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市場調查報告書
商品編碼
2073312

亞太地區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）

Asia-Pacific AI-Powered Energy Management Software - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2026 - 2031)
出版日期: | 出版商: Mordor Intelligence | 英文 181 Pages | 商品交期: 2-3個工作天內

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簡介目錄

據 Mordor Intelligence 稱，2025 年亞太地區人工智慧能源管理軟體的市場規模為 9.5 億美元，預計到 2031 年將達到 31 億美元，2026 年至 2031 年的複合年成長率為 22.15%。

亞太地區人工智慧能源管理軟體市場-IMG1

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（例如，能源消耗和需求最佳化、資產性能和預測性維護）、最終用戶（例如，商業建築、工業設施）和地區進行細分。市場預測以美元計價。

亞太地區人工智慧能源管理軟體市場的趨勢與洞察。

商業和工業設施對即時能源最佳化的需求日益成長。

亞太地區商業和工業設施營運商面臨著收費系統，這些結構對總用電量和尖峰需求都造成了沉重的負擔。在印度，高壓配電價格包含分時尖峰電價，其比離峰時段時段電價高出40%至70%。需求費用也佔總電費的30%至40%。這種電價收費系統降低了被動監控的價值，並增加了對人工智慧平台的需求，這些平台能夠實現電池儲能調度、太陽能發電輸出管理和即時負載轉移。亞太地區的人工智慧能源管理軟體市場正受益於此趨勢，因為大規模製造地和商業設施通常需要在不中斷核心營運的情況下調整能源使用。 2026年2月，Honeywell和塔塔諮詢服務公司宣佈建立合作夥伴關係，旨在推動人工智慧驅動的建築和工業自主運營，印度是其首個重點市場。

人工智慧與智慧電網和分散式能源的整合

人工智慧與智慧電網和分散式能源的融合正在提升電力生產調整的品質和全部區域的系統可視性。韓國電力公司（KEPCO）經營一個虛擬電廠平台，該平台聚合了超過2.8吉瓦的分散式能源，並利用人工智慧來調整電池、暖通空調系統和工業負載。在中國，國家能源局等相關機構於2026年5月發布了一項行動計劃，其中包括51個人工智慧和能源應用場景以及2030年的容量目標，進一步加強了支援體系。實施和營運這些系統的電力公司將透過同時扮演供應商、營運商和標竿客戶三重角色而獲得競爭優勢。這凸顯了成熟的編配工具在亞太地區人工智慧驅動的能源管理軟體市場中的價值，對於需要可在電網、負載和儲能層運行的軟體的電力公司而言，這一點尤其重要。

與傳統OT和IT系統進行高階整合的複雜性。

亞太地區人工智慧驅動的能源管理軟體市場面臨的主要障礙之一是現代軟體堆疊與傳統操作技術（OT）之間的鴻溝。許多電力公司、工廠和大型建築仍然依賴專有協議和控制系統，這些協議和系統並非為開放資料交換而設計。因此，供應商被迫建立特定站點的連接器和中間件，導致成本增加和部署時間延長。此外，能源和工業資產的更新周期通常為15至25年，而非幾年，這比典型的軟體更新周期要長，加劇了這個問題。因此，能夠以最小的客製化程度處理混合環境的供應商更有可能在公共產業和重工業領域拓展業務。

細分市場分析

到了2025年，軟體在亞太地區人工智慧能源管理軟體市場中佔70.18%的佔有率。這反映出市場對平台主導部署的強烈偏好，而非孤立的單一功能工具。訂閱模式將支出納入營運預算，簡化了許多用戶的購買流程，從而支撐了這一佔有率的成長。此外，由於預測、最佳化和報告功能預計將在單一介面上完成，而非分散在各個工具中，軟體作為領先的商業層級也繼續保持其地位。在亞太地區的人工智慧能源管理軟體市場，這一趨勢使得擁有更廣泛平台功能的供應商在部署的早期和中期階段擁有明顯的優勢。

預計2026年至2031年間，服務業的複合年成長率將達到22.23%，這意味著對實施支援的需求成長速度與核心軟體的需求成長速度大致相同。即使在初始部署之後，客戶仍然需要系統整合、模型調優、資料管道維護和工作流程客製化等服務。這意味著服務在部署後仍然至關重要，一旦平台投入日常運營，轉換成本就會增加。Honeywell與塔塔諮詢服務公司於2026年2月簽署的合作協議正是基於這一趨勢，該協議將雙方的技術能力與在建築和工業設施領域久經考驗的實施經驗相結合。

到2025年，在包括中國、印度、新加坡、日本和澳洲在內的國家數位基礎設施不斷擴展的推動下，基於雲端的採用率將達到61.14%。雲端模式對那些希望減少內部IT需求、更輕鬆地進行更新以及在多個地點快速部署的用戶極具吸引力。這尤其適合那些不希望維護自身完整資料堆疊的商業建築營運商和小規模公共產業。雖然從擴充性的角度來看，雲端在亞太地區人工智慧驅動的能源管理軟體市場仍然備受青睞，但這種趨勢並非適用於所有終端用戶。

預計2026年至2031年間，混合部署的複合年成長率將達到22.34%，成為市場上成長最快的部署模式。這反映了電力公司和工業營運商的需求：他們希望對延遲敏感型功能進行本地控制，同時利用雲端分析實現更廣泛的最佳化。此外，當由於資料主權策略和營運風險導致全面雲端遷移困難時，混合配置也更易於被接受。從長遠來看，這將為能夠整合邊緣處理、本地控制和集中式分析，而無需強迫客戶採用單一架構的供應商創造新的商機。

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。
    • 人工智慧與智慧電網和分散式能源的整合
    • 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
    • 智慧電網與分散式能源（DER）的管理
    • 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
  • 按地區
    • 中國
    • 印度
    • 日本
    • 韓國
    • 澳洲和紐西蘭
    • 其他亞太國家

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
    • GE Vernova Inc.
    • Rockwell Automation, Inc.
    • Eaton Corporation plc
    • Microsoft Corporation
    • Amazon Web Services, Inc.
    • Oracle Corporation
    • C3.ai, Inc.
    • Bidgely, Inc.
    • Hitachi Energy Ltd.
    • Innowatts, Inc.
    • Enel X Srl
    • GridPoint, Inc.
    • Envision Digital International
    • AutoGrid Systems, Inc.

第7章 市場機會與未來展望

簡介目錄
Product Code: 99579

According to Mordor Intelligence, the asia-Pacific AI-powered energy management software market size was valued at USD 0.95 billion in 2025 and is forecast to reach USD 3.10 billion by 2031, advancing at a CAGR of 22.15% during 2026-2031.

Asia-Pacific AI-Powered Energy Management Software - Market - IMG1

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), End User (Commercial Buildings, Industrial Facilities, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Asia-Pacific AI-Powered Energy Management Software Market Trends and Insights

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

Commercial and industrial operators across the Asia-Pacific are dealing with tariff structures that penalize peak demand as heavily as total consumption. In India, high-tension distribution tariffs include time-of-day peak rates that run 40-70% above off-peak levels, while demand charges account for 30-40% of total electricity bills. This pricing structure devalues passive monitoring and increases demand for AI platforms that can schedule battery storage, manage solar output, and shift loads in real time. The Asia-Pacific AI-powered energy management software market is benefiting from this trend, as large manufacturing and commercial sites can often adjust energy use without interrupting core operations. Honeywell and Tata Consultancy Services announced a partnership in February 2026 to advance AI-driven autonomous operations for buildings and industries, with India as the initial focus market.

AI Integration with Smart Grids and Distributed Energy Resources

AI integration with smart grids and distributed resources is improving dispatch quality and system visibility across the region. Korea Electric Power Corporation operates a virtual power plant platform that aggregates more than 2.8 GW of distributed resources and uses AI to coordinate batteries, HVAC systems, and industrial loads. China added another layer of support in May 2026 when the National Energy Administration and other agencies issued an action plan with 51 AI and energy application scenarios and a 2030 capability target. Utilities that both deploy and operate these systems gain an advantage because they become vendors, operators, and reference customers simultaneously. This underscores the value of proven orchestration tools in the Asia-Pacific AI-powered energy management software market, especially for utilities that need software that can operate across grid, load, and storage layers.

High Integration Complexity with Legacy OT And IT Systems

A major obstacle in the Asia-Pacific AI-powered energy management software market is the gap between modern software stacks and long-installed operational technology. Many utilities, factories, and large buildings still rely on proprietary protocols and control systems that were never designed for open data exchange. That forces vendors to build site-specific connectors and middleware, which raises cost and extends deployment timelines. The problem also lasts longer than a normal software cycle because energy and industrial assets are often replaced over 15-25 years rather than every few years. As a result, vendors that can handle mixed environments with less customization are more likely to scale across public utilities and heavy industry.

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 held 70.18% of the Asia-Pacific AI-powered energy management software market share in 2025, reflecting the strong preference for platform-led deployments over isolated point tools. This share was supported by subscription models that moved spending into operating budgets and simplified the buying process for many users. Software also remained the main commercial layer because forecasting, optimization, and reporting are now expected to sit on a single interface rather than across separate tools. Within the Asia-Pacific AI-powered energy management software market, this gave vendors with broader platform capability a clear advantage in early and mid-stage deployments.

Services are projected to expand at a 22.23% CAGR during 2026-2031, which shows that deployment support is rising almost as quickly as core software demand. Clients still need system integration, model tuning, data pipeline maintenance, and workflow customization after the initial installation. This keeps services relevant well beyond launch and raises switching costs once a platform is embedded into daily operations. Honeywell's February 2026 partnership with Tata Consultancy Services reflected this direction by pairing technology capability with implementation depth for buildings and industrial sites.

Cloud-based deployment accounted for 61.14% of the market in 2025, supported by expanding digital infrastructure across China, India, Singapore, Japan, and Australia. Cloud models appeal to users who want lower in-house IT requirements, easier updates, and faster rollout across multiple sites. This has been especially relevant for commercial building operators and smaller utilities that do not want to maintain their own full data stack. The Asia-Pacific AI-powered energy management software market continues to favor cloud for scalability, but the pattern is not uniform across all end users.

Hybrid deployment is projected to grow at a 22.34% CAGR from 2026 to 2031, making it the fastest-growing mode in the market. This reflects the needs of utilities and industrial operators that want local control for latency-sensitive functions while still using cloud analytics for broader optimization. Hybrid setups are also easier to accept when data sovereignty policies or operational risk make a full cloud migration difficult. Over time, this creates an opening for vendors that can coordinate edge processing, on-site control, and centralized analytics without forcing customers into a single architecture.

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
  • By Geography
    • China
    • India
    • Japan
    • South Korea
    • Australia and New Zealand
    • Rest of Asia-Pacific

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. GE Vernova Inc.
  8. Rockwell Automation, Inc.
  9. Eaton Corporation plc
  10. Microsoft Corporation
  11. Amazon Web Services, Inc.
  12. Oracle Corporation
  13. C3.ai, Inc.
  14. Bidgely, Inc.
  15. Hitachi Energy Ltd.
  16. Innowatts, Inc.
  17. Enel X S.r.l.
  18. GridPoint, Inc.
  19. Envision Digital International
  20. AutoGrid Systems, Inc.

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 AI Integration With Smart Grids 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
  • 5.5 By Geography
    • 5.5.1 China
    • 5.5.2 India
    • 5.5.3 Japan
    • 5.5.4 South Korea
    • 5.5.5 Australia and New Zealand
    • 5.5.6 Rest of Asia-Pacific

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 GE Vernova Inc.
    • 6.4.8 Rockwell Automation, Inc.
    • 6.4.9 Eaton Corporation plc
    • 6.4.10 Microsoft Corporation
    • 6.4.11 Amazon Web Services, Inc.
    • 6.4.12 Oracle Corporation
    • 6.4.13 C3.ai, Inc.
    • 6.4.14 Bidgely, Inc.
    • 6.4.15 Hitachi Energy Ltd.
    • 6.4.16 Innowatts, Inc.
    • 6.4.17 Enel X S.r.l.
    • 6.4.18 GridPoint, Inc.
    • 6.4.19 Envision Digital International
    • 6.4.20 AutoGrid Systems, Inc.

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

  • 7.1 White-Space and Unmet-Need Assessment