싱가포르의 AI 기반 에너지 관리 소프트웨어 시장 : 시장 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the Singapore AI-powered energy management software market size was valued at USD 48.61 million in 2025 and is estimated to grow from USD 59.02 million in 2026 to reach USD 168.12 million by 2031, at a CAGR of 23.29% during the forecast period (2026-2031).

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).

Singapore AI-powered Energy Management Software Market Trends and Insights

Rising Smart Building Retrofits in Singapore Commercial Real Estate

The Green Mark Incentive Scheme for Existing Buildings 2.0, a SGD 63 million (USD 46 million) program, lowered the financial barrier to retrofitting in privately owned buildings with a gross floor area above 5,000 m2. That directly widened the deployment base for the Singapore AI-powered Energy Management Software Market because more older buildings are now being fitted with the controls and metering needed for AI optimization. The Mandatory Energy Improvement regime also began to require action when energy-intensive buildings exceeded Energy Use Intensity thresholds for three consecutive years, pushing owners toward structured upgrade programs. These retrofit programs often require granular submetering, and that data layer lowers integration costs later when AI software is added on top. The effect is especially visible in hospitality and healthcare, where owners are trying to meet compliance, Green Mark recertification, and investor reporting needs within the same capital cycle.

Tightening Corporate Energy Reporting and ESG Disclosure Requirements

Mandatory climate reporting is moving energy data from a facilities issue into a finance and governance issue in Singapore. SGX-listed companies must report Scope 1 and Scope 2 emissions from the financial year 2025, and STI constituents must also report Scope 3 emissions from FY2026. That shift is driving demand for continuous, audit-ready energy data because periodic manual readings do not provide the consistency reporting teams now need. The carbon tax increase from SGD 25/tCO2e in 2024-2025 to SGD 45/tCO2e in 2026 is also making the business case more direct for finance teams that are deciding where to allocate spending. In the Singapore AI-powered Energy Management Software Market, this policy stack is aligning facilities, finance, and sustainability functions around a single software decision.

Data Fragmentation across Legacy Building Systems

Data fragmentation across older building estates remains one of the clearest barriers in the Singapore AI-powered Energy Management Software Market. Many commercial properties built before 2005 still operate with proprietary building management environments and uneven protocol implementation across BACnet, Modbus, and LonWorks systems. Retrofit layers added over time by different contractors have left gaps between meters, HVAC controls, lighting platforms, and access systems, which slows unified data ingestion. Those gaps degrade baseline quality, weakening forecast accuracy and making audit-ready reporting harder to defend. Vendors with strong edge-layer protocol translation are better placed to win retrofit-heavy projects because they can normalize data without forcing a full BMS replacement.

Other drivers and restraints analyzed in the detailed report include:

1. Accelerating Utility Tariff Optimization and Demand Charge Management
2. AI-Enabled Fault Detection, Diagnostics, and Predictive Control Adoption
3. Cybersecurity And Data Residency Concerns in Cloud-Hosted Energy Platforms

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

Segment Analysis

Software held 65.18% of the Singapore AI-powered Energy Management Software Market share in 2025, which reflected the depth of platform adoption across utilities, commercial real estate portfolios, and industrial facilities. Buyers have already embedded energy analytics, real-time dashboards, and carbon reporting into day-to-day operating workflows, which gives the software layer a central role in building operations. That position is reinforced by switching costs: once building management system data, historical baselines, and reporting workflows are housed on a single platform, vendor migration becomes disruptive for owners and operators. This helps incumbent software providers maintain stronger renewal rates than many adjacent enterprise software categories.

Services are projected to expand at a 24.31% CAGR from 2026 to 2031, making them the fastest-growing component of the Singapore AI-powered Energy Management Software Market. Mid-size commercial and industrial operators are driving much of this momentum because many lack in-house energy engineering teams and prefer managed outcomes over software administration. A multi-year AI-native ecosystem initiative from Singapore directly addressed this shift by linking energy monitoring and ESG reporting into a more continuous managed workflow. Outcome-based energy performance contracts are also becoming more attractive because they shift part of the execution risk to vendors and convert upfront investment into recurring operating expense.

Cloud-based deployment accounted for 55.14% of the Singapore AI-powered Energy Management Software Market in 2025, supported by subscription economics, continuous model updates, and easier benchmarking across multi-site portfolios. It also reduced the need for regular on-site hardware refresh cycles, which many property owners and operators prefer to avoid. Even so, hybrid deployment is projected to grow at a 24.42% CAGR from 2026 to 2031 and is becoming the preferred architecture for more regulated and complex environments. Enterprises are using hybrid setups to keep sensitive telemetry local while still sending normalized performance indicators into cloud analytics layers.

This balance fits the Singapore AI-powered Energy Management Software Market well because it gives buyers a way to combine AI scalability with tighter data control. It is particularly relevant in hospitals, data centers, and government-linked sites where air-gapping, local hosting, or stricter review processes still shape procurement. On-premises deployment, therefore, retains a role, especially where full cloud connectivity is operationally difficult or not preferred. Purpose-built edge inference hardware, including the Univers EnOS(TM) AI Box introduced at CES 2026, also shows how the gap between on-premises and hybrid architecture is narrowing for high-frequency control applications.

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. Schneider Electric SE
2. Siemens AG
3. Johnson Controls International plc
4. Honeywell International Inc.
5. ABB Ltd
6. Delta Electronics, Inc.
7. Eaton Corporation plc
8. IBM Corporation
9. Cisco Systems, Inc.
10. SAP SE
11. Oracle Corporation
12. C3.AI, Inc.
13. Univers
14. Keppel Infrastructure
15. Azendian Solutions
16. Planon B.V.
17. SP Digital
18. Carrier Global Corporation
19. Trane Technologies plc
20. SensorFlow

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 Smart Building Retrofits in Singapore Commercial Real Estate
  • 4.2.2 Tightening Corporate Energy Reporting and ESG Disclosure Requirements
  • 4.2.3 Accelerating Utility Tariff Optimization and Demand Charge Management
  • 4.2.4 AI-Enabled Fault Detection, Diagnostics, and Predictive Control Adoption
  • 4.2.5 Edge-Connected IoT Metering and Building Management System Integration
  • 4.2.6 Growing Enterprise Preference for Continuous Commissioning and Autonomous Optimization
• 4.3 Market Restraints
  • 4.3.1 Data Fragmentation Across Legacy Building Systems
  • 4.3.2 Cybersecurity and Data Residency Concerns in Cloud-Hosted Energy Platforms
  • 4.3.3 High Integration Complexity With Mixed Vendor Building Estates
  • 4.3.4 Limited Energy Baseline Visibility in Older Small and Medium Buildings
• 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 Schneider Electric SE
  • 6.4.2 Siemens AG
  • 6.4.3 Johnson Controls International plc
  • 6.4.4 Honeywell International Inc.
  • 6.4.5 ABB Ltd
  • 6.4.6 Delta Electronics, Inc.
  • 6.4.7 Eaton Corporation plc
  • 6.4.8 IBM Corporation
  • 6.4.9 Cisco Systems, Inc.
  • 6.4.10 SAP SE
  • 6.4.11 Oracle Corporation
  • 6.4.12 C3.ai, Inc.
  • 6.4.13 Univers
  • 6.4.14 Keppel Infrastructure
  • 6.4.15 Azendian Solutions
  • 6.4.16 Planon B.V.
  • 6.4.17 SP Digital
  • 6.4.18 Carrier Global Corporation
  • 6.4.19 Trane Technologies plc
  • 6.4.20 SensorFlow

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space and Unmet-Need Assessment