS TO PROPEL MARKET GROWTH
9.3 GRID OPTIMIZATION & MANAGEMENT
9.3.1 REAL-TIME MONITORING, ANALYSIS, AND CONTROL TO HELP TRANSFORM ENERGY NETWORKS INTO INTELLIGENT SYSTEMS
9.4 ENERGY STORAGE OPTIMIZATION
9.4.1 PREDICTION OF ENERGY NEEDS AND IDENTIFICATION OF PERFORMANCE ANOMALIES IN STORAGE SYSTEMS TO AID MARKET GROWTH
9.5 RENEWABLES INTEGRATION
9.5.1 SEAMLESS INCORPORATION OF VARIABLE ENERGY SOURCES INTO POWER GRIDS TO ENSURE EFFICIENCY AND RELIABILITY
9.6 ENERGY TRADING & MARKET FORECASTING
9.6.1 CRUCIAL ROLE IN STREAMLINING OPERATIONS AND FOSTERING SUSTAINABLE ENERGY ECONOMIES TO SUPPORT MARKET GROWTH
9.7 ENERGY SUSTAINABILITY MANAGEMENT
9.7.1 REAL-TIME MONITORING OF ENERGY CONSUMPTION TO DRIVE MARKET
9.8 DISASTER RESILIENCE & RECOVERY
9.8.1 RISING DEMAND FOR MINIMIZING DOWNTIME AND ENSURING RELIABLE POWER DURING CRISES TO HELP MARKET GROWTH
9.9 OTHER APPLICATIONS
10 AI IN ENERGY MARKET, BY END USE
10.1 INTRODUCTION
10.1.1 END USE: AI IN ENERGY MARKET DRIVERS
10.2 GENERATION
10.2.1 REDUCED COSTS, ENHANCED SUSTAINABILITY, AND IMPROVED OPERATIONAL EFFICIENCY TO FOSTER MARKET GROWTH
10.3 TRANSMISSION
10.3.1 RESILIENT, SUSTAINABLE, AND SECURE ENERGY INFRASTRUCTURE TO DRIVE MARKET
10.4 DISTRIBUTION
10.4.1 OPTIMIZATION OF ENERGY DISTRIBUTION BY BALANCING LOAD DEMAND AND DETECTING FAULTS IN REAL TIME TO BOOST MARKET
10.5 CONSUMPTION
10.5.1 OPTIMIZED ENERGY USAGE, REDUCED COSTS, AND ENHANCED SUSTAINABILITY TO FUEL MARKET GROWTH
10.5.2 COMMERCIAL
10.5.3 INDUSTRIAL
11 AI IN ENERGY MARKET, BY REGION
11.1 INTRODUCTION
11.2 NORTH AMERICA
11.2.1 NORTH AMERICA: MACROECONOMIC OUTLOOK
11.2.2 US
11.2.2.1 Government initiatives and funding to boost market growth
11.2.3 CANADA
11.2.3.1 Increased focus on reducing energy consumption to fuel market growth
11.3 EUROPE
11.3.1 EUROPE: MACROECONOMIC OUTLOOK
11.3.2 GERMANY
11.3.2.1 Significant investments and collaborative projects to drive market growth
11.3.3 UK
11.3.3.1 Key investments focused on cutting emissions in energy and transportation to drive market
11.3.4 FRANCE
11.3.4.1 Increased focus on reducing environmental impact of fossil fuels to accelerate market growth
11.3.5 ITALY
11.3.5.1 Public investments and collaboration between private players to drive market
11.3.6 SPAIN
11.3.6.1 Green energy initiatives and investments to aid market growth
11.3.7 NORDICS
11.3.7.1 Innovative AI-based projects to reduce energy consumption and government initiatives driving market growth
11.3.8 REST OF EUROPE
11.4 ASIA PACIFIC
11.4.1 ASIA PACIFIC: MACROECONOMIC OUTLOOK
11.4.2 CHINA
11.4.2.1 Rising demand for energy efficiency and sustainability to fuel market growth
11.4.3 JAPAN
11.4.3.1 Initiatives for reducing fossil fuel reliance to drive sustainable market growth
11.4.4 INDIA
11.4.4.1 Government initiatives for sustainable development and efficient resource management to foster market growth
11.4.5 AUSTRALIA & NEW ZEALAND
11.4.5.1 Increasing demand for smart home energy to drive market
11.4.6 SOUTH KOREA
11.4.6.1 Transformative shift driven by AI initiatives to bolster market growth
11.4.7 ASEAN
11.4.7.1 Growing integration of AI into energy systems to drive sustainability and efficiency
11.4.8 REST OF ASIA PACIFIC
11.5 MIDDLE EAST & AFRICA
11.5.1 MIDDLE EAST & AFRICA: MACROECONOMIC OUTLOOK
11.5.1.1 KSA
11.5.1.1.1 Increasing focus on reducing transmission losses and enhancing energy efficiency goals to aid market growth
11.5.1.2 UAE
11.5.1.2.1 Increasing energy demands and focus on reducing environmental footprints to foster market growth
11.5.1.3 Kuwait
11.5.1.3.1 Rising applications of AI for enhancing asset management, operational excellence, and technical capabilities to assist market growth
11.5.1.4 Bahrain
11.5.1.4.1 Digitalization in energy sector to drive growth
11.5.1.5 South Africa
11.5.1.5.1 Increasing awareness of sustainability and government commitments to create significant growth opportunities
11.5.1.6 Rest of Middle East & Africa
11.6 LATIN AMERICA
11.6.1 LATIN AMERICA: MACROECONOMIC OUTLOOK
11.6.2 BRAZIL
11.6.2.1 Government support, technological advancements, and skilled workforce to drive market
11.6.3 ARGENTINA
11.6.3.1 Government initiatives for optimizing energy consumption and integrating renewable sources to accelerate market growth
11.6.4 MEXICO
11.6.4.1 National AI strategy and increasing demand for energy forecasting to drive market
11.6.5 REST OF LATIN AMERICA
12 COMPETITIVE LANDSCAPE
12.1 INTRODUCTION
12.2 KEY PLAYER STRATEGIES/RIGHT TO WIN, 2021–2024
12.3 MARKET SHARE ANALYSIS, 2024
12.3.1 MARKET RANKING ANALYSIS
12.4 REVENUE ANALYSIS, 2019–2023
12.5 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2024
12.5.1 STARS
12.5.2 EMERGING LEADERS
12.5.3 PERVASIVE PLAYERS
12.5.4 PARTICIPANTS
12.5.5 COMPANY FOOTPRINT: KEY PLAYERS, 2024
12.5.5.1 Company footprint
12.5.5.2 Region footprint
12.5.5.3 Offering footprint
12.5.5.4 Energy type footprint
12.5.5.5 Type footprint
12.5.5.6 Application footprint
12.5.5.7 End-use footprint
12.6 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2024
12.6.1 PROGRESSIVE COMPANIES
12.6.2 RESPONSIVE COMPANIES
12.6.3 DYNAMIC COMPANIES
12.6.4 STARTING BLOCKS
12.6.5 COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2024
12.6.5.1 Detailed list of key startups/SMEs
12.6.5.2 Competitive benchmarking of key startups/SMEs
12.7 COMPETITIVE SCENARIO
12.7.1 PRODUCT LAUNCHES AND ENHANCEMENTS
12.7.2 DEALS
12.8 BRAND/PRODUCT COMPARISON
12.9 COMPANY VALUATION AND FINANCIAL METRICS
13 COMPANY PROFILES
13.1 KEY PLAYERS
13.1.1 SCHNEIDER ELECTRIC SE
13.1.1.1 Business overview
13.1.1.2 Products/Solutions/Services offered
13.1.1.3 Recent developments
13.1.1.3.1 Product launches and enhancements
13.1.1.3.2 Deals
13.1.1.4 MnM view
13.1.1.4.1 Key strengths
13.1.1.4.2 Strategic choices
13.1.1.4.3 Weaknesses and competitive threats
13.1.2 GE VERNOVA
13.1.2.1 Business overview
13.1.2.2 Products/Solutions/Services offered
13.1.2.3 Recent developments
13.1.2.3.1 Product launches and enhancements
13.1.2.3.2 Deals
13.1.2.4 MnM view
13.1.2.4.1 Key strengths
13.1.2.4.2 Strategic choices
13.1.2.4.3 Weaknesses and competitive threats
13.1.3 ABB LTD.
13.1.3.1 Business overview
13.1.3.2 Products/Solutions/Services offered
13.1.3.3 Recent developments
13.1.3.3.1 Deals
13.1.3.4 MnM view
13.1.3.4.1 Key strengths
13.1.3.4.2 Strategic choices
13.1.3.4.3 Weaknesses and competitive threats
13.1.4 HONEYWELL INTERNATIONAL, INC.
13.1.4.1 Business overview
13.1.4.2 Products/Solutions/Services offered
13.1.4.3 Recent developments
13.1.4.3.1 Product launches and enhancements
13.1.4.3.2 Deals
13.1.4.4 MnM view
13.1.4.4.1 Key strengths
13.1.4.4.2 Strategic choices
13.1.4.4.3 Weaknesses and competitive threats
13.1.5 SIEMENS AG
13.1.5.1 Business overview
13.1.5.2 Products/Solutions/Services offered
13.1.5.3 Recent developments
13.1.5.3.1 Deals
13.1.5.4 MnM view
13.1.5.4.1 Key strengths
13.1.5.4.2 Strategic choices
13.1.5.4.3 Weaknesses and competitive threats
13.1.6 ORACLE CORPORATION
13.1.6.1 Business overview
13.1.6.2 Products/Solutions/Services offered
13.1.6.3 Recent developments
13.1.6.3.1 Deals
13.1.7 VESTAS WIND SYSTEMS A/S
13.1.7.1 Business overview
13.1.7.2 Products/Solutions/Services offered
13.1.7.3 Recent developments
13.1.7.3.1 Deals
13.1.8 IBM CORPORATION
13.1.8.1 Business overview
13.1.8.2 Products/Solutions/Services offered
13.1.8.3 Recent developments
13.1.8.3.1 Deals
13.1.9 MICROSOFT CORPORATION, INC.
13.1.9.1 Business overview
13.1.9.2 Products/Solutions/Services offered
13.1.9.3 Recent developments
13.1.9.3.1 Deals
13.1.10 AMAZON WEB SERVICES, INC
13.1.10.1 Business overview
13.1.10.2 Products/Solutions/Services offered
13.1.10.3 Recent developments
13.1.10.3.1 Deals
13.1.11 ATOS SE
13.1.11.1 Business overview
13.1.11.2 Products/Solutions/Services offered
13.1.11.3 Recent developments
13.1.11.3.1 Product launches and enhancements
13.1.11.3.2 Deals
13.1.12 TESLA, INC.
13.1.13 C3.AI, INC.
13.1.14 ALPIQ
13.1.15 ENEL S.P.A.
13.2 STARTUPS/SMES
13.2.1 ORIGAMI ENERGY
13.2.2 INNOWATTS
13.2.3 IRASUS TECHNOLOGIES
13.2.4 GRID4C
13.2.5 UPLIGHT
13.2.6 GRIDBEYOND
13.2.7 ESMART SYSTEMS
13.2.8 NDUSTRIAL
13.2.9 DATATEGY
13.2.10 OMDENA
13.2.11 BIDGELY
13.2.12 AVATHON
14 ADJACENT/RELATED MARKETS
14.1 INTRODUCTION
14.2 CONVERSATIONAL AI MARKET
14.2.1 MARKET OVERVIEW
14.2.2 CONVERSATIONAL AI MARKET, BY OFFERING
14.3 SERVICES
14.3.1 CONVERSATIONAL AI MARKET, BY SERVICE
14.3.2 CONVERSATIONAL AI MARKET, BY BUSINESS FUNCTION
14.3.3 CONVERSATIONAL AI MARKET, BY INTEGRATION MODE
14.3.4 CONVERSATIONAL AI MARKET, BY VERTICAL
14.4 CUSTOMER EXPERIENCE MANAGEMENT MARKET
14.4.1 MARKET DEFINITION
14.4.2 MARKET OVERVIEW
14.4.3 CUSTOMER EXPERIENCE MANAGEMENT MARKET, BY OFFERING
14.4.4 CUSTOMER EXPERIENCE MANAGEMENT MARKET, BY DEPLOYMENT TYPE
14.4.5 CUSTOMER EXPERIENCE MANAGEMENT MARKET, BY ORGANIZATION SIZE
14.4.6 CUSTOMER EXPERIENCE MANAGEMENT MARKET, BY VERTICAL
15 APPENDIX
15.1 DISCUSSION GUIDE
15.2 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL
15.3 CUSTOMIZATION OPTIONS
15.4 RELATED REPORTS
15.5 AUTHOR DETAILS
1.1 STUDY OBJECTIVES
1.2 MARKET DEFINITION
1.3 STUDY SCOPE
1.3.1 MARKET SEGMENTATION
1.3.2 INCLUSIONS AND EXCLUSIONS
1.4 YEARS CONSIDERED
1.5 CURRENCY CONSIDERED
1.6 STAKEHOLDERS
2 RESEARCH METHODOLOGY
2.1 RESEARCH DATA
2.1.1 SECONDARY DATA
2.1.2 PRIMARY DATA
2.1.2.1 Primary interviews with experts
2.1.2.2 Breakdown of primary profiles
2.1.2.3 Key insights from industry experts
2.2 MARKET SIZE ESTIMATION
2.2.1 TOP-DOWN APPROACH
2.2.2 BOTTOM-UP APPROACH
2.2.3 AI IN ENERGY MARKET ESTIMATION: DEMAND-SIDE ANALYSIS
2.3 DATA TRIANGULATION
2.4 LIMITATIONS AND RISK ASSESSMENT
2.5 RESEARCH ASSUMPTIONS
2.6 RESEARCH LIMITATIONS
3 EXECUTIVE SUMMARY
4 PREMIUM INSIGHTS
4.1 OPPORTUNITIES FOR KEY PLAYERS IN AI IN ENERGY MARKET
4.2 AI IN ENERGY MARKET, BY OFFERING
4.3 AI IN ENERGY MARKET, BY SERVICE
4.4 AI IN ENERGY MARKET, BY PROFESSIONAL SERVICE
4.5 AI IN ENERGY MARKET, BY APPLICATION
4.6 AI IN ENERGY MARKET, BY ENERGY TYPE
4.7 AI IN ENERGY MARKET, BY END USE
4.8 AI IN ENERGY MARKET, BY TYPE
4.9 NORTH AMERICA: AI IN ENERGY MARKET, BY OFFERING AND END USE
5 MARKET OVERVIEW AND INDUSTRY TRENDS
5.1 INTRODUCTION
5.2 MARKET DYNAMICS
5.2.1 DRIVERS
5.2.1.1 Energy market volatility and risk management
5.2.1.2 Rising consumer demand for smart energy solutions
5.2.1.3 AI-powered robots increasing energy sector worker safety
5.2.2 RESTRAINTS
5.2.2.1 Data privacy and security
5.2.2.2 High implementation costs
5.2.3 OPPORTUNITIES
5.2.3.1 Increasing shift toward carbon emission reduction and sustainability
5.2.3.2 Renewable energy integration
5.2.4 CHALLENGES
5.2.4.1 Insufficient real-time energy data limiting training and deployment of AI models
5.2.4.2 Lack of skilled professionals in AI and energy analytics
5.3 BRIEF HISTORY OF AI IN ENERGY MARKET
5.4 ECOSYSTEM ANALYSIS
5.5 CASE STUDY ANALYSIS
5.5.1 OPTIMIZING ENERGY EFFICIENCY ACROSS PORTFOLIOS: BLACKSTONE'S STRATEGIC PARTNERSHIP WITH SCHNEIDER ELECTRIC
5.5.2 C3 AI ENERGY MANAGEMENT PLATFORM HELPED LEADING PETROCHEMICAL COMPANY BOOST ENERGY EFFICIENCY AND ENVIRONMENTAL PERFORMANCE
5.5.3 ENVERUS INSTANT ANALYST ENABLED ENERGY COMPANIES IMPROVE DECISION-MAKING AND OPERATIONAL EFFICIENCY
5.5.4 AI-POWERED MICROGRIDS FACILITATED ENERGY RESILIENCE AND EQUITY IN REGIONAL COMMUNITIES
5.5.5 C3 AI ENERGY MANAGEMENT PLATFORM HELPED LEADING STEEL MANUFACTURER GAIN SUBSTANTIAL COST SAVINGS AND
OPERATIONAL IMPROVEMENTS
5.6 SUPPLY CHAIN ANALYSIS
5.7 TARIFF AND REGULATORY LANDSCAPE
5.7.1 TARIFF RELATED TO PROCESSORS AND CONTROLLERS (HSN: 854231)
5.7.2 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
5.7.3 KEY REGULATIONS: AI IN ENERGY
5.7.3.1 North America
5.7.3.1.1 SCR 17: Artificial Intelligence Bill (California)
5.7.3.1.2 S1103: Artificial Intelligence Automated Decision Bill (Connecticut)
5.7.3.1.3 National Artificial Intelligence Initiative Act (NAIIA)
5.7.3.1.4 The Artificial Intelligence and Data Act (AIDA) - Canada
5.7.3.2 Europe
5.7.3.2.1 European Union (EU) - Artificial Intelligence Act (AIA)
5.7.3.2.2 General Data Protection Regulation (Europe)
5.7.3.3 Asia Pacific
5.7.3.3.1 Interim Administrative Measures for Generative Artificial Intelligence Services (China)
5.7.3.3.2 National AI Strategy (Singapore)
5.7.3.3.3 Hiroshima AI Process Comprehensive Policy Framework (Japan)
5.7.3.4 Middle East & Africa
5.7.3.4.1 National Strategy for Artificial Intelligence (UAE)
5.7.3.4.2 National Artificial Intelligence Strategy (Qatar)
5.7.3.4.3 AI Ethics Principles and Guidelines (Dubai)
5.7.3.5 Latin America
5.7.3.5.1 Santiago Declaration (Chile)
5.7.3.5.2 Brazilian Artificial Intelligence Strategy (EBIA)
5.8 PRICING ANALYSIS
5.8.1 AVERAGE SELLING PRICE, BY RENEWABLE ENERGY TYPE
5.8.2 INDICATIVE PRICING ANALYSIS, BY OFFERING, 2024
5.9 TECHNOLOGY ANALYSIS
5.9.1 KEY TECHNOLOGIES
5.9.1.1 Conversational AI
5.9.1.2 Energy modeling and simulation tools
5.9.1.3 AutoML
5.9.1.4 MLOps
5.9.2 COMPLEMENTARY TECHNOLOGIES
5.9.2.1 Blockchain
5.9.2.2 Edge computing
5.9.2.3 Sensors and robotics
5.9.2.4 Cybersecurity
5.9.2.5 Big data
5.9.2.6 IoT
5.9.3 ADJACENT TECHNOLOGIES
5.9.3.1 Smart grids
5.9.3.2 Robotics
5.9.3.3 Geospatial technologies
5.10 PATENT ANALYSIS
5.10.1 LIST OF MAJOR PATENTS
5.11 PORTER’S FIVE FORCES ANALYSIS
5.11.1 THREAT OF NEW ENTRANTS
5.11.2 THREAT OF SUBSTITUTES
5.11.3 BARGAINING POWER OF BUYERS
5.11.4 BARGAINING POWER OF SUPPLIERS
5.11.5 INTENSITY OF COMPETITIVE RIVALRY
5.12 TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS
5.13 KEY STAKEHOLDERS AND BUYING CRITERIA
5.13.1 KEY STAKEHOLDERS IN BUYING PROCESS
5.13.2 BUYING CRITERIA
5.14 KEY CONFERENCES AND EVENTS, 2024–2025
5.15 TECHNOLOGY ROADMAP FOR AI IN ENERGY MARKET
5.15.1 SHORT-TERM ROADMAP (2023–2025)
5.15.2 MID-TERM ROADMAP (2026–2028)
5.15.3 LONG-TERM ROADMAP (2029–2030)
5.16 BEST PRACTICES IN AI IN ENERGY MARKET
5.16.1 ENSURE DATA QUALITY AND INTEGRATION
5.16.2 ADOPT AI-POWERED PREDICTIVE MAINTENANCE
5.16.3 FOSTER COLLABORATION AMONG STAKEHOLDERS
5.16.4 PRIORITIZE SCALABILITY AND FLEXIBILITY
5.16.5 FOCUS ON ETHICAL AI IMPLEMENTATION
5.16.6 INVEST IN AI-DRIVEN ENERGY TRADING PLATFORMS
5.16.7 IMPLEMENT AI FOR ENERGY FORECASTING AND LOAD MANAGEMENT
5.16.8 ENHANCE CUSTOMER ENGAGEMENT WITH AI SOLUTIONS
5.17 CURRENT AND EMERGING BUSINESS MODELS
5.17.1 ENERGY-AS-A-SERVICE (EAAS)
5.17.2 PREDICTIVE MAINTENANCE CONTRACTS
5.17.3 AI-DRIVEN TRADING PLATFORMS
5.17.4 GRID FLEXIBILITY SOLUTIONS
5.17.5 SUSTAINABILITY-AS-A-SERVICE
5.17.6 REMOTE ENERGY MONITORING AND MANAGEMENT
5.17.7 GREEN FINANCE AND AI-POWERED CREDIT SCORING
5.17.8 AI-BASED ENERGY EFFICIENCY AUDITS AND RETROFITTING SERVICES
5.18 AI IN ENERGY MARKET: TOOLS, FRAMEWORKS, AND TECHNIQUES
5.19 TRADE ANALYSIS (8542)
5.19.1 EXPORT SCENARIO OF PROCESSORS AND CONTROLLERS
5.19.2 IMPORT SCENARIO OF PROCESSORS AND CONTROLLERS
5.20 INVESTMENT AND FUNDING SCENARIO
5.21 IMPACT OF AI/GEN AI ON AI IN ENERGY MARKET
5.21.1 IMPACT OF AI/GEN AI ON ENERGY SECTOR
5.21.2 USE CASES OF GEN AI IN ENERGY SECTOR
6 AI IN ENERGY MARKET, BY OFFERING
6.1 INTRODUCTION
6.1.1 OFFERING: AI IN ENERGY MARKET DRIVERS
6.2 SOLUTIONS
6.2.1 AI IN ENERGY SOLUTIONS TO DRIVE EFFICIENCY, SUSTAINABILITY, AND INNOVATION
6.3 SERVICES
6.3.1 FOCUS ON CONTINUOUS MONITORING, MAINTENANCE, AND PERFORMANCE OPTIMIZATION TO BOOST MARKET
6.3.2 PROFESSIONAL SERVICES
6.3.2.1 Training & consulting
6.3.2.2 System integration & implementation
6.3.2.3 Support & maintenance
6.3.3 MANAGED SERVICES
7 AI IN ENERGY MARKET, BY ENERGY TYPE
7.1 INTRODUCTION
7.1.1 ENERGY TYPE: AI IN ENERGY MARKET DRIVERS
7.2 CONVENTIONAL ENERGY
7.2.1 ENHANCED MONITORING AND OPERATIONAL OPTIMIZATION TO PROPEL MARKET GROWTH
7.2.2 FOSSIL FUELS
7.2.2.1 Coal
7.2.2.2 Oil
7.2.2.3 Natural gas
7.2.3 NUCLEAR ENERGY
7.2.4 OTHER CONVENTIONAL ENERGY TYPES
7.3 RENEWABLE ENERGY
7.3.1 BETTER MAINTENANCE PRACTICES, RESOURCE ALLOCATION, AND INTEGRATION OF INNOVATIVE SOLUTIONS TO SUPPORT MARKET GROWTH
7.3.2 SOLAR
7.3.3 WIND
7.3.4 HYDROPOWER
7.3.5 BIOMASS
7.3.6 OTHER RENEWABLE ENERGY TYPES
8 AI IN ENERGY MARKET, BY TYPE
8.1 INTRODUCTION
8.1.1 TYPE: AI IN ENERGY MARKET DRIVERS
8.2 GENERATIVE AI
8.2.1 GENERATION OF SYNTHETIC DATA THAT MIMICS REAL-WORLD CONDITIONS TO DRIVE MARKET
8.3 OTHER AI
8.3.1 AI TECHNOLOGIES TO TRANSFORM ENERGY PROCESSES WITH SMARTER, FASTER, AND MORE ADAPTIVE SOLUTIONS
8.3.2 MACHINE LEARNING
8.3.3 NATURAL LANGUAGE PROCESSING
8.3.4 PREDICTIVE ANALYTICS
8.3.5 COMPUTER VISION
9 AI IN ENERGY MARKET, BY APPLICATION
9.1 INTRODUCTION
9.1.1 APPLICATION: AI IN ENERGY MARKET DRIVERS
9.2 ENERGY DEMAND FORECASTING
9.2.1 ALIGNING SUPPLY WITH ANTICIPATED DEMAND AND REAL-TIME DEMAND PREDICTIONS TO PROPEL MARKET GROWTH
9.3 GRID OPTIMIZATION & MANAGEMENT
9.3.1 REAL-TIME MONITORING, ANALYSIS, AND CONTROL TO HELP TRANSFORM ENERGY NETWORKS INTO INTELLIGENT SYSTEMS
9.4 ENERGY STORAGE OPTIMIZATION
9.4.1 PREDICTION OF ENERGY NEEDS AND IDENTIFICATION OF PERFORMANCE ANOMALIES IN STORAGE SYSTEMS TO AID MARKET GROWTH
9.5 RENEWABLES INTEGRATION
9.5.1 SEAMLESS INCORPORATION OF VARIABLE ENERGY SOURCES INTO POWER GRIDS TO ENSURE EFFICIENCY AND RELIABILITY
9.6 ENERGY TRADING & MARKET FORECASTING
9.6.1 CRUCIAL ROLE IN STREAMLINING OPERATIONS AND FOSTERING SUSTAINABLE ENERGY ECONOMIES TO SUPPORT MARKET GROWTH
9.7 ENERGY SUSTAINABILITY MANAGEMENT
9.7.1 REAL-TIME MONITORING OF ENERGY CONSUMPTION TO DRIVE MARKET
9.8 DISASTER RESILIENCE & RECOVERY
9.8.1 RISING DEMAND FOR MINIMIZING DOWNTIME AND ENSURING RELIABLE POWER DURING CRISES TO HELP MARKET GROWTH
9.9 OTHER APPLICATIONS
10 AI IN ENERGY MARKET, BY END USE
10.1 INTRODUCTION
10.1.1 END USE: AI IN ENERGY MARKET DRIVERS
10.2 GENERATION
10.2.1 REDUCED COSTS, ENHANCED SUSTAINABILITY, AND IMPROVED OPERATIONAL EFFICIENCY TO FOSTER MARKET GROWTH
10.3 TRANSMISSION
10.3.1 RESILIENT, SUSTAINABLE, AND SECURE ENERGY INFRASTRUCTURE TO DRIVE MARKET
10.4 DISTRIBUTION
10.4.1 OPTIMIZATION OF ENERGY DISTRIBUTION BY BALANCING LOAD DEMAND AND DETECTING FAULTS IN REAL TIME TO BOOST MARKET
10.5 CONSUMPTION
10.5.1 OPTIMIZED ENERGY USAGE, REDUCED COSTS, AND ENHANCED SUSTAINABILITY TO FUEL MARKET GROWTH
10.5.2 COMMERCIAL
10.5.3 INDUSTRIAL
11 AI IN ENERGY MARKET, BY REGION
11.1 INTRODUCTION
11.2 NORTH AMERICA
11.2.1 NORTH AMERICA: MACROECONOMIC OUTLOOK
11.2.2 US
11.2.2.1 Government initiatives and funding to boost market growth
11.2.3 CANADA
11.2.3.1 Increased focus on reducing energy consumption to fuel market growth
11.3 EUROPE
11.3.1 EUROPE: MACROECONOMIC OUTLOOK
11.3.2 GERMANY
11.3.2.1 Significant investments and collaborative projects to drive market growth
11.3.3 UK
11.3.3.1 Key investments focused on cutting emissions in energy and transportation to drive market
11.3.4 FRANCE
11.3.4.1 Increased focus on reducing environmental impact of fossil fuels to accelerate market growth
11.3.5 ITALY
11.3.5.1 Public investments and collaboration between private players to drive market
11.3.6 SPAIN
11.3.6.1 Green energy initiatives and investments to aid market growth
11.3.7 NORDICS
11.3.7.1 Innovative AI-based projects to reduce energy consumption and government initiatives driving market growth
11.3.8 REST OF EUROPE
11.4 ASIA PACIFIC
11.4.1 ASIA PACIFIC: MACROECONOMIC OUTLOOK
11.4.2 CHINA
11.4.2.1 Rising demand for energy efficiency and sustainability to fuel market growth
11.4.3 JAPAN
11.4.3.1 Initiatives for reducing fossil fuel reliance to drive sustainable market growth
11.4.4 INDIA
11.4.4.1 Government initiatives for sustainable development and efficient resource management to foster market growth
11.4.5 AUSTRALIA & NEW ZEALAND
11.4.5.1 Increasing demand for smart home energy to drive market
11.4.6 SOUTH KOREA
11.4.6.1 Transformative shift driven by AI initiatives to bolster market growth
11.4.7 ASEAN
11.4.7.1 Growing integration of AI into energy systems to drive sustainability and efficiency
11.4.8 REST OF ASIA PACIFIC
11.5 MIDDLE EAST & AFRICA
11.5.1 MIDDLE EAST & AFRICA: MACROECONOMIC OUTLOOK
11.5.1.1 KSA
11.5.1.1.1 Increasing focus on reducing transmission losses and enhancing energy efficiency goals to aid market growth
11.5.1.2 UAE
11.5.1.2.1 Increasing energy demands and focus on reducing environmental footprints to foster market growth
11.5.1.3 Kuwait
11.5.1.3.1 Rising applications of AI for enhancing asset management, operational excellence, and technical capabilities to assist market growth
11.5.1.4 Bahrain
11.5.1.4.1 Digitalization in energy sector to drive growth
11.5.1.5 South Africa
11.5.1.5.1 Increasing awareness of sustainability and government commitments to create significant growth opportunities
11.5.1.6 Rest of Middle East & Africa
11.6 LATIN AMERICA
11.6.1 LATIN AMERICA: MACROECONOMIC OUTLOOK
11.6.2 BRAZIL
11.6.2.1 Government support, technological advancements, and skilled workforce to drive market
11.6.3 ARGENTINA
11.6.3.1 Government initiatives for optimizing energy consumption and integrating renewable sources to accelerate market growth
11.6.4 MEXICO
11.6.4.1 National AI strategy and increasing demand for energy forecasting to drive market
11.6.5 REST OF LATIN AMERICA
12 COMPETITIVE LANDSCAPE
12.1 INTRODUCTION
12.2 KEY PLAYER STRATEGIES/RIGHT TO WIN, 2021–2024
12.3 MARKET SHARE ANALYSIS, 2024
12.3.1 MARKET RANKING ANALYSIS
12.4 REVENUE ANALYSIS, 2019–2023
12.5 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2024
12.5.1 STARS
12.5.2 EMERGING LEADERS
12.5.3 PERVASIVE PLAYERS
12.5.4 PARTICIPANTS
12.5.5 COMPANY FOOTPRINT: KEY PLAYERS, 2024
12.5.5.1 Company footprint
12.5.5.2 Region footprint
12.5.5.3 Offering footprint
12.5.5.4 Energy type footprint
12.5.5.5 Type footprint
12.5.5.6 Application footprint
12.5.5.7 End-use footprint
12.6 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2024
12.6.1 PROGRESSIVE COMPANIES
12.6.2 RESPONSIVE COMPANIES
12.6.3 DYNAMIC COMPANIES
12.6.4 STARTING BLOCKS
12.6.5 COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2024
12.6.5.1 Detailed list of key startups/SMEs
12.6.5.2 Competitive benchmarking of key startups/SMEs
12.7 COMPETITIVE SCENARIO
12.7.1 PRODUCT LAUNCHES AND ENHANCEMENTS
12.7.2 DEALS
12.8 BRAND/PRODUCT COMPARISON
12.9 COMPANY VALUATION AND FINANCIAL METRICS
13 COMPANY PROFILES
13.1 KEY PLAYERS
13.1.1 SCHNEIDER ELECTRIC SE
13.1.1.1 Business overview
13.1.1.2 Products/Solutions/Services offered
13.1.1.3 Recent developments
13.1.1.3.1 Product launches and enhancements
13.1.1.3.2 Deals
13.1.1.4 MnM view
13.1.1.4.1 Key strengths
13.1.1.4.2 Strategic choices
13.1.1.4.3 Weaknesses and competitive threats
13.1.2 GE VERNOVA
13.1.2.1 Business overview
13.1.2.2 Products/Solutions/Services offered
13.1.2.3 Recent developments
13.1.2.3.1 Product launches and enhancements
13.1.2.3.2 Deals
13.1.2.4 MnM view
13.1.2.4.1 Key strengths
13.1.2.4.2 Strategic choices
13.1.2.4.3 Weaknesses and competitive threats
13.1.3 ABB LTD.
13.1.3.1 Business overview
13.1.3.2 Products/Solutions/Services offered
13.1.3.3 Recent developments
13.1.3.3.1 Deals
13.1.3.4 MnM view
13.1.3.4.1 Key strengths
13.1.3.4.2 Strategic choices
13.1.3.4.3 Weaknesses and competitive threats
13.1.4 HONEYWELL INTERNATIONAL, INC.
13.1.4.1 Business overview
13.1.4.2 Products/Solutions/Services offered
13.1.4.3 Recent developments
13.1.4.3.1 Product launches and enhancements
13.1.4.3.2 Deals
13.1.4.4 MnM view
13.1.4.4.1 Key strengths
13.1.4.4.2 Strategic choices
13.1.4.4.3 Weaknesses and competitive threats
13.1.5 SIEMENS AG
13.1.5.1 Business overview
13.1.5.2 Products/Solutions/Services offered
13.1.5.3 Recent developments
13.1.5.3.1 Deals
13.1.5.4 MnM view
13.1.5.4.1 Key strengths
13.1.5.4.2 Strategic choices
13.1.5.4.3 Weaknesses and competitive threats
13.1.6 ORACLE CORPORATION
13.1.6.1 Business overview
13.1.6.2 Products/Solutions/Services offered
13.1.6.3 Recent developments
13.1.6.3.1 Deals
13.1.7 VESTAS WIND SYSTEMS A/S
13.1.7.1 Business overview
13.1.7.2 Products/Solutions/Services offered
13.1.7.3 Recent developments
13.1.7.3.1 Deals
13.1.8 IBM CORPORATION
13.1.8.1 Business overview
13.1.8.2 Products/Solutions/Services offered
13.1.8.3 Recent developments
13.1.8.3.1 Deals
13.1.9 MICROSOFT CORPORATION, INC.
13.1.9.1 Business overview
13.1.9.2 Products/Solutions/Services offered
13.1.9.3 Recent developments
13.1.9.3.1 Deals
13.1.10 AMAZON WEB SERVICES, INC
13.1.10.1 Business overview
13.1.10.2 Products/Solutions/Services offered
13.1.10.3 Recent developments
13.1.10.3.1 Deals
13.1.11 ATOS SE
13.1.11.1 Business overview
13.1.11.2 Products/Solutions/Services offered
13.1.11.3 Recent developments
13.1.11.3.1 Product launches and enhancements
13.1.11.3.2 Deals
13.1.12 TESLA, INC.
13.1.13 C3.AI, INC.
13.1.14 ALPIQ
13.1.15 ENEL S.P.A.
13.2 STARTUPS/SMES
13.2.1 ORIGAMI ENERGY
13.2.2 INNOWATTS
13.2.3 IRASUS TECHNOLOGIES
13.2.4 GRID4C
13.2.5 UPLIGHT
13.2.6 GRIDBEYOND
13.2.7 ESMART SYSTEMS
13.2.8 NDUSTRIAL
13.2.9 DATATEGY
13.2.10 OMDENA
13.2.11 BIDGELY
13.2.12 AVATHON
14 ADJACENT/RELATED MARKETS
14.1 INTRODUCTION
14.2 CONVERSATIONAL AI MARKET
14.2.1 MARKET OVERVIEW
14.2.2 CONVERSATIONAL AI MARKET, BY OFFERING
14.3 SERVICES
14.3.1 CONVERSATIONAL AI MARKET, BY SERVICE
14.3.2 CONVERSATIONAL AI MARKET, BY BUSINESS FUNCTION
14.3.3 CONVERSATIONAL AI MARKET, BY INTEGRATION MODE
14.3.4 CONVERSATIONAL AI MARKET, BY VERTICAL
14.4 CUSTOMER EXPERIENCE MANAGEMENT MARKET
14.4.1 MARKET DEFINITION
14.4.2 MARKET OVERVIEW
14.4.3 CUSTOMER EXPERIENCE MANAGEMENT MARKET, BY OFFERING
14.4.4 CUSTOMER EXPERIENCE MANAGEMENT MARKET, BY DEPLOYMENT TYPE
14.4.5 CUSTOMER EXPERIENCE MANAGEMENT MARKET, BY ORGANIZATION SIZE
14.4.6 CUSTOMER EXPERIENCE MANAGEMENT MARKET, BY VERTICAL
15 APPENDIX
15.1 DISCUSSION GUIDE
15.2 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL
15.3 CUSTOMIZATION OPTIONS
15.4 RELATED REPORTS
15.5 AUTHOR DETAILS
