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1925066

全球人工智慧驅動型電力預測市場預測（至2032年）：按產品類型、組件、技術、應用、最終用戶和地區分類

AI-Enabled Power Forecasting Market Forecasts to 2032 - Global Analysis By Product Type, Component, Technology, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的一項研究，全球基於人工智慧的電力預測市場預計到 2025 年將達到 54 億美元，到 2032 年將達到 172 億美元，在預測期內的複合年成長率為 18%。

人工智慧驅動的能源預測利用機器學習和巨量資料分析來預測能源需求和發電量隨時間的變化。它分析歷史能耗、天氣模式和電網運作情況，從而預測負載曲線、可再生能源輸出和市場價格。這些預測有助於電力公司平衡供需、最佳化發電，並整合太陽能和風能等間歇性能源。人工智慧模型在準確性和適應性方面超越了傳統方法，從而支援更智慧的電網運行和能源規劃。

據美國能源局稱，人工智慧驅動的預測技術已將天氣相關的能源預測準確率提高了 30%，使電力系統營運商能夠更有效地平衡供需。

提高可再生能源滲透率

隨著電力公司加速將太陽能、風能和分散式能源併入電網，可再生能源發電的日益普及成為人工智慧電力預測市場的主要驅動力。這些可變發電來源需要精準的即時預測來維持電網穩定並平衡供需。人工智慧預測解決方案透過處理大量的歷史數據、營運數據和環境數據來提高預測精度。此外，監管機構日益成長的提高能源效率和減少碳排放的壓力也進一步加速了先進電力預測技術的應用。

變異性下的預測準確性

在波動性較大的環境下，預測準確性仍然是人工智慧驅動的電力預測市場面臨的主要阻礙因素。可再生能源發電的快速波動、不斷變化的消費模式以及極端天氣事件都會使預測模型變得複雜。即使是先進的人工智慧演算法也難以應對資料缺失、輸入不一致和突發的系統故障，迫使電力公司不斷重新校準模型，從而增加了營運的複雜性和成本。這些挑戰會限制人工智慧驅動的預測的可靠性，尤其是在可再生能源波動性極高的地區。

機器學習驅動的預測模型

機器學習驅動的預測模型為人工智慧電力預測市場帶來了巨大的成長機會。先進的演算法能夠實現自適應學習、即時最佳化，並提高短期和長期預測的準確性。深度學習、神經網路和混合模式的融合，使電力公司能夠更好地管理可再生能源的波動性和需求側動態變化。智慧電錶、物聯網感測器和電網數位化舉措的日益普及，進一步提升了數據的可用性，從而增強了人工智慧預測平台的提案。

氣象數據不確定性的影響

天氣資料的不確定性對人工智慧驅動的電力預測技術的應用構成顯著威脅。預測模型高度依賴天氣數據，而天氣預報的不準確性會對發電量和需求預測產生重大影響。氣候變遷導致的極端天氣事件進一步加劇了天氣預報的不確定性，降低了模型的可靠性。此外，依賴第三方天氣資料提供者也存在資料品質、延遲和可用性方面的風險。這些因素會影響電力公司和電網運營商的預測準確性和營運決策。

預計在預測期內，負載預測解決方案細分市場將佔據最大的市場佔有率。

由於負載預測解決方案在系統規劃、能源交易和需求管理中發揮關鍵作用，預計在預測期內，該細分市場將佔據最大的市場佔有率。電力公司依靠準確的負載預測來最佳化發電計劃、降低不平衡成本並提高系統可靠性。人工智慧驅動的負載預測透過分析消費趨勢、行為模式和外部變量，提高了不同時間跨度內的預測準確性。不斷成長的電力需求、電氣化舉措以及智慧電網的日益普及，進一步鞏固了負載預測解決方案在市場上的主導地位。

預計在預測期內，軟體平台細分市場將呈現最高的複合年成長率。

預計在預測期內，軟體平台細分市場將實現最高成長率，這主要得益於市場對可擴展的雲端預測解決方案日益成長的需求。軟體平台能夠實現高級分析、即時視覺化，並與現有能源管理系統無縫整合。與硬體密集型解決方案相比，公共產業更傾向於採用軟體驅動型模式，因為其前期成本更低，部署速度更快。人工智慧演算法、互通性和數據處理能力的不斷提升，進一步加速了軟體平台的普及應用，並推動了該細分市場的快速成長。

佔比最大的地區：

由於中國、印度和東南亞地區可再生能源裝置容量的快速成長以及電力需求的不斷攀升，預計亞太地區將在預測期內佔據最大的市場佔有率。政府主導的清潔能源目標、智慧電網投資和電網現代化舉措正在推動人工智慧驅動的預測解決方案的廣泛應用。不斷加快的都市化和工業化進程進一步提升了對精準電力規劃的需求，使亞太地區成為市場收入的主要區域貢獻者。

複合年成長率最高的地區：

在預測期內，北美預計將實現最高的複合年成長率，這主要得益於能源領域對先進數位基礎設施和人工智慧技術的早期應用。對可再生能源併網、電網自動化和能源儲存系統的大力投資正在推動對先進預測解決方案的需求。有利的法規結構、對電網可靠性的重視以及主要人工智慧和分析服務提供者的存在，進一步促進了全部區域市場的快速擴張。

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    • 根據主要參與者的產品系列、地理覆蓋範圍和策略聯盟進行基準分析

目錄

第1章執行摘要

第2章 前言

  • 概括
  • 相關利益者
  • 調查範圍
  • 調查方法
  • 研究材料

第3章 市場趨勢分析

  • 促進要素
  • 抑制因素
  • 機會
  • 威脅
  • 產品分析
  • 技術分析
  • 應用分析
  • 終端用戶分析
  • 新興市場
  • 新冠疫情的感染疾病

第4章 波特五力分析

  • 供應商的議價能力
  • 買方的議價能力
  • 替代品的威脅
  • 新進入者的威脅
  • 競爭對手之間的競爭

第5章 全球以人工智慧為基礎的電力預測市場（依產品類型分類）

  • 負載預測解決方案
    • 短期負載預測
    • 長期負載預測
  • 可再生能源電力預測解決方案
    • 太陽能發電量預測
    • 風力發電預測
  • 混合動力預測平台
    • 併網預測解決方案
    • 多源能源預測

第6章 全球基於人工智慧的電力預測市場（按組件分類）

  • 軟體平台
  • 人工智慧和機器學習模型
  • 資料整合模組
  • 視覺化和報告工具

第7章 全球基於人工智慧的電力預測市場（按技術分類）

  • 機器學習演算法
  • 深度學習模型
  • 巨量資料分析
  • 基於雲端的預測

第8章 全球基於人工智慧的電力預測市場（按應用分類）

  • 電網運轉/輸電指令
  • 可再生能源併網
  • 能源交易與市場運作
  • 需量反應管理
  • 能力規劃

第9章 全球基於人工智慧的電力預測市場（按最終用戶分類）

  • 公共產業公司
  • 可再生能源開發商
  • 能源交易員
  • 獨立電力生產商
  • 其他最終用戶

第10章 全球以人工智慧為基礎的電力預測市場（按地區分類）

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 義大利
    • 法國
    • 西班牙
    • 其他歐洲
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 亞太其他地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 其他南美國家
  • 中東和非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 卡達
    • 南非
    • 其他中東和非洲地區

第11章 重大進展

  • 協議、夥伴關係、合作和合資企業
  • 併購
  • 新產品發布
  • 業務拓展
  • 其他關鍵策略

第12章 企業概況

  • 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
  • DNV
Product Code: SMRC33460

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

List of Tables

  • Table 1 Global AI-Enabled Power Forecasting Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global AI-Enabled Power Forecasting Market Outlook, By Product Type (2024-2032) ($MN)
  • Table 3 Global AI-Enabled Power Forecasting Market Outlook, By Load Forecasting Solutions (2024-2032) ($MN)
  • Table 4 Global AI-Enabled Power Forecasting Market Outlook, By Short-Term Load Forecasting (2024-2032) ($MN)
  • Table 5 Global AI-Enabled Power Forecasting Market Outlook, By Long-Term Load Forecasting (2024-2032) ($MN)
  • Table 6 Global AI-Enabled Power Forecasting Market Outlook, By Renewable Power Forecasting Solutions (2024-2032) ($MN)
  • Table 7 Global AI-Enabled Power Forecasting Market Outlook, By Solar Power Forecasting (2024-2032) ($MN)
  • Table 8 Global AI-Enabled Power Forecasting Market Outlook, By Wind Power Forecasting (2024-2032) ($MN)
  • Table 9 Global AI-Enabled Power Forecasting Market Outlook, By Hybrid Power Forecasting Platforms (2024-2032) ($MN)
  • Table 10 Global AI-Enabled Power Forecasting Market Outlook, By Grid-Integrated Forecasting Solutions (2024-2032) ($MN)
  • Table 11 Global AI-Enabled Power Forecasting Market Outlook, By Multi-Source Energy Forecasting (2024-2032) ($MN)
  • Table 12 Global AI-Enabled Power Forecasting Market Outlook, By Component (2024-2032) ($MN)
  • Table 13 Global AI-Enabled Power Forecasting Market Outlook, By Software Platforms (2024-2032) ($MN)
  • Table 14 Global AI-Enabled Power Forecasting Market Outlook, By AI & Machine Learning Models (2024-2032) ($MN)
  • Table 15 Global AI-Enabled Power Forecasting Market Outlook, By Data Integration Modules (2024-2032) ($MN)
  • Table 16 Global AI-Enabled Power Forecasting Market Outlook, By Visualization & Reporting Tools (2024-2032) ($MN)
  • Table 17 Global AI-Enabled Power Forecasting Market Outlook, By Technology (2024-2032) ($MN)
  • Table 18 Global AI-Enabled Power Forecasting Market Outlook, By Machine Learning Algorithms (2024-2032) ($MN)
  • Table 19 Global AI-Enabled Power Forecasting Market Outlook, By Deep Learning Models (2024-2032) ($MN)
  • Table 20 Global AI-Enabled Power Forecasting Market Outlook, By Big Data Analytics (2024-2032) ($MN)
  • Table 21 Global AI-Enabled Power Forecasting Market Outlook, By Cloud-Based Forecasting (2024-2032) ($MN)
  • Table 22 Global AI-Enabled Power Forecasting Market Outlook, By Application (2024-2032) ($MN)
  • Table 23 Global AI-Enabled Power Forecasting Market Outlook, By Grid Operations & Dispatch (2024-2032) ($MN)
  • Table 24 Global AI-Enabled Power Forecasting Market Outlook, By Renewable Energy Integration (2024-2032) ($MN)
  • Table 25 Global AI-Enabled Power Forecasting Market Outlook, By Energy Trading & Market Operations (2024-2032) ($MN)
  • Table 26 Global AI-Enabled Power Forecasting Market Outlook, By Demand Response Management (2024-2032) ($MN)
  • Table 27 Global AI-Enabled Power Forecasting Market Outlook, By Capacity Planning (2024-2032) ($MN)
  • Table 28 Global AI-Enabled Power Forecasting Market Outlook, By End User (2024-2032) ($MN)
  • Table 29 Global AI-Enabled Power Forecasting Market Outlook, By Utility Companies (2024-2032) ($MN)
  • Table 30 Global AI-Enabled Power Forecasting Market Outlook, By Renewable Energy Developers (2024-2032) ($MN)
  • Table 31 Global AI-Enabled Power Forecasting Market Outlook, By Energy Traders (2024-2032) ($MN)
  • Table 32 Global AI-Enabled Power Forecasting Market Outlook, By Independent Power Producers (2024-2032) ($MN)
  • Table 33 Global AI-Enabled Power Forecasting Market Outlook, By Other End Users (2024-2032) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.