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

According to Mordor Intelligence, the united kingdom AI-powered energy management software market size was valued at USD 236.33 million in 2025 and estimated to grow from USD 274.33 million in 2026 to reach USD 613.28 million by 2031, at a CAGR of 17.46% during the forecast period 2026-2031.

This report is Segmented by Component (Software and Services), Deployment Mode (Cloud, On-Premises, and Hybrid), Application (Energy Control, Asset Performance, Smart Grid Analytics, Renewable Energy Management, and More), and End User (Utilities, Commercial Buildings, Industrial Facilities, and Residential). The Market Forecasts are Provided in Terms of Value (USD).

United Kingdom AI-Powered Energy Management Software Market Trends and Insights

AI-Based Real-Time Load Optimization Drives Structural Platform Demand

The United Kingdom AI-powered energy management software market is gaining from the shift in real-time load optimization from a performance feature to a grid management requirement as distributed assets become harder to coordinate. National Grid's Emerald AI trial showed that AI-enabled data centers could flex power demand by up to 40% in under a minute in response to live grid signals. The Department for Science, Innovation and Technology stated in June 2026 that probabilistic and risk-aware optimization is the key feature that separates production-grade deployment from pilot activity in clean energy applications. This matters because many grid-edge and industrial assets need control decisions in sub-second timeframes, which gives an advantage to suppliers that combine edge computing with AI rather than relying only on remote cloud execution. As a result, buying criteria in the United Kingdom AI-powered energy management software market are moving toward local inference, rapid response, and deeper control-system integration.

UK Net-Zero Compliance Generates Policy-Led Software Demand

The United Kingdom AI-powered energy management software market is also supported by policy obligations that are more durable than short-cycle cost-reduction programs. The government's climate action update in June 2026 backed the proposed 7th Carbon Budget, which targets an 87% reduction in emissions across 2038-2042 and reinforces the long planning horizon for low-carbon infrastructure decisions. The Energy Digitalization Framework described digitalization as enabling infrastructure for a coordinated, connected energy system and tied it directly to the need for 51-66 GW of flexible capacity. The same policy direction is likely to shorten the path from strategy to procurement, especially for regulated operators, which typically accelerate system investment once governance standards and data rules are formalized. This gives the UK AI-powered energy management software market a policy base that extends beyond energy savings alone and supports demand in utilities, infrastructure, and larger commercial estates.

Legacy OT Integration And Data Interoperability Complexity Slows Deployment

Legacy control environments remain a major brake on the United Kingdom AI-powered energy management software market because many energy assets were not designed for interoperable data exchange or modern cyber controls. DESNZ research on operational technology vulnerabilities stated that many of these systems have little integrated protection beyond the IT network that manages them. This pushes buyers into longer deployment cycles because platform providers must address data extraction, control logic, safety validation, and compliance review before live operation begins. The Energy Sector Cyber Security Strategy for 2026-2030 calls the bridge between OT engineering and cybersecurity one of the hardest and most important issues in the sector. Until common architectures are more widely established, integration costs will remain uneven across projects and will keep slowing deployment in the United Kingdom AI-powered energy management software market.

Other drivers and restraints analyzed in the detailed report include:

1. Rising Electricity Price Volatility Sharpens The Commercial Case
2. Smart Meter And IoT Data Readiness Expands AI Use Cases
3. Cybersecurity And Data Sovereignty Concerns Constrain Enterprise Adoption Pace

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

Segment Analysis

Software held 41.07% of the market in 2025, making it the leading offering in the United Kingdom AI-powered energy management software market. Buyers favored integrated software because it could combine baseline monitoring, optimization, and reporting in a single operating layer. This was especially relevant for commercial buildings, utilities, and industrial facilities that needed better visibility across multiple assets and reporting requirements. The SaaS model also supported wider adoption by reducing initial contract size and allowing vendors to expand later through analytics modules, connectors, and integration layers.

Services are projected to expand at a 19.78% CAGR through 2031, making them the fastest-growing segment. This growth reflects the fact that many buyers need help with OT integration, model retraining, controls tuning, and ongoing reporting after the initial software purchase. Trane Technologies' move into BrainBox AI showed how equipment and building-system players are using AI software capabilities to deepen long-term service value in energy management and autonomous building control. In practice, the value gap between software installation and realized savings is pushing more contracts toward managed or outcome-linked services across the UK AI-powered energy management software market.

Cloud-based deployment accounted for 58.15% of the United Kingdom AI-powered energy management software market size in 2025, which kept it as the dominant deployment model. Many commercial building operators preferred cloud platforms because they offered faster onboarding and lower infrastructure burden than isolated on-premises systems. Cloud platforms also fit well with broader interoperability goals because they can expose standardized interfaces across meter, asset, and consumer data environments. This made cloud deployment a practical choice in less latency-sensitive use cases where central analytics and portfolio-wide visibility mattered most.

Hybrid deployment is projected to expand at a 18.67% CAGR through 2031 and is gaining popularity because some control decisions must be made close to the asset. Battery storage, EV charging, heat pumps, and smart inverters often need very fast local decision-making, which pure cloud architectures cannot always deliver. The push toward hybrid systems is therefore based on operational need as much as on cyber assurance or data residency concerns. This balance between central analytics and local control is likely to keep hybrid adoption strong as the UK AI-powered energy management software market moves deeper into utility, industrial, and grid-edge use cases.

Complete Report Scope:

• By Offering
  • 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. C3.ai, Inc.
2. GridBeyond Limited
3. Uplight, Inc.
4. Bidgely, Inc.
5. Copperleaf Technologies Inc.
6. BrainBox AI Inc.
7. Kaluza Limited
8. Verdigris Technologies, Inc.
9. EnergyCAP, LLC
10. Schneider Electric SE
11. Spacewell International N.V.
12. Wattics Limited
13. Dexma Sensors, S.L.U.
14. Smart Energy Water, Inc.
15. Encentiv Energy, Inc.
16. Logical Buildings, Inc.
17. nZero, Inc.
18. Energy Elephant Limited
19. Passiv Systems Limited
20. EM3
21. Greenbyte AB
22. Siemens AG
23. RetrofitAI, Inc.
24. Noda AI Ltd.
25. Power Factors, 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 AI-Based Real-Time Load Optimization Across Distributed Assets
  • 4.2.2 United Kingdom Net-Zero Compliance and Carbon Reporting Pressure
  • 4.2.3 Rising Electricity Price Volatility and Peak Demand Exposure
  • 4.2.4 Smart Meter, IoT, and Building Automation Data Readiness
  • 4.2.5 Digital Twin Adoption for Predictive Energy Control
  • 4.2.6 Data Center, Commercial Property, and Industrial Efficiency Mandates
• 4.3 Market Restraints
  • 4.3.1 Legacy OT Integration and Data Interoperability Complexity
  • 4.3.2 Cybersecurity and Data Sovereignty Concerns
  • 4.3.3 Unclear Payback Period for Mid-Market Buyers
  • 4.3.4 Shortage of AI, Energy, and Controls Talent
• 4.4 Industry Value Chain Analysis
• 4.5 Impact of Macroeconomic Factors on the Market
• 4.6 Regulatory Landscape
• 4.7 Technological Outlook
• 4.8 Porter's Five Forces Analysis
  • 4.8.1 Threat of New Entrants
  • 4.8.2 Bargaining Power of Buyers
  • 4.8.3 Bargaining Power of Suppliers
  • 4.8.4 Threat of Substitutes
  • 4.8.5 Competitive Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Offering
  • 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 C3.ai, Inc.
  • 6.4.2 GridBeyond Limited
  • 6.4.3 Uplight, Inc.
  • 6.4.4 Bidgely, Inc.
  • 6.4.5 Copperleaf Technologies Inc.
  • 6.4.6 BrainBox AI Inc.
  • 6.4.7 Kaluza Limited
  • 6.4.8 Verdigris Technologies, Inc.
  • 6.4.9 EnergyCAP, LLC
  • 6.4.10 Schneider Electric SE
  • 6.4.11 Spacewell International N.V.
  • 6.4.12 Wattics Limited
  • 6.4.13 Dexma Sensors, S.L.U.
  • 6.4.14 Smart Energy Water, Inc.
  • 6.4.15 Encentiv Energy, Inc.
  • 6.4.16 Logical Buildings, Inc.
  • 6.4.17 nZero, Inc.
  • 6.4.18 Energy Elephant Limited
  • 6.4.19 Passiv Systems Limited
  • 6.4.20 EM3
  • 6.4.21 Greenbyte AB
  • 6.4.22 Siemens AG
  • 6.4.23 RetrofitAI, Inc.
  • 6.4.24 Noda AI Ltd.
  • 6.4.25 Power Factors, Inc.

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-space and Unmet-Need Assessment