查看購物車
  • English
  • Japanese
封面
市場調查報告書
商品編碼
1904701

人工智慧在能源管理領域的市場預測（至2032年）：按組件、能源來源、部署類型、應用、最終用戶和地區分類的全球分析

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個工作天內

價格

根據 Stratistics MRC 的一項研究，預計到 2025 年，全球能源管理人工智慧 (AI) 市場規模將達到 102 億美元，到 2032 年將達到 313 億美元，預測期內複合年成長率為 15%。

人工智慧在能源管理的應用，利用包括機器學習、深度學習和預測分析在內的人工智慧演算法，最佳化能源的生產、分配和消費。其主要應用領域包括負載預測、需量反應、預測性維護和電網分析。人工智慧能夠提高效率、降低成本，並支援可再生和分散式能源的併網。它支援即時決策、異常檢測和自主控制，從而將傳統能源系統轉變為智慧自適應網路。

提高能源效率的必要性

能源成本不斷上漲和永續性目標日益嚴格，推動了能源效率最佳化，這也是人工智慧在能源管理領域市場發展的核心驅動力。越來越多的企業開始採用以人工智慧為基礎的分析技術來監控能耗模式、減少能源浪費並最佳化負載管理。在碳減排和營運成本壓力的雙重驅動下，人工智慧驅動的能源管理系統能夠提供即時洞察和預測性最佳化。這些功能有助於工業、商業和公共產業規模的能源運作做出更明智的決策。

數據整合和互通性挑戰

由於能源系統依賴各種傳統和現代平台，數據整合和互通性的挑戰嚴重限制了市場成長。資料來源分散、標準不一致以及通訊協定不相容，使得人工智慧部署複雜且耗時。整合智慧電錶、物聯網設備和企業系統需要大量的客製化工作和先進的技術專長。在大規模能源網路中，這些挑戰會增加部署成本、延遲投資回報，並限制擁有高度異質能源基礎設施的公共產業和企業的採用。

人工智慧驅動的智慧建築解決方案

人工智慧驅動的智慧建築解決方案代表著能源管理領域人工智慧市場的重要成長機會。智慧建築利用人工智慧技術，根據人員佔用情況和即時環境條件最佳化暖通空調系統、照明和儲能系統。在都市化、綠色建築認證和數位雙胞胎技術的推動下，商業和住宅領域的應用正在加速成長。這些解決方案能夠顯著節省能源並減少排放，因此深受尋求智慧化和永續建築營運的設施管理人員和房地產開發商的青睞。

資料隱私和演算法偏見

資料隱私問題和演算法偏見對能源管理領域的人工智慧市場構成重大威脅。人工智慧系統嚴重依賴大量的用戶和營運數據，這引發了人們對數據安全和合規性的擔憂。面對監管機構和相關人員日益嚴格的審查，存在偏見的演算法可能導致能源分配效率低下和決策不公。這些風險可能會削弱使用者信任，並減緩人工智慧的普及，尤其是在資料保護條例和人工智慧倫理要求嚴格的地區。

新冠疫情的影響：

新冠疫情對能源管理領域的人工智慧市場產生了雙重影響。短期內，工業活動的放緩降低了對能源最佳化的即時需求，而基礎設施投資的延遲也減緩了計劃的部署。然而，疫情加速了數位轉型和遠端能源監控的普及。在對高彈性、自動化能源系統的需求驅動下，各組織在疫情後加大了對人工智慧解決方案的投資。儘管疫情帶來了暫時的經濟和營運挑戰，但這種轉變增強了市場的長期前景。

預計在預測期內，軟體平台細分市場將佔據最大的市場佔有率。

由於軟體平台在數據分析、視覺化和決策支援方面發揮核心作用，預計在預測期內，軟體平台細分市場將佔據最大的市場佔有率。人工智慧驅動的平台能夠聚合來自多個能源資產的數據，並透過預測模型和指導模型提供可操作的洞察。憑藉擴充性、雲端部署和持續的演算法更新，軟體平台具備跨產業的柔軟性。它們與現有能源系統的整合能力將推動其應用，並鞏固該細分市場的主導地位。

預計在預測期內，可再生能源領域將呈現最高的複合年成長率。

在預測期內，受太陽能、風能和分散式能源資源日益成長的併網影響，可再生能源領域預計將實現最高成長率。人工智慧解決方案能夠實現精準預測、電網平衡和可再生能源資產性能最佳化。在全球脫碳目標和波動性管理需求的推動下，公共產業和能源生產商正在迅速採用人工智慧工具。這些功能提高了可靠性並實現了收益最大化，從而推動了以可再生能源為中心的能源管理應用領域的快速複合年成長率。

佔比最大的地區：

由於快速的工業化、城市擴張和不斷成長的能源需求，亞太地區預計將在預測期內佔據最大的市場佔有率。中國、日本和印度等國家正大力投資智慧電網和人工智慧驅動的能源最佳化。在政府主導的數位化舉措和大規模可再生能源計劃的推動下，該地區展現出強勁的普及勢頭。成本效益高的技術應用和龐大的能源消耗基礎進一步鞏固了亞太地區的市場主導地位。

年複合成長率最高的地區：

在預測期內，北美預計將實現最高的複合年成長率，這主要得益於該地區人工智慧技術和先進能源基礎設施的早期應用。對智慧建築、電網分析和可再生能源併網的大力投資正在推動對基於人工智慧的能源管理解決方案的需求。在政策支持、企業永續性和技術創新的推動下，該地區展現出快速成長的潛力。主要人工智慧和能源技術供應商的存在也進一步促進了市場擴張。

免費客製化服務：

購買此報告的客戶可以選擇以下免費自訂選項之一：

  • 公司概況
    • 對其他市場公司（最多 3 家公司）進行全面分析
    • 對主要企業進行SWOT分析（最多3家公司）
  • 區域細分
    • 根據客戶要求，提供主要國家的市場估算和預測以及複合年成長率（註：可行性需確認）。
  • 競爭標竿分析
    • 根據主要企業的產品系列、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

  • 摘要
  • 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
  • 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

5. 全球能源管理領域人工智慧市場（按組件分類）

  • 硬體
  • 軟體平台
  • 人工智慧演算法
  • 雲端基礎設施

6. 全球能源管理領域人工智慧市場（按能源來源分類）

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

7. 全球能源管理領域人工智慧市場（按部署類型分類）

  • 本地部署
  • 基於雲端的

8. 全球能源管理領域人工智慧市場（按應用分類）

  • 負荷預測
  • 需量反應
  • 能源最佳化
  • 預測性維護
  • 網格分析

9. 全球能源管理領域人工智慧市場（按最終用戶分類）

  • 公共產業
  • 工業設施
  • 商業建築

第10章：全球能源管理領域人工智慧市場（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

  • 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.