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

According to Mordor Intelligence, the India AI-powered energy management software market size was USD 154.3 million in 2025 and is projected to reach USD 563.2 million by 2031, registering a CAGR of 24.43% during 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, Industrial Facilities, and More). The Market Forecasts are Provided in Terms of Value (USD).

India AI-powered Energy Management Software Market Trends and Insights

Rising Energy Tariffs and Peak-Demand Charges Across Commercial Facilities

Electricity tariff revisions across states have made energy costs harder to manage for large commercial and industrial users. The Tamil Nadu Electricity Regulatory Commission raised industrial tariffs by 3.4% to INR 7.5 per kWh (USD 0.079 USD per kWh) and increased demand charges to INR 608 per kVA (USD 6.43 per kVA) per month for FY2026, effective July 1, 2025. Similar pressure has been building in other high-tension consumer categories across major industrial states, making energy optimization software easier to justify in budget discussions. The direct value comes from AI-led load shifting during 15-minute peak intervals, because that is where India's tariff structure creates the sharpest billing exposure. In the India AI-powered Energy Management Software Market, this means demand optimization remains one of the quickest paths to visible savings for commercial campuses and factories.

Smart Meter Rollout Expands High-Frequency Energy Data Availability

India's smart meter rollout has created a much larger base of interval-level consumption data for utilities and service providers. By December 2025, 52.8 million smart meters had been deployed, and the broader distribution capex pipeline tied to sector modernization reached INR 11.2 trillion (USD 131 billion). In 2026, the Ministry of Power directed utilities to move AI and ML use cases beyond pilot programs, which pushed demand forecasting, loss analysis, and predictive maintenance into a more operational stage. The gap between sanctioned meters and installed meters has also put pressure on AMI providers, as delayed go-live schedules increase the need for stronger forecasting and analytics support. The 2026 amendment to the meter installation and operation rules also improved interoperability conditions, which matters because model accuracy depends on cleaner, more standardized data flows.

Fragmented Legacy OT and BMS Data Slows Model Deployment

Much of India's power and large-building infrastructure still runs on older SCADA, BMS, and PLC environments that were not built for smooth IT and OT integration. That creates data normalization issues across sites, vendors, and protocol generations, pushing deployment timelines from weeks to several months. In utilities and large industrial facilities, this problem is more difficult because different generations of control systems often operate side by side, and each one can produce data in a different format. Honeywell's February 2026 partnership with TCS in India was aimed directly at this problem by combining OT connectivity, cloud modernization, and AI-led operations support. In the India AI-powered Energy Management Software Market, integration capability has therefore become a core product differentiator rather than an implementation detail.

Other drivers and restraints analyzed in the detailed report include:

1. Net-Zero Commitments Push Continuous Energy Optimization Programs
2. Expansion of Distributed Energy Resources Raises Need for AI Orchestration
3. Cybersecurity and Data Sovereignty Concerns Limit Cloud Adoption in Critical Sites

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

Segment Analysis

Software accounted for 66.21% of India AI-powered Energy Management Software Market in 2025, keeping the category centered on platform-led buying patterns rather than isolated tools. Buyers have favored software because it enables demand forecasting, asset diagnostics, and renewable integration to be housed in a single operating layer rather than separate applications. This matters because utilities and large industrial groups typically want a single system of record for energy performance across multiple sites. The recurring-revenue nature of software contracts also fits the preference for long-term platform control in regulated and energy-intensive sectors.

Services are projected to expand at a 25.47% CAGR through 2031, indicating that implementation work is becoming increasingly important as deployments move into more complex operating environments. Companies that have already purchased software increasingly need managed analytics, integration support, and site-level model tuning before operational value becomes visible. The Honeywell and TCS partnership reflected that shift by treating convergence between legacy OT and newer cloud analytics as an ongoing service task rather than a one-time setup. BEE's ECBC-linked metering and management requirements also create a baseline that vendors can build on through software upgrades and associated services.

Cloud-based deployment accounted for 56.17% of India AI-powered Energy Management Software Market in 2025, supported by lower upfront costs and easier access to analytics-intensive computing. Commercial building operators and mid-sized industrial users have leaned toward the cloud because it shortens deployment time and avoids heavy local infrastructure investment. For the market, that has helped cloud become the default option in less sensitive operating environments where speed and cost carry more weight than strict data control. The growing presence of hyperscale cloud infrastructure in India has also made cloud-based delivery more practical for software vendors that rely on real-time analytics.

Hybrid deployment is expected to grow at a 25.58% CAGR through 2031 because it addresses the tension between cloud efficiency and local control. In regulated power and utility settings, buyers often want sensitive OT data to remain on-site while less critical analytics workloads move to the cloud. That is why architecture flexibility is becoming a procurement requirement, especially for discoms, generators, and transmission-linked users. ABB's 2026 launch of BuildingPro Suites, which supports edge, on-premises, and cloud deployment, reflected this hybrid-first direction.

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. Eaton Corporation plc
7. Emerson Electric Co.
8. IBM Corporation
9. Microsoft Corporation
10. Oracle Corporation
11. SAP SE
12. C3.AI, Inc.
13. BrainBox AI Inc.
14. GridPoint, Inc.
15. Verdigris Technologies, Inc.
16. Uplight, Inc.
17. AutoGrid Systems, Inc.
18. EnergyCAP, LLC
19. Amp X Ltd.
20. GNA Energy
21. Greenovative Energy
22. Tata Power
23. AutoGrid Systems, Inc.
24. EnerCog

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 Energy Tariffs and Peak-Demand Charges Across Commercial Facilities
  • 4.2.2 Smart Meter Rollout Expands High-Frequency Energy Data Availability
  • 4.2.3 Net-Zero Commitments Push Continuous Energy Optimization Programs
  • 4.2.4 Expansion of Distributed Energy Resources Raises Need For AI Orchestration
  • 4.2.5 Retrofit-Heavy Building Stock Creates Large Software-First Efficiency Opportunity
  • 4.2.6 Demand For Remote Monitoring In Multi-Site Enterprises Accelerates Adoption
• 4.3 Market Restraints
  • 4.3.1 Fragmented Legacy OT and BMS Data Slows Model Deployment
  • 4.3.2 Cybersecurity and Data Sovereignty Concerns Limit Cloud Adoption in Critical Sites
  • 4.3.3 Weak Internal AI and Energy Analytics Skills Reduce Implementation Velocity
  • 4.3.4 Long Sales Cycles and Low-Capex Prioritization Delay Enterprise Purchases
• 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 Eaton Corporation plc
  • 6.4.7 Emerson Electric Co.
  • 6.4.8 IBM Corporation
  • 6.4.9 Microsoft Corporation
  • 6.4.10 Oracle Corporation
  • 6.4.11 SAP SE
  • 6.4.12 C3.ai, Inc.
  • 6.4.13 BrainBox AI Inc.
  • 6.4.14 GridPoint, Inc.
  • 6.4.15 Verdigris Technologies, Inc.
  • 6.4.16 Uplight, Inc.
  • 6.4.17 AutoGrid Systems, Inc.
  • 6.4.18 EnergyCAP, LLC
  • 6.4.19 Amp X Ltd.
  • 6.4.20 GNA Energy
  • 6.4.21 Greenovative Energy
  • 6.4.22 Tata Power
  • 6.4.23 AutoGrid Systems, Inc.
  • 6.4.24 EnerCog

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space and Unmet-Need Assessment