AI 기반 전력 예측 시장 예측(-2032년) : 제품 유형별, 컴포넌트별, 기술별, 용도별, 최종사용자별, 지역별

According to Stratistics MRC, the Global AI-Enabled Power Forecasting Market is accounted for $5.4 billion in 2025 and is expected to reach $17.2 billion by 2032 growing at a CAGR of 18% during the forecast period. AI-Enabled Power Forecasting uses machine learning and big data analytics to predict electricity demand and generation across time horizons. It analyzes historical consumption, weather patterns, and grid behavior to forecast load curves, renewable output, and market prices. These forecasts help utilities balance supply and demand, optimize dispatch, and integrate intermittent sources like solar and wind. AI models outperform traditional methods in accuracy and adaptability, supporting smarter grid operations and energy planning.

According to the U.S. Department of Energy, AI-driven forecasting is achieving up to 30% higher accuracy in weather-dependent energy prediction, enabling grid operators to balance supply and demand more effectively.

Market Dynamics:

Driver:

Rising renewable energy penetration

Rising renewable energy penetration is a key driver for the AI-enabled power forecasting market, as utilities increasingly integrate solar, wind, and distributed energy resources into power grids. These variable generation sources require accurate, real-time forecasting to maintain grid stability and balance supply with demand. AI-enabled forecasting solutions enhance prediction accuracy by processing large volumes of historical, operational, and environmental data. Growing regulatory pressure to improve energy efficiency and reduce carbon emissions further accelerates adoption of advanced power forecasting technologies.

Restraint:

Forecasting accuracy under volatility

Forecasting accuracy under volatility remains a significant restraint for the AI-enabled power forecasting market. Rapid fluctuations in renewable generation, changing consumption patterns, and extreme weather events complicate prediction models. Even advanced AI algorithms may struggle with data gaps, inconsistent inputs, and sudden system disturbances. Utilities must continuously recalibrate models, increasing operational complexity and costs. These challenges can limit confidence in AI-driven forecasts, particularly in regions with highly variable renewable energy profiles.

Opportunity:

Machine learning-driven forecasting models

Machine learning-driven forecasting models present a strong growth opportunity for the AI-enabled power forecasting market. Advanced algorithms enable adaptive learning, real-time optimization, and improved accuracy across short-term and long-term forecasting horizons. Integration of deep learning, neural networks, and hybrid models allows utilities to better manage renewable variability and demand-side dynamics. Expanding deployment of smart meters, IoT sensors, and grid digitization initiatives further enhances data availability, strengthening the value proposition of AI-enabled forecasting platforms.

Threat:

Weather data uncertainty impacts

Weather data uncertainty poses a notable threat to AI-enabled power forecasting adoption. Forecasting models rely heavily on meteorological inputs, and inaccuracies in weather predictions can significantly impact power generation and demand estimates. Climate change-driven weather anomalies further increase unpredictability, reducing model reliability. Dependence on third-party weather data providers also introduces risks related to data quality, latency, and availability. These factors can affect forecasting confidence and operational decision-making for utilities and grid operators.

The load forecasting solutions segment is expected to be the largest during the forecast period

The load forecasting solutions segment is expected to account for the largest market share during the forecast period, due to their critical role in grid planning, energy trading, and demand management. Utilities rely on accurate load forecasts to optimize generation schedules, reduce imbalance costs, and enhance grid reliability. AI-enabled load forecasting improves precision across different time horizons by analyzing consumption trends, behavioral patterns, and external variables. Growing electricity demand, electrification initiatives, and smart grid deployments reinforce the dominance of load forecasting solutions in the market.

The software platforms segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the software platforms segment is predicted to witness the highest growth rate, reinforced by increasing demand for scalable, cloud-based forecasting solutions. Software platforms enable advanced analytics, real-time visualization, and seamless integration with existing energy management systems. Utilities favor software-driven models due to lower upfront costs and faster deployment compared to hardware-intensive solutions. Continuous improvements in AI algorithms, interoperability, and data processing capabilities further accelerate adoption, driving rapid growth in this segment.

Region with largest share:

During the forecast period, the Asia Pacific region is expected to hold the largest market share, ascribed to rapid expansion of renewable energy capacity and increasing electricity demand across China, India, and Southeast Asia. Government-led clean energy targets, smart grid investments, and grid modernization initiatives drive strong adoption of AI-enabled forecasting solutions. Growing urbanization and industrialization further elevate the need for accurate power planning, positioning Asia Pacific as the leading regional contributor to market revenue.

Region with highest CAGR:

Over the forecast period, the North America region is anticipated to exhibit the highest CAGR associated with advanced digital infrastructure and early adoption of AI technologies in the energy sector. Strong investments in renewable integration, grid automation, and energy storage systems accelerate demand for sophisticated forecasting solutions. Favorable regulatory frameworks, emphasis on grid reliability, and the presence of leading AI and analytics providers further support rapid market expansion across the region.

Key players in the market

Some of the key players in AI-Enabled Power Forecasting Market include IBM Corporation, Microsoft Corporation, Google Cloud AI, Amazon Web Services (AWS), Siemens Energy, Schneider Electric, Autogrid Systems, Oracle Utilities, Uptake Technologies, C3.ai, Tibco Software, Teradata, EnerNex, Vaisala, and DNV

Key Developments:

In January 2026, IBM Corporation expanded its Watsonx AI platform with new energy forecasting modules, enabling utilities to integrate renewable variability predictions directly into grid operations.

In December 2025, Microsoft Corporation announced enhancements to its Azure Energy Forecasting Suite, adding multi-source hybrid forecasting models for solar, wind, and load balancing, targeting European utilities under new EU grid resilience mandates.

In November 2025, Google Cloud AI partnered with NextEra Energy to deploy AI-driven renewable forecasting engines, improving solar and wind prediction accuracy by up to 20% using Google's TensorFlow-based models.

In October 2025, Amazon Web Services (AWS) launched its Energy Forecasting on SageMaker JumpStart, providing pre-trained models for short-term and long-term load forecasting, optimized for utilities and microgrid operators.

Product Types Covered:

• Load Forecasting Solutions
• Renewable Power Forecasting Solutions
• Hybrid Power Forecasting Platforms

Components Covered:

• Software Platforms
• AI & Machine Learning Models
• Data Integration Modules
• Visualization & Reporting Tools

Technologies Covered:

• Machine Learning Algorithms
• Deep Learning Models
• Big Data Analytics
• Cloud-Based Forecasting

Applications Covered:

• Grid Operations & Dispatch
• Renewable Energy Integration
• Energy Trading & Market Operations
• Demand Response Management
• Capacity Planningx

End Users Covered:

• Utility Companies
• Renewable Energy Developers
• Energy Traders
• Independent Power Producers
• Other End Users

Regions Covered:

• North America
  • US
  • Canada
  • Mexico
• Europe
  • Germany
  • UK
  • Italy
  • France
  • Spain
  • Rest of Europe
• Asia Pacific
  • Japan
  • China
  • India
  • Australia
  • New Zealand
  • South Korea
  • Rest of Asia Pacific
• South America
  • Argentina
  • Brazil
  • Chile
  • Rest of South America
• Middle East & Africa
  • Saudi Arabia
  • UAE
  • Qatar
  • South Africa
  • 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 2024, 2025, 2026, 2028, and 2032
• 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
  • Comprehensive profiling of additional market players (up to 3)
  • SWOT Analysis of key players (up to 3)
• Regional Segmentation
  • 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 Product Analysis
• 3.7 Technology Analysis
• 3.8 Application Analysis
• 3.9 End User Analysis
• 3.10 Emerging Markets
• 3.11 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-Enabled Power Forecasting Market, By Product Type

• 5.1 Introduction
• 5.2 Load Forecasting Solutions
  • 5.2.1 Short-Term Load Forecasting
  • 5.2.2 Long-Term Load Forecasting
• 5.3 Renewable Power Forecasting Solutions
  • 5.3.1 Solar Power Forecasting
  • 5.3.2 Wind Power Forecasting
• 5.4 Hybrid Power Forecasting Platforms
  • 5.4.1 Grid-Integrated Forecasting Solutions
  • 5.4.2 Multi-Source Energy Forecasting

6 Global AI-Enabled Power Forecasting Market, By Component

• 6.1 Introduction
• 6.2 Software Platforms
• 6.3 AI & Machine Learning Models
• 6.4 Data Integration Modules
• 6.5 Visualization & Reporting Tools

7 Global AI-Enabled Power Forecasting Market, By Technology

• 7.1 Introduction
• 7.2 Machine Learning Algorithms
• 7.3 Deep Learning Models
• 7.4 Big Data Analytics
• 7.5 Cloud-Based Forecasting

8 Global AI-Enabled Power Forecasting Market, By Application

• 8.1 Introduction
• 8.2 Grid Operations & Dispatch
• 8.3 Renewable Energy Integration
• 8.4 Energy Trading & Market Operations
• 8.5 Demand Response Management
• 8.6 Capacity Planning

9 Global AI-Enabled Power Forecasting Market, By End User

• 9.1 Introduction
• 9.2 Utility Companies
• 9.3 Renewable Energy Developers
• 9.4 Energy Traders
• 9.5 Independent Power Producers
• 9.6 Other End Users

10 Global AI-Enabled Power Forecasting Market, By Geography

• 10.1 Introduction
• 10.2 North America
  • 10.2.1 US
  • 10.2.2 Canada
  • 10.2.3 Mexico
• 10.3 Europe
  • 10.3.1 Germany
  • 10.3.2 UK
  • 10.3.3 Italy
  • 10.3.4 France
  • 10.3.5 Spain
  • 10.3.6 Rest of Europe
• 10.4 Asia Pacific
  • 10.4.1 Japan
  • 10.4.2 China
  • 10.4.3 India
  • 10.4.4 Australia
  • 10.4.5 New Zealand
  • 10.4.6 South Korea
  • 10.4.7 Rest of Asia Pacific
• 10.5 South America
  • 10.5.1 Argentina
  • 10.5.2 Brazil
  • 10.5.3 Chile
  • 10.5.4 Rest of South America
• 10.6 Middle East & Africa
  • 10.6.1 Saudi Arabia
  • 10.6.2 UAE
  • 10.6.3 Qatar
  • 10.6.4 South Africa
  • 10.6.5 Rest of Middle East & Africa

11 Key Developments

• 11.1 Agreements, Partnerships, Collaborations and Joint Ventures
• 11.2 Acquisitions & Mergers
• 11.3 New Product Launch
• 11.4 Expansions
• 11.5 Other Key Strategies

12 Company Profiling

• 12.1 IBM Corporation
• 12.2 Microsoft Corporation
• 12.3 Google Cloud AI
• 12.4 Amazon Web Services (AWS)
• 12.5 Siemens Energy
• 12.6 Schneider Electric
• 12.7 Autogrid Systems
• 12.8 Oracle Utilities
• 12.9 Uptake Technologies
• 12.10 C3.ai
• 12.11 Tibco Software
• 12.12 Teradata
• 12.13 EnerNex
• 12.14 Vaisala
• 12.15 DNV