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市场调查报告书
商品编码
1925066

全球人工智慧驱动型电力预测市场预测（至2032年）：按产品类型、组件、技术、应用、最终用户和地区划分

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个工作天内

价格

根据 Stratistics MRC 的一项研究，全球基于人工智慧的电力预测市场预计到 2025 年将达到 54 亿美元，到 2032 年将达到 172 亿美元，在预测期内的复合年增长率为 18%。

人工智慧驱动的能源预测利用机器学习和巨量资料分析来预测能源需求和发电量随时间的变化。它分析历史能耗、天气模式和电网运作情况，从而预测负载曲线、可再生能源输出和市场价格。这些预测有助于电力公司平衡供需、优化发电，并整合太阳能和风能等间歇性能源。人工智慧模型在准确性和适应性方面超越了传统方法，从而支援更智慧的电网运行和能源规划。

据美国能源局称，人工智慧驱动的预测技术已将天气相关的能源预测准确率提高了 30%，使电力系统营运商能够更有效地平衡供需。

提高可再生能源渗透率

随着电力公司加速将太阳能、风能和分散式能源併入电网，可再生能源发电的日益普及成为人工智慧电力预测市场的主要驱动力。这些可变发电来源需要精准的即时预测来维持电网稳定并平衡供需。人工智慧预测解决方案透过处理大量的历史数据、营运数据和环境数据来提高预测精度。此外，监管机构日益增长的提高能源效率和减少碳排放的压力也进一步加速了先进电力预测技术的应用。

变异性下的预测准确性

在波动性较大的环境下，预测准确性仍然是人工智慧驱动的电力预测市场面临的主要阻碍因素。可再生能源发电的快速波动、不断变化的消费模式以及极端天气事件都会使预测模型变得复杂。即使是先进的人工智慧演算法也难以应对资料缺失、输入不一致和突发的系统故障，迫使电力公司不断重新校准模型，从而增加了营运的复杂性和成本。这些挑战会限制人工智慧驱动的预测的可靠性，尤其是在可再生能源波动性极高的地区。

机器学习驱动的预测模型

机器学习驱动的预测模型为人工智慧电力预测市场带来了巨大的成长机会。先进的演算法能够实现自适应学习、即时优化，并提高短期和长期预测的准确性。深度学习、神经网路和混合模式的融合，使电力公司能够更好地管理可再生能源的波动性和需求侧动态变化。智慧电錶、物联网感测器和电网数位化倡议的日益普及，进一步提升了数据的可用性，从而增强了人工智慧预测平台的提案。

气象数据不确定性的影响

天气资料的不确定性对人工智慧驱动的电力预测技术的应用构成显着威胁。预测模型高度依赖天气数据，而天气预报的不准确性会对发电量和需求预测产生重大影响。气候变迁导致的极端天气事件进一步加剧了天气预报的不确定性，降低了模型的可靠性。此外，依赖第三方天气资料提供者也存在资料品质、延迟和可用性方面的风险。这些因素会影响电力公司和电网运营商的预测准确性和营运决策。

预计在预测期内，负载预测解决方案细分市场将占据最大的市场份额。

由于负载预测解决方案在系统规划、能源交易和需求管理中发挥关键作用，预计在预测期内，该细分市场将占据最大的市场份额。电力公司依靠准确的负载预测来优化发电计划、降低不平衡成本并提高系统可靠性。人工智慧驱动的负载预测透过分析消费趋势、行为模式和外部变量，提高了不同时间跨度内的预测准确性。不断增长的电力需求、电气化倡议以及智慧电网的日益普及，进一步巩固了负载预测解决方案在市场上的主导地位。

预计在预测期内，软体平台细分市场将呈现最高的复合年增长率。

预计在预测期内，软体平台细分市场将实现最高成长率，这主要得益于市场对可扩展的云端预测解决方案日益增长的需求。软体平台能够实现高级分析、即时视觉化，并与现有能源管理系统无缝整合。与硬体密集型解决方案相比，公共产业更倾向于采用软体驱动型模式，因为其前期成本更低，部署速度更快。人工智慧演算法、互通性和数据处理能力的不断提升，进一步加速了软体平台的普及应用，并推动了该细分市场的快速成长。

占比最大的地区：

由于中国、印度和东南亚地区可再生能源装置容量的快速增长以及电力需求的不断攀升，预计亚太地区将在预测期内占据最大的市场份额。政府主导的清洁能源目标、智慧电网投资和电网现代化倡议正在推动人工智慧驱动的预测解决方案的广泛应用。不断加快的都市化和工业化进程进一步提升了对精准电力规划的需求，使亚太地区成为市场收入的主要区域贡献者。

复合年增长率最高的地区：

在预测期内，北美预计将实现最高的复合年增长率，这主要得益于能源领域对先进数位基础设施和人工智慧技术的早期应用。对可再生能源併网、电网自动化和能源储存系统的大力投资正在推动对先进预测解决方案的需求。有利的法规结构、对电网可靠性的重视以及主要人工智慧和分析服务提供者的存在，进一步促进了全部区域市场的快速扩张。

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  • 竞争标竿分析
    • 根据主要参与者的产品系列、地理覆盖范围和策略联盟进行基准分析

目录

第一章执行摘要

第二章 前言

  • 概括
  • 相关利益者
  • 调查范围
  • 调查方法
  • 研究材料

第三章 市场趋势分析

  • 司机
  • 抑制因素
  • 机会
  • 威胁
  • 产品分析
  • 技术分析
  • 应用分析
  • 终端用户分析
  • 新兴市场
  • 新冠疫情的感染疾病

第四章 波特五力分析

  • 供应商的议价能力
  • 买方的议价能力
  • 替代品的威胁
  • 新进入者的威胁
  • 竞争对手之间的竞争

第五章 全球以人工智慧为基础的电力预测市场（依产品类型划分）

  • 负载预测解决方案
    • 短期负载预测
    • 长期负载预测
  • 可再生能源电力预测解决方案
    • 太阳能发电量预测
    • 风力发电预测
  • 混合动力预测平台
    • 併网预测解决方案
    • 多源能源预测

第六章 全球基于人工智慧的电力预测市场（按组件划分）

  • 软体平台
  • 人工智慧和机器学习模型
  • 资料整合模组
  • 视觉化和报告工具

第七章 全球基于人工智慧的电力预测市场（按技术划分）

  • 机器学习演算法
  • 深度学习模型
  • 巨量资料分析
  • 基于云端的预测

第八章 全球基于人工智慧的电力预测市场（按应用划分）

  • 电网运转/输电指令
  • 可再生能源併网
  • 能源交易与市场运作
  • 需量反应管理
  • 能力规划

第九章 全球基于人工智慧的电力预测市场（按最终用户划分）

  • 公共产业公司
  • 可再生能源开发商
  • 能源交易员
  • 独立电力生产商
  • 其他最终用户

第十章 全球以人工智慧为基础的电力预测市场（按地区划分）

  • 北美洲
    • 我们
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 亚太其他地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 其他南美国家
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲地区

第十一章 重大进展

  • 协议、伙伴关係、合作和合资企业
  • 併购
  • 新产品发布
  • 业务拓展
  • 其他关键策略

第十二章 企业概况

  • 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.