中國人工智慧能源管理軟體：市場佔有率分析、產業趨勢與統計及成長預測（2026-2031）

根據 Mordor Intelligence 預測，中國人工智慧能源管理軟體市場規模預計到 2025 年將達到 3.247 億美元，到 2026 年將達到 3.865 億美元，到 2031 年將達到 9.853 億美元，2026 年至 2031 年的複合年成長率為 20.58%。

本報告按元件（軟體和服務）、部署模式（雲端、本地部署、混合部署）、應用程式（例如，能源消耗和需求最佳化、資產效能和預測性維護）以及最終用戶（例如，公共產業、商業建築）進行細分。市場預測以美元計價。

中國人工智慧能源管理軟體市場趨勢及洞察

商業和工業設施對即時能源最佳化的需求日益成長。

工業和商業能源用戶在中國人工智慧能源管理軟體市場中仍然佔據核心地位，因為他們面臨越來越大的壓力，既要控制能源消耗，又要滿足日益嚴格的能源效率和碳排放目標。國家發展與改革委員會、國家能源局於2025年9月發布的實施意見，將人工智慧在能源領域的應用從一個廣泛的政策主題，轉變為涵蓋電力、煤炭、油氣等系統的明確應用議程。隨後於2026年5月發布的行動計劃，透過提出51個人工智慧和能源應用場景，進一步明確了這一方向，為企業和公共產業提供了更清晰的採購和實施路徑。這項政策框架意義重大，因為它為採購者提供了更有力的理由，讓他們有理由投資能夠提升工廠和建築監控、控制和報告能力的軟體。這也解釋了中國人工智慧能源管理軟體市場日益成長的需求，這種需求不僅包括核心分析工具，還包括與實際營運決策相關的實施支援和工作流程整合。

人工智慧、智慧電網和分散式能源的融合

中國的人工智慧能源管理軟體市場正獲得電網方面的支持。在電網方面，使用傳統的靜態工具來管理可再生能源併網和平衡電力變得越來越困難。目前，可再生能源已佔中國裝置容量的48%，這提升了能夠預測發電波動並更有效地協調分散式資源的軟體的價值。 《2026年國家行動計畫》明確將智慧電網、虛擬電廠和新能源預測列為優先應用領域，顯示人工智慧在電網領域的應用已正式納入國家規劃。這進一步增強了對能夠提供適用於電網運作、分散式資產和企業能源系統產品的供應商的需求。此外，中國人工智慧能源管理軟體市場的進入門檻也不斷提高，供應商將被要求擁有可靠的在地化實施經驗和符合電力公司營運規定的架構。

與傳統OT和IT系統整合的複雜性

由於人工智慧工具依賴穩定且結構化的運作數據，傳統控制環境仍然是中國人工智慧能源管理軟體市場面臨的最大阻礙因素之一。一項於2025年開展的關於IT和OT整合的同行評審研究表明，工業數位化依賴於五個相互關聯的領域：通訊、IT主導的OT支援、人性化的設計、安全性和先進的工業應用，每個領域都需要專門的工程工作。這解釋了為什麼許多專案在仍然依賴過時的PLC、孤立的監控系統和多年來構建的客製化介面時進展緩慢。問題不僅在於技術成本；漫長的整合週期也會延遲買家實現可衡量的成本節約和合規性效益的時間。在中國的人工智慧能源管理軟體市場，那些能夠簡化資料收集和系統整合的供應商，比那些在展現價值之前就依賴全廠現代化改造的供應商，更有可能將更多的商機轉化為實際交易。

細分市場分析

到2025年，軟體銷售額將佔中國人工智慧能源管理軟體市場總銷售額的68.11%，成為該市場最大的組成部分。這一主導地位反映了平台授權、軟體訂閱、分析模組和控制應用在企業部署中的核心作用。即使在大規模公共產業合約和多站點工業部署中，支出往往首先集中在核心平台上，因為買家通常需要一個“記錄系統”，然後再添加諮詢和最佳化層。因此，中國人工智慧能源管理軟體市場一直依賴軟體作為價值創造的基礎，尤其是在需要跨多個資產整合監控、預測和運作控制時。

預計2026年至2031年間，服務業將以21.22%的複合年成長率成長，成為預測期間內成長最快的產業。這一成長表明，買家不再滿足於僅購買軟體，因為許多實施方案需要模型調優、實施支援、碳計量工作流程以及持續最佳化才能產生可操作的結果。 2026年3月制定的節能設備部署計畫要求開發大規模人工智慧驅動的節能減排模型，並推廣智慧設備管理服務，將直接推動軟體部署服務層的進一步擴展。這項轉變對供應商意義重大，因為它不僅拓展了其初始軟體銷售的收入來源，還增加了部署和支援帶來的持續收入。這也解釋了為什麼中國人工智慧能源管理軟體市場越來越依賴執行能力和本地服務交付質量，而不是產品本身的功能。

到2025年，雲端部署將佔據中國人工智慧能源管理軟體市場58.16%的市場佔有率，成為最大的部署模式。這反映了國內雲端基礎設施的快速發展，以及市場對集中存取、快速更新和跨分散式運行環境的大規模資料聚合等優勢的認可。許多企業希望整合監控、分析和報告功能，而無需在每個地點建立完全獨立的軟體堆疊，因此這種部署模式對市場大有裨益。此外，雲端部署也適用於商業建築和輕工業環境中的新型應用場景，在這些場景中，柔軟性和降低初始基礎設施投資至關重要。

預計2026年至2031年間，混合部署將以21.34%的複合年成長率成長，成為市場上成長最快的架構。這一趨勢與日益嚴格的能源和營運資料管理要求相吻合，尤其對於公共產業和關鍵基礎設施營運商而言，他們必須將敏感資訊保存在經批准的管轄範圍內。能源產業資料安全管理措施於2025年12月制定，並於2026年7月1日生效，進一步認可了利用雲端資源進行聚合和報告，同時將部分資料和工作負載保留在本地的實用方法。因此，中國人工智慧能源管理軟體市場正轉向新的部署模式，使企業能夠在保持敏感的營運技術（OT）功能靠近其資產的同時，繼續利用廣泛的全站分析和基準測試。隨著人工智慧應用場景擴展到公共產業、製造業和資料中心等領域，這種平衡將至關重要。

其他好處：

• Excel格式的市場預測（ME）表
• 3個月的分析師支持

目錄

第1章：引言

• 研究假設和市場定義
• 調查範圍

第2章：調查方法

第3章執行摘要

第4章 市場狀況

• 市場概覽
• 市場促進因素
  • 商業和工業設施對即時能源最佳化的需求日益成長。
  • 人工智慧、智慧電網和分散式能源的融合
  • 對自動化需量反應和尖峰負載管理的需求日益成長
  • 擴展ESG報表和碳核算工作流程
  • 引進邊緣人工智慧技術，實現站點級能源控制和故障檢測
  • 由於建築物和工業基礎設施老化，對維修的需求日益成長。
• 市場限制因素
  • 與傳統OT和IT系統整合難度很高。
  • 與數據品質、互通性和感測器碎片化相關的挑戰。
  • 對關鍵能源資產的網路安全和數據主權問題的擔憂
  • 中小型站點負載密度有限，投資報酬率存在不確定性。
• 宏觀經濟因素對市場的影響
• 產業價值鏈分析
• 監理情勢
• 技術展望
• 波特五力分析

第5章 市場規模與成長預測

• 按組件
  • 軟體
  • 服務
• 部署模式
  • 基於雲端的
  • 現場
  • 混合
• 透過使用
  • 最佳化能源消耗和需求
  • 資產性能和預測性維護
  • 智慧電網和分散式能源（DER）管理
  • 可再生能源預測與整合
  • 能源交易、定價和市場訊息
• 最終用戶
  • 公用事業
  • 商業建築
  • 工業設施
  • 住宅大樓

第6章 競爭情勢

• 市場集中度
• 策略趨勢
• 市佔率分析
• 公司簡介
  • Siemens AG
  • Schneider Electric SE
  • ABB Ltd.
  • Honeywell International Inc.
  • IBM Corporation
  • Johnson Controls International plc
  • Oracle Corporation
  • Microsoft Corporation
  • Amazon Web Services, Inc.
  • C3.ai, Inc.
  • Bidgely, Inc.
  • Grid4C Ltd.
  • Innowatts, Inc.
  • EnergyCAP, LLC
  • Enel X Srl
  • Guoneng Rixin
  • State Grid Corporation of China
  • Dexma Sensors, SL
  • Rockwell Automation, Inc.
  • Envision Digital

第7章 市場機會與未來展望

According to Mordor Intelligence, the China AI-powered energy management software market size is projected to be USD 324.7 million in 2025, USD 386.5 million in 2026, and reach USD 985.3 million by 2031, growing at a CAGR of 20.58% from 2026 to 2031.

This report is Segmented by Component (Software, and Services), Deployment Mode (Cloud-Based, On-Premises, and Hybrid), Application (Energy Consumption and Demand Optimization, Asset Performance and Predictive Maintenance, and More), and End User (Utilities, Commercial Buildings, and More). The Market Forecasts are Provided in Terms of Value (USD).

China AI-powered Energy Management Software Market Trends and Insights

Rising Need For Real-Time Energy Optimization in Commercial and Industrial Facilities

Industrial and commercial energy users remain central to the China AI-powered Energy Management Software Market, as they face growing pressure to control consumption while meeting increasingly stringent efficiency and carbon-emission targets. The September 2025 implementation opinions from the National Development and Reform Commission and the National Energy Administration moved AI in energy from a broad policy theme to a defined application agenda across power, coal, and oil and gas systems. The May 2026 action plan then expanded on that direction by setting out 51 AI and energy application scenarios, providing enterprises and utilities with a clearer path for procurement and deployment. This policy structure matters because buyers now have stronger reasons to justify software spending that improves monitoring, control, and reporting across plants and buildings. It also helps explain why the China AI-powered Energy Management Software Market is seeing demand not only for core analytics tools, but also for implementation support and workflow integration tied to real operating decisions.

Integration of AI With Smart Grid and Distributed Energy Resources

The China AI-powered Energy Management Software Market is also gaining support from the grid side, where integrating renewables and balancing power have become more difficult to manage with static tools. China's renewables already accounted for 48% of installed power capacity, and that has raised the value of software that can forecast variable output and coordinate distributed resources more effectively. The 2026 national action plan specifically included smart grids, virtual power plants, and new energy forecasting among its priority application areas, indicating that grid-facing AI use cases are now part of formal state planning. That creates a stronger demand base for vendors whose products can work across grid operations, distributed assets, and enterprise energy systems. It also raises the bar in the China AI-powered Energy Management Software Market, as suppliers increasingly need credible local deployment experience and architectures that comply with utility operating rules.

High Integration Complexity with Legacy OT and IT Systems

Legacy control environments remain one of the clearest constraints on the China AI-powered Energy Management Software Market, as AI tools depend on stable, structured operational data. A 2025 peer-reviewed survey on IT and OT integration found that industrial digitalization depends on five linked domains: communication, IT-driven OT support, human centricity, security, and advanced industrial applications, and each one needs dedicated engineering effort. That helps explain why many projects still move slowly when sites rely on old PLCs, siloed supervisory systems, and custom interfaces built over many years. The issue is not only technical cost, because long integration cycles also delay the point at which buyers can see measurable savings or compliance gains. In the China AI-powered Energy Management Software Market, vendors that simplify data capture and system integration are likely to convert more opportunities than those that depend on full-site modernization before value can be demonstrated.

Other drivers and restraints analyzed in the detailed report include:

1. Increasing Demand for Automated Demand Response and Peak Load Management
2. Expansion of ESG Reporting and Carbon Accounting Workflows
3. Data Quality, Interoperability, and Sensor Fragmentation Issues

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Software accounted for 68.11% of revenue in 2025, making it the largest component of the China AI-powered Energy Management Software Market. This lead reflects the central role of platform licenses, software subscriptions, analytics modules, and control applications in enterprise deployments. Large utility contracts and multi-site industrial rollouts also tend to concentrate spending in core platforms first, since buyers usually need a system of record before they add advisory or optimization layers. The China AI-powered Energy Management Software Market, therefore, continued to lean on software as the basis for value creation, especially when monitoring, forecasting, and operational control needed to be integrated across multiple assets.

Services are projected to expand at a 21.22% CAGR from 2026 to 2031, which makes them the fastest-growing component over the forecast period. This rise shows that buyers are not stopping at software purchase, because many deployments require model tuning, implementation support, carbon accounting workflows, and ongoing optimization to produce usable results. The March 2026 implementation plan for energy-saving equipment called for the development of large-scale AI-based energy-saving and carbon-reducing models and for promoting intelligent device management services, which directly support a broader software adoption service layer. The shift is important because it gives vendors more room to grow recurring revenue from delivery and support, not just from initial software sales. It also explains why the China AI-powered Energy Management Software Market is becoming more dependent on execution capability and local service depth, rather than on product features alone.

Cloud-based deployment held 58.16% of the China AI-powered Energy Management Software Market share in 2025, making it the largest deployment mode. This position reflected the rapid expansion of domestic cloud infrastructure and the appeal of centralized access, faster updates, and wider data aggregation across distributed operations. The market benefited from this setup because many enterprises wanted to connect monitoring, analytics, and reporting without building fully separate software stacks at every site. Cloud deployment also suited newer use cases in commercial buildings and lighter industrial environments where buyers favored flexibility and lower upfront infrastructure needs.

Hybrid deployment is projected to expand at a 21.34% CAGR from 2026 to 2031, which makes it the fastest-growing architecture in the market. This pattern aligns with the stricter treatment of energy and operational data, especially for utilities and critical infrastructure operators that must keep sensitive information within approved jurisdictions. The December 2025 measures for data security management in the energy sector, effective July 1, 2026, strengthened the practical case for keeping some data and workloads local while still using cloud resources for aggregation and reporting. The China AI-powered Energy Management Software Market is therefore shifting toward deployment models that let enterprises keep OT-sensitive functions close to the asset while still using broader analytics and benchmarking across sites. That balance is likely to remain important as AI use cases expand across utilities, manufacturing, and data center environments.

Complete Report Scope:

• By Component
  • Software
  • Services
• By Deployment Mode
  • Cloud-Based
  • On-Premises
  • Hybrid
• By Application
  • Energy Consumption and Demand Optimization
  • Asset Performance and Predictive Maintenance
  • Smart Grid and Distributed Energy Resource (DER) Management
  • Renewable Energy Forecasting and Integration
  • Energy Trading, Pricing and Market Intelligence
• By End User
  • Utilities
  • Commercial Buildings
  • Industrial Facilities
  • Residential Buildings

List of Companies Covered in this Report:

1. Siemens AG
2. Schneider Electric SE
3. ABB Ltd.
4. Honeywell International Inc.
5. IBM Corporation
6. Johnson Controls International plc
7. Oracle Corporation
8. Microsoft Corporation
9. Amazon Web Services, Inc.
10. C3.AI, Inc.
11. Bidgely, Inc.
12. Grid4C Ltd.
13. Innowatts, Inc.
14. EnergyCAP, LLC
15. Enel X S.r.l.
16. Guoneng Rixin
17. State Grid Corporation of China
18. Dexma Sensors, S.L.
19. Rockwell Automation, Inc.
20. Envision Digital

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

• 1.1 Study Assumptions and Market Definition
• 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET LANDSCAPE

• 4.1 Market Overview
• 4.2 Market Drivers
  • 4.2.1 Rising Need for Real-Time Energy Optimization in Commercial and Industrial Facilities
  • 4.2.2 Integration of AI With Smart Grid and Distributed Energy Resources
  • 4.2.3 Increasing Demand for Automated Demand Response and Peak Load Management
  • 4.2.4 Expansion of ESG Reporting and Carbon Accounting Workflows
  • 4.2.5 Edge AI Adoption for Site-Level Energy Control and Fault Detection
  • 4.2.6 Growing Retrofit Demand From Aging Building and Industrial Infrastructure
• 4.3 Market Restraints
  • 4.3.1 High Integration Complexity With Legacy OT and IT Systems
  • 4.3.2 Data Quality, Interoperability, and Sensor Fragmentation Issues
  • 4.3.3 Cybersecurity and Data Sovereignty Concerns for Critical Energy Assets
  • 4.3.4 Payback Uncertainty in Small and Mid-Sized Sites With Limited Load Density
• 4.4 Impact of Macroeconomic Factors on the Market
• 4.5 Industry Value-Chain Analysis
• 4.6 Regulatory Landscape
• 4.7 Technological Outlook
• 4.8 Porter's Five Forces Analysis
  • 4.8.1 Bargaining Power of Buyers
  • 4.8.2 Bargaining Power of Suppliers
  • 4.8.3 Threat of New Entrants
  • 4.8.4 Threat of Substitutes
  • 4.8.5 Intensity of Competitive Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Component
  • 5.1.1 Software
  • 5.1.2 Services
• 5.2 By Deployment Mode
  • 5.2.1 Cloud-Based
  • 5.2.2 On-Premises
  • 5.2.3 Hybrid
• 5.3 By Application
  • 5.3.1 Energy Consumption and Demand Optimization
  • 5.3.2 Asset Performance and Predictive Maintenance
  • 5.3.3 Smart Grid and Distributed Energy Resource (DER) Management
  • 5.3.4 Renewable Energy Forecasting and Integration
  • 5.3.5 Energy Trading, Pricing and Market Intelligence
• 5.4 By End User
  • 5.4.1 Utilities
  • 5.4.2 Commercial Buildings
  • 5.4.3 Industrial Facilities
  • 5.4.4 Residential Buildings

6 COMPETITIVE LANDSCAPE

• 6.1 Market Concentration
• 6.2 Strategic Moves
• 6.3 Market Share Analysis
• 6.4 Company Profiles (includes Global Level Overview, Market Level Overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share, Products and Services, Recent Developments)
  • 6.4.1 Siemens AG
  • 6.4.2 Schneider Electric SE
  • 6.4.3 ABB Ltd.
  • 6.4.4 Honeywell International Inc.
  • 6.4.5 IBM Corporation
  • 6.4.6 Johnson Controls International plc
  • 6.4.7 Oracle Corporation
  • 6.4.8 Microsoft Corporation
  • 6.4.9 Amazon Web Services, Inc.
  • 6.4.10 C3.ai, Inc.
  • 6.4.11 Bidgely, Inc.
  • 6.4.12 Grid4C Ltd.
  • 6.4.13 Innowatts, Inc.
  • 6.4.14 EnergyCAP, LLC
  • 6.4.15 Enel X S.r.l.
  • 6.4.16 Guoneng Rixin
  • 6.4.17 State Grid Corporation of China
  • 6.4.18 Dexma Sensors, S.L.
  • 6.4.19 Rockwell Automation, Inc.
  • 6.4.20 Envision Digital

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space and Unmet-Need Assessment