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市场调查报告书
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1904701

人工智慧在能源管理领域的市场预测（至2032年）：按组件、能源来源、部署类型、应用、最终用户和地区分類的全球分析

AI in Energy Management Market Forecasts to 2032 - Global Analysis By Component (Hardware, Software Platforms, AI Algorithms and Cloud Infrastructure), Energy Source, Deployment, 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，预计到 2025 年，全球能源管理人工智慧 (AI) 市场规模将达到 102 亿美元，到 2032 年将达到 313 亿美元，预测期内复合年增长率为 15%。

人工智慧在能源管理的应用，利用包括机器学习、深度学习和预测分析在内的人工智慧演算法，优化能源的生产、分配和消费。其主要应用领域包括负载预测、需量反应、预测性维护和电网分析。人工智慧能够提高效率、降低成本，并支援可再生和分散式能源的併网。它支援即时决策、异常检测和自主控制，从而将传统能源系统转变为智慧自适应网路。

提高能源效率的必要性

能源成本不断上涨和永续性目标日益严格，推动了能源效率优化，这也是人工智慧在能源管理领域市场发展的核心驱动力。越来越多的企业开始采用以人工智慧为基础的分析技术来监控能耗模式、减少能源浪费并优化负载管理。在碳减排和营运成本压力的双重驱动下，人工智慧驱动的能源管理系统能够提供即时洞察和预测性优化。这些功能有助于工业、商业和公共产业规模的能源运作做出更明智的决策。

数据整合和互通性挑战

由于能源系统依赖各种传统和现代平台，数据整合和互通性的挑战严重限制了市场成长。资料来源分散、标准不一致以及通讯协定不相容，使得人工智慧部署复杂且耗时。整合智慧电錶、物联网设备和企业系统需要大量的客製化工作和先进的技术专长。在大规模能源网路中，这些挑战会增加部署成本、延迟投资回报，并限制拥有高度异质能源基础设施的公共产业和企业的采用。

人工智慧驱动的智慧建筑解决方案

人工智慧驱动的智慧建筑解决方案代表着能源管理领域人工智慧市场的重要成长机会。智慧建筑利用人工智慧技术，根据人员占用情况和即时环境条件优化暖通空调系统、照明和储能係统。在都市化、绿色建筑认证和数数位双胞胎技术的推动下，商业和住宅领域的应用正在加速成长。这些解决方案能够显着节省能源并减少排放，因此深受寻求智慧化和永续建筑营运的设施管理人员和房地产开发商的青睐。

资料隐私和演算法偏见

资料隐私问题和演算法偏见对能源管理领域的人工智慧市场构成重大威胁。人工智慧系统严重依赖大量的用户和营运数据，这引发了人们对数据安全和合规性的担忧。面对监管机构和相关人员日益严格的审查，存在偏见的演算法可能导致能源分配效率低下和决策不公。这些风险可能会削弱使用者信任，并减缓人工智慧的普及，尤其是在资料保护条例和人工智慧伦理要求严格的地区。

新冠疫情的影响：

新冠疫情对能源管理领域的人工智慧市场产生了双重影响。短期内，工业活动的放缓降低了对能源优化的即时需求，而基础设施投资的延迟也减缓了计划的部署。然而，疫情加速了数位转型和远端能源监控的普及。在对高弹性、自动化能源系统的需求驱动下，各组织在疫情后加大了对人工智慧解决方案的投资。儘管疫情带来了暂时的经济和营运挑战，但这种转变增强了市场的长期前景。

预计在预测期内，软体平台细分市场将占据最大的市场份额。

由于软体平台在数据分析、视觉化和决策支援方面发挥核心作用，预计在预测期内，软体平台细分市场将占据最大的市场份额。人工智慧驱动的平台能够聚合来自多个能源资产的数据，并透过预测模型和指导模型提供可操作的洞察。凭藉扩充性、云端部署和持续的演算法更新，软体平台具备跨产业的柔软性。它们与现有能源系统的整合能力将推动其应用，并巩固该细分市场的主导地位。

预计在预测期内，可再生能源领域将呈现最高的复合年增长率。

在预测期内，受太阳能、风能和分散式能源资源日益增长的併网影响，可再生能源领域预计将实现最高成长率。人工智慧解决方案能够实现精准预测、电网平衡和可再生能源资产性能优化。在全球脱碳目标和波动性管理需求的推动下，公共产业和能源生产商正在迅速采用人工智慧工具。这些功能提高了可靠性并实现了收益最大化，从而推动了以可再生能源为中心的能源管理应用领域的快速复合年增长率。

占比最大的地区：

由于快速的工业化、城市扩张和不断增长的能源需求，亚太地区预计将在预测期内占据最大的市场份额。中国、日本和印度等国家正大力投资智慧电网和人工智慧驱动的能源优化。在政府主导的数位化倡议和大规模可再生能源计划的推动下，该地区展现出强劲的普及势头。成本效益高的技术应用和庞大的能源消耗基础进一步巩固了亚太地区的市场主导地位。

年复合成长率最高的地区：

在预测期内，北美预计将实现最高的复合年增长率，这主要得益于该地区人工智慧技术和先进能源基础设施的早期应用。对智慧建筑、电网分析和可再生能源併网的大力投资正在推动对基于人工智慧的能源管理解决方案的需求。在政策支持、企业永续性和技术创新的推动下，该地区展现出快速成长的潜力。主要人工智慧和能源技术供应商的存在也进一步促进了市场扩张。

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目录

第一章执行摘要

第二章 前言

  • 摘要
  • 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
  • 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

5. 全球能源管理领域人工智慧市场（按组件划分）

  • 硬体
  • 软体平台
  • 人工智慧演算法
  • 云端基础设施

6. 全球能源管理领域人工智慧市场（按能源来源划分）

  • 可再生能源
  • 不可可再生能源
  • 混合能源系统
  • 分散式能源

7. 全球能源管理领域人工智慧市场（按部署类型划分）

  • 本地部署
  • 基于云端的

8. 全球能源管理领域人工智慧市场（按应用划分）

  • 负荷预测
  • 需量反应
  • 能源最佳化
  • 预测性维护
  • 网格分析

9. 全球能源管理领域人工智慧市场（按最终用户划分）

  • 公共产业
  • 工业设施
  • 商业建筑

第十章：全球能源管理领域人工智慧市场（按地区划分）

  • 北美洲
    • 美国
    • 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

  • Schneider Electric SE
  • Siemens AG
  • ABB Ltd.
  • IBM Corporation
  • Oracle Corporation
  • Google LLC
  • Microsoft Corporation
  • Amazon Web Services, Inc.
  • General Electric Company
  • Honeywell International Inc.
  • Enel X
  • Autogrid Systems, Inc.
  • C3.ai, Inc.
  • Uplight, Inc.
  • EnergyHub
  • GridPoint, Inc.
Product Code: SMRC33073

According to Stratistics MRC, the Global AI in Energy Management Market is accounted for $10.2 billion in 2025 and is expected to reach $31.3 billion by 2032 growing at a CAGR of 15% during the forecast period. AI in energy management involves the use of artificial intelligence algorithms such as machine learning, deep learning, and predictive analytics to optimize energy generation, distribution, and consumption. Applications include load forecasting, demand response, predictive maintenance, and grid analytics. AI enhances efficiency, reduces costs, and supports integration of renewables and distributed energy resources. It enables real-time decision-making, anomaly detection, and autonomous control, transforming traditional energy systems into intelligent, adaptive networks.

Market Dynamics:

Driver:

Need for energy efficiency optimization

The need for energy efficiency optimization is a core driver of the AI in Energy Management market, driven by rising energy costs and stringent sustainability targets. Organizations are increasingly adopting AI-based analytics to monitor consumption patterns, reduce energy waste, and optimize load management. Fueled by carbon reduction commitments and operational cost pressures, AI-enabled energy management systems deliver real-time insights and predictive optimization. These capabilities support smarter decision-making across industrial, commercial, and utility-scale energy operations.

Restraint:

Data integration and interoperability issues

Data integration and interoperability challenges significantly restrain market growth, as energy systems rely on diverse legacy and modern platforms. Influenced by fragmented data sources, inconsistent standards, and incompatible communication protocols, AI deployment becomes complex and time-intensive. Integrating smart meters, IoT devices, and enterprise systems requires substantial customization and technical expertise. For large-scale energy networks, these challenges increase implementation costs and delay ROI, limiting adoption among utilities and enterprises with highly heterogeneous energy infrastructures.

Opportunity:

AI-driven smart building solutions

AI-driven smart building solutions present a major growth opportunity within the AI in Energy Management market. Smart buildings leverage AI to optimize HVAC systems, lighting, and energy storage based on occupancy and real-time conditions. Propelled by urbanization, green building certifications, and digital twin technologies, adoption is accelerating across commercial and residential sectors. These solutions enable significant energy savings and emissions reduction, creating strong demand from facility managers and real estate developers seeking intelligent, sustainable building operations.

Threat:

Data privacy and algorithm bias

Data privacy concerns and algorithm bias pose critical threats to the AI in Energy Management market. AI systems rely heavily on large volumes of user and operational data, raising concerns over data security and regulatory compliance. Fueled by increasing scrutiny from regulators and stakeholders, biased algorithms may lead to inefficient energy allocation or unfair decision-making. These risks can undermine trust among users and slow adoption, particularly in regions with strict data protection regulations and ethical AI requirements.

Covid-19 Impact:

The COVID-19 pandemic had a dual impact on the AI in Energy Management market. Short-term disruptions in industrial activity reduced immediate energy optimization demand, while delayed infrastructure investments slowed project rollouts. However, the pandemic accelerated digital transformation and remote energy monitoring adoption. Motivated by the need for resilient, automated energy systems, organizations increasingly invested in AI-driven solutions post-pandemic. This shift strengthened long-term market prospects despite temporary economic and operational challenges during the crisis.

The software platforms segment is expected to be the largest during the forecast period

The software platforms segment is expected to account for the largest market share during the forecast period, resulting from its central role in data analytics, visualization, and decision support. AI-powered platforms aggregate data from multiple energy assets and deliver actionable insights through predictive and prescriptive models. Driven by scalability, cloud deployment, and continuous algorithm upgrades, software platforms offer flexibility across industries. Their ability to integrate with existing energy systems strengthens adoption and reinforces segment leadership.

The renewable energy segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the renewable energy segment is predicted to witness the highest growth rate, propelled by increasing integration of solar, wind, and distributed energy resources. AI solutions enable accurate forecasting, grid balancing, and performance optimization of renewable assets. Spurred by global decarbonization goals and variability management requirements, utilities and energy producers are rapidly deploying AI tools. These capabilities enhance reliability and maximize returns, driving rapid CAGR within renewable-focused energy management applications.

Region with largest share:

During the forecast period, the Asia Pacific region is expected to hold the largest market share, attributed to rapid industrialization, urban expansion, and rising energy demand. Countries such as China, Japan, and India are investing heavily in smart grids and AI-enabled energy optimization. Supported by government-led digitalization initiatives and large-scale renewable projects, the region demonstrates strong adoption momentum. Cost-efficient technology deployment and a vast energy consumer base further support Asia Pacific's market dominance.

Region with highest CAGR:

Over the forecast period, the North America region is anticipated to exhibit the highest CAGR associated with early adoption of AI technologies and advanced energy infrastructure. Strong investments in smart buildings, grid analytics, and renewable integration drive demand for AI-based energy management solutions. Fueled by supportive policies, corporate sustainability commitments, and technological innovation, the region shows rapid growth potential. The presence of leading AI and energy technology providers further accelerates market expansion.

Key players in the market

Some of the key players in AI in Energy Management Market include Schneider Electric SE, Siemens AG, ABB Ltd., IBM Corporation, Oracle Corporation, Google LLC, Microsoft Corporation, Amazon Web Services, Inc., General Electric Company, Honeywell International Inc., Enel X, Autogrid Systems, Inc., C3.ai, Inc., Uplight, Inc., EnergyHub and GridPoint, Inc.

Key Developments:

In November 2025, ABB unveiled its AI-enabled Ability(TM) Energy Management Suite, designed to reduce industrial energy consumption by up to 20% through advanced load forecasting and automated control systems.

In October 2025, IBM expanded its Watson AI platform with energy-specific modules, providing utilities with predictive maintenance and demand-side management tools to improve grid reliability and efficiency.

In October 2025, Microsoft integrated AI-driven sustainability dashboards into Azure Energy Data Services, empowering enterprises to track carbon emissions and optimize energy usage across global operations.

Components Covered:

  • Hardware
  • Software Platforms
  • AI Algorithms
  • Cloud Infrastructure

Energy Sources Covered:

  • Renewable Energy
  • Non-Renewable
  • Hybrid Energy Systems
  • Distributed Energy Resources

Deployments Covered:

  • On-Premise
  • Cloud-Based

Applications Covered:

  • Load Forecasting
  • Demand Response
  • Energy Optimization
  • Predictive Maintenance
  • Grid Analytics

End Users Covered:

  • Utilities
  • Industrial Facilities
  • Commercial Buildings

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 Application Analysis
  • 3.7 End User Analysis
  • 3.8 Emerging Markets
  • 3.9 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 in Energy Management Market, By Component

  • 5.1 Introduction
  • 5.2 Hardware
  • 5.3 Software Platforms
  • 5.4 AI Algorithms
  • 5.5 Cloud Infrastructure

6 Global AI in Energy Management Market, By Energy Source

  • 6.1 Introduction
  • 6.2 Renewable Energy
  • 6.3 Non-Renewable
  • 6.4 Hybrid Energy Systems
  • 6.5 Distributed Energy Resources

7 Global AI in Energy Management Market, By Deployment

  • 7.1 Introduction
  • 7.2 On-Premise
  • 7.3 Cloud-Based

8 Global AI in Energy Management Market, By Application

  • 8.1 Introduction
  • 8.2 Load Forecasting
  • 8.3 Demand Response
  • 8.4 Energy Optimization
  • 8.5 Predictive Maintenance
  • 8.8 Grid Analytics

9 Global AI in Energy Management Market, By End User

  • 9.1 Introduction
  • 9.2 Utilities
  • 9.3 Industrial Facilities
  • 9.4 Commercial Buildings

10 Global AI in Energy Management 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 Schneider Electric SE
  • 12.2 Siemens AG
  • 12.3 ABB Ltd.
  • 12.4 IBM Corporation
  • 12.5 Oracle Corporation
  • 12.6 Google LLC
  • 12.7 Microsoft Corporation
  • 12.8 Amazon Web Services, Inc.
  • 12.9 General Electric Company
  • 12.10 Honeywell International Inc.
  • 12.11 Enel X
  • 12.12 Autogrid Systems, Inc.
  • 12.13 C3.ai, Inc.
  • 12.14 Uplight, Inc.
  • 12.15 EnergyHub
  • 12.16 GridPoint, Inc.

List of Tables

  • Table 1 Global AI in Energy Management Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global AI in Energy Management Market Outlook, By Component (2024-2032) ($MN)
  • Table 3 Global AI in Energy Management Market Outlook, By Hardware (2024-2032) ($MN)
  • Table 4 Global AI in Energy Management Market Outlook, By Software Platforms (2024-2032) ($MN)
  • Table 5 Global AI in Energy Management Market Outlook, By AI Algorithms (2024-2032) ($MN)
  • Table 6 Global AI in Energy Management Market Outlook, By Cloud Infrastructure (2024-2032) ($MN)
  • Table 7 Global AI in Energy Management Market Outlook, By Energy Source (2024-2032) ($MN)
  • Table 8 Global AI in Energy Management Market Outlook, By Renewable Energy (2024-2032) ($MN)
  • Table 9 Global AI in Energy Management Market Outlook, By Non-Renewable (2024-2032) ($MN)
  • Table 10 Global AI in Energy Management Market Outlook, By Hybrid Energy Systems (2024-2032) ($MN)
  • Table 11 Global AI in Energy Management Market Outlook, By Distributed Energy Resources (2024-2032) ($MN)
  • Table 12 Global AI in Energy Management Market Outlook, By Deployment (2024-2032) ($MN)
  • Table 13 Global AI in Energy Management Market Outlook, By On-Premise (2024-2032) ($MN)
  • Table 14 Global AI in Energy Management Market Outlook, By Cloud-Based (2024-2032) ($MN)
  • Table 15 Global AI in Energy Management Market Outlook, By Application (2024-2032) ($MN)
  • Table 16 Global AI in Energy Management Market Outlook, By Load Forecasting (2024-2032) ($MN)
  • Table 17 Global AI in Energy Management Market Outlook, By Demand Response (2024-2032) ($MN)
  • Table 18 Global AI in Energy Management Market Outlook, By Energy Optimization (2024-2032) ($MN)
  • Table 19 Global AI in Energy Management Market Outlook, By Predictive Maintenance (2024-2032) ($MN)
  • Table 20 Global AI in Energy Management Market Outlook, By Grid Analytics (2024-2032) ($MN)
  • Table 21 Global AI in Energy Management Market Outlook, By End User (2024-2032) ($MN)
  • Table 22 Global AI in Energy Management Market Outlook, By Utilities (2024-2032) ($MN)
  • Table 23 Global AI in Energy Management Market Outlook, By Industrial Facilities (2024-2032) ($MN)
  • Table 24 Global AI in Energy Management Market Outlook, By Commercial Buildings (2024-2032) ($MN)

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