1Buildings, including residential, commercial, and industrial account for nearly 30% of global final energy use and about 40% of energy-related CO₂ emissions.1 With rapid urbanization, economic growth, and the impacts of climate change, building energy demand is projected to rise significantly, making the sector vital for achieving global climate and sustainability goals.2 Managing this growing energy demand is therefore critical to reducing emissions, enhancing energy security, and ensuring sustainable urban development. Effective energy management in buildings involves strategically planning, monitoring, and optimizing energy consumption while enhancing comfort and minimizing environmental impact.3 AI is increasingly seen as a game-changer in this space. By enabling real-time analytics, forecasting demand, predictive control, and smart decision-making, AI helps maximize system efficiency and support low-carbon building operations.4 This article highlights the challenges and opportunities of AI in building energy management, presents real-world AI applications in building energy management, and concludes with a forward-looking perspective and strategic recommendations for future implementation.
2Between 2010 and 2022, global building energy use grew by about 1.1% per year, reaching around 133 exajoules (EJ).1 Although digital technology has advanced quickly, most buildings are still designed and operated without using these tools. This results in wasted energy and poor indoor comfort. In Europe, for instance, 85% of buildings were built before modern energy standards, which contributes to about 75% of them performing poorly in terms of energy use.5 That said, energy performance in both new and older buildings is gradually improving, helped by the growing adoption of digital technologies.1 According to the IEA’s assessment report4 shown in Figure 1, AI is most advanced in operational optimization, benefiting the buildings sector through smart HVAC control, predictive maintenance, and real-time energy management. However, its use in resource management, system design, and automation remains limited or currently not available. This suggests AI is currently focused more on improving daily operations than shaping the overall design or energy strategy of buildings.
Figure 1: AI applications for energy optimization by sector
Source: Adapted from IEA (2025).4
3Building energy management is being transformed by digital technology, unlocking new opportunities to improve efficiency, reduce emissions, and enhance system responsiveness. However, key challenges persist. This section explores these issues alongside emerging opportunities for developing smarter, more sustainable buildings.
4Most buildings function as passive energy users,4 which makes effective energy management difficult. They often cannot adjust their energy use based on electricity prices, grid needs, or available renewable energy, like solar power. This leads to high energy demand in the evening when solar output is low, thus increasing both costs and reliance on fossil fuels. Without smart systems, buildings tend to operate inefficiently, wasting energy and missing chances to use solar panels or batteries. Additionally, many building occupants and operators have low awareness about their role in managing energy.6 They may not understand how their behaviour affects energy use or how digital tools can help. Without proper engagement, training, and incentives, even the best technologies may go unused. This mix of passive systems and passive users makes it harder to improve building energy performance.
5Many buildings operate with separate systems for lighting, HVAC, security, and energy monitoring, often supplied by different vendors using incompatible software or data formats. These siloed systems rarely communicate with each other, leading to inefficiencies and missed opportunities for holistic optimization across the building. Legacy equipment frequently relies on proprietary protocols, making integration with existing and modern Building Energy Management Systems (BEMS) complex and costly. Without standardisation of data formats and communication protocols, it becomes difficult to consolidate data into a unified platform for comprehensive analysis. In some cases, data is locked within closed systems or missing entirely due to limited sensor coverage or reliance on manual record-keeping. Additionally, organizational and privacy barriers often restrict access to data, with facility managers, building owners, and energy service providers each having only partial visibility.6
6Traditional energy monitoring in buildings typically relies on utility meters that provide only total energy consumption, offering little insight into how energy is used across specific systems or spaces. This lack of granularity is especially common in older buildings, which often lack sub-meters, smart sensors, or connected devices needed for real-time monitoring. As a result, building operators are unable to detect inefficiencies, respond promptly to issues, or optimize energy use effectively. To address these limitations, BEMS emerged in the 1990s, integrating sensors, microprocessors, and communication networks to monitor and control significant energy use in the building such as HVAC and lighting.7 However, widespread adoption of BEMS has been slow due to high installation costs and technical complexity.6 These challenges are further compounded by limited digital connectivity.4
7Implementing smart energy management systems in buildings presents significant challenges related to cybersecurity and data privacy. As energy management systems in buildings increasingly rely on connected devices and real-time data collection, the risk of cyberattacks, data breaches, and unauthorized access grows. Such incidents can lead to data loss, operational disruptions, financial losses, and reputational harm. Moreover, this smart energy management system often collects sensitive information, including building performance metrics and occupant behaviour data, raising additional privacy concerns.8 These risks can create resistance among building owners and operators to adopt digital systems, particularly in sectors with strict regulatory or privacy requirements.
8A growing challenge in using AI for energy management in buildings is balancing the energy used by AI systems with the energy savings it helps achieve. IEA estimates that emissions reductions from the broad application of existing AI-led solutions to be equivalent to around 5% of energy-related emissions in 2035.4 However, running these AI models, especially large or cloud-based ones, can consume significant electricity. Studies show that training large AI models can use as much energy as several cars over their lifetimes.8 This is especially concerning when AI is used in real-time control or digital twins, which require constant data processing. To solve this, researchers are exploring more energy-efficient AI approaches, such as smaller models, edge computing (processing data locally), and smarter algorithms that use less power.9 The key challenge is to ensure that AI helps buildings save more energy than it consumes, so that its use supports, rather than contradicts, sustainability goals.
9The evolution of AI marks a shift from early rule-based and symbolic reasoning systems to advanced, data-driven models, overcoming past limitations in scalability and adaptability.10 Recent breakthroughs in computing power, data availability, and ML algorithms, particularly deep neural networks, have driven the rapid rise of advanced AI, including generative models.4 Since 2008, the amount of training data has increased nearly 30,000 times, and computational power used to train state-of-the-art models has grown by around 350,000 times since 2014, while hardware costs have sharply declined.1 These advancements have created powerful, flexible AI systems that are no longer confined to academic research but are now central to high-value industries, including energy management in buildings.
10Advanced BEMS offer significant opportunities to improve energy efficiency, reduce operational costs, and support sustainability goals. Powered by technologies like IoT, cloud computing, AI, and ML, modern BEMS can monitor, forecast and control HVAC, lighting, and other building systems in real time, optimising performance based on occupancy patterns, weather, and usage trends.11
11Emerging smart energy technologies in buildings offer vast opportunities for the integration of AI to optimize energy management. For instance, AI can optimize the performance of Renewable Energy (RE) systems such as solar PV by predicting generation based on weather data and aligning it with building energy demand.12 When coupled with battery storage, AI can intelligently manage charging and discharging to maximise self-consumption, reduce peak demand, and respond to dynamic electricity tariffs.13 In smart HVAC systems, AI can continuously adjust settings based on occupancy, weather forecasts, and indoor environmental quality, ensuring comfort while minimising energy use.14 Smart lighting integrated with occupancy and daylight sensors can be further enhanced through AI to adapt lighting schedules and intensities dynamically.15 Additionally, AI can be applied to smart meters, smart appliances, and building-integrated electric vehicle (EV) charging infrastructure, optimising energy flows across the entire building ecosystem.
12Energy-as-a-Service (EaaS) models offer a practical way to incorporate AI in building energy management. EaaS allows building owners to access energy efficiency upgrades, smart technologies, and RE solutions through performance-based or subscription models, where payments are made based on actual energy savings or service outcomes. Through approaches like Energy Performance Contracting (EPC), building owners can partner with service providers who install and manage AI-enhanced systems, while being paid through shared energy savings.
13Supportive policies and incentives worldwide are accelerating the adoption of AI in building energy management. In the EU, the revised Energy Efficiency Directive (2023/1791) mandates performance improvements in large buildings and promotes smart technologies,16 while the Digital Europe Programme allocates €7.5 billion for digital transformation, including AI and IoT for energy efficiency.17 Singapore’s Green Mark Incentive Scheme co-funds AI-enabled BEMS under its Smart Nation strategy.18 In South Korea, the Korean New Deal and AI National Strategy support smart building pilots with subsidies for AI integration.19 In the U.S., the Inflation Reduction Act (2022) provides tax credits for smart energy upgrades.20 Cybersecurity is also a priority, with Germany’s BSI IT-Grundschutz21 and Australia’s Cyber Security Strategy 2023‒2030 addressing risks to smart energy systems.22 Broader policy frameworks, such as the EU’s Energy Performance of Buildings Directive and the U.S. ENERGY STAR program, further support the deployment of BEMS and smart energy technologies.23
14As energy use in buildings continues to grow, traditional energy management is shifting toward AI-driven systems. AI enables smarter, adaptive, and sustainable building operations through machine learning, predictive analytics, and real-time data. It helps forecast demand, optimize energy use, and automate decisions. This section explores current AI applications in building energy management, highlighting a real-world case study to demonstrate its benefits and integration.
15Modern AI-powered solutions provide a wide range of capabilities, from basic automation to advanced decision-making. These systems process large volumes of data from sensors, controls, and user inputs to deliver actionable insights. Integrating AI with IoT and digital twins enables effective monitoring and control of building systems like HVAC and lighting.
16Key AI-based functionalities in energy management include:
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AI in Smart HVAC Systems: Optimizes heating and cooling schedules based on occupancy patterns, weather forecasts, and thermal comfort models to reduce energy waste while maintaining comfort.
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AI-Powered Demand Response: Predicts peak load conditions and initiates automated load-shedding or load-shifting actions in response to utility price signals or grid conditions.
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Predictive Maintenance through AI: Detects anomalies and early signs of equipment failure using machine learning and sensor data, enabling timely maintenance, minimizing downtime and costs.
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Intelligent Building Operations with IoT and Digital Twins: Uses virtual building replicas and real-time data to simulate, analyze, and optimize operations such as lighting, HVAC, and space utilization before implementation.
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Energy Management with AI for Solar PV and Battery Integration: Manages renewable generation and battery systems by forecasting solar output, scheduling storage usage, and maximizing self-consumption or grid export based on tariff signals.
17These functionalities not only improve energy efficiency but also enhance user comfort, operational resilience, and cost-effectiveness.
18HVAC systems typically account for the largest share of energy usage in buildings. Figure 2 illustrates how AI-driven smart HVAC systems leverage machine learning to analyze occupancy patterns, weather forecasts, and historical usage data to optimize heating and cooling schedules. These systems adapt in real time, reducing energy waste while maintaining occupant comfort.
Figure 2: AI in Smart HVAC Systems
19A notable case is the Edge Building in Amsterdam, known as the “smartest building”. It uses AI-powered HVAC systems connected to 28,000 sensors that monitor occupancy, lighting, temperature, and humidity.24 This smart control reduced electricity usage by 70% and earned a 98.36% BREEAM sustainability rating.25
20Another example is the Siam Cement Group (SCG) in Bangkok, Thailand, where an AI-driven HVAC optimization system was implemented in partnership with Resync. It uses real-time occupancy and environmental data to adjust cooling, achieving a 15% reduction in HVAC energy consumption and enhancing operational efficiency.26
21These examples illustrate how AI improves energy efficiency, reduce operational costs, and contribute to decarbonization goals while enhancing occupant comfort.
22Demand response (DR) programs allow buildings to reduce or shift their electricity use during peak periods. AI enhances DR by predicting peak demand using historical data, weather, occupancy, and pricing signals. It then autonomously adjusts energy usage in real time, optimizing load reduction while maintaining operational efficiency.
23In a pilot project at multiple Google data centers, an AI-driven demand response system shifted non-urgent compute tasks based on grid stress signals. By anticipating grid events, it rescheduled workloads across time and location, reducing energy use without disrupting services which highlights the potential of AI to support grid reliability and lower peak energy use.27
24Unplanned equipment failures lead to energy inefficiency and increased costs. Figure 3 shows how AI-driven predictive maintenance uses anomaly detection and pattern recognition to identify signs of wear or failure before breakdowns occur. AI monitors real-time data such as vibration, airflow, and power consumption from HVAC and electrical equipment. This enables early fault detection, timely maintenance, reduced downtime, and improved system efficiency.
Figure 3: Predictive Maintenance through AI
25For instance, the Empire State Building in New York uses an AI-enabled systems for predictive maintenance across its HVAC and lighting systems. These systems analyze real-time sensor data such as temperature, occupancy, and CO₂ levels to detect anomalies, forecast potential equipment issues, and trigger timely maintenance alerts, resulting in 38% energy savings.28
26Similarly, Microsoft’s Redmond campus uses AI to monitor 125 buildings, identifying over 2,000 hidden anomalies. This predictive maintenance approach reduced energy waste, improved efficiency, and achieved energy savings of up to 20%.29
27Predictive maintenance improves energy efficiency, extends equipment lifespan, and lowers maintenance costs, supporting the achievement of ESG objectives.
28IoT and digital twins significantly enhance the AI capabilities in building energy management. IoT devices like smart meters, occupancy sensors, and temperature probes provide the granular, real-time data AI needs to make precise decisions.
29Digital twins simulate building operations, allowing AI to test energy-saving strategies virtually before real-world implementation. By running these simulations, AI can determine the best strategies for energy reduction, cost savings, and occupant comfort before implementing them in the real world.
30A prime example is Siemens’ headquarters in Munich, uses a digital twin fed by thousands of sensors to monitor and optimize building performance. Integrated with the Siemen’s Desigo CC platform, it enables real-time simulation of ventilation, lighting, and maintenance schedules, reducing energy use by over 30% and lowering maintenance costs.30
31AI plays a critical role in integrating clean energy technologies such as Solar PV systems and BESS into building operations. By forecasting renewable generation, predicting load demand, and optimizing battery usage, AI enables buildings to maximize self-consumption, reduce reliance on the grid, and lower operational costs.
32For example, Seattle’s 303 Battery is a 15-story net-zero energy apartment building that integrates solar panels on its roof, walls, and balconies, combined with lithium battery storage to power the building. Smart technologies, including sensors and AI-driven controls, optimize energy use by adjusting thermostats and lighting, maximizing solar self-consumption and efficiency. This approach enhances solar benefits, reduces energy costs, and supports sustainable living through effective load management and battery usage.31
33This demonstrate how AI not only facilitates clean energy adoption but also ensures its efficient utilization, making zero-energy buildings more achievable.
34As buildings continue to play a critical role in the global energy transition, the integration of AI into building energy management is poised to become a key enabler of efficiency, sustainability, and resilience. However, widespread adoption is hindered by some challenges. To overcome these barriers and unlock AI’s full potential, a coordinated approach involving all key stakeholders is essential.
35The following strategic recommendations summarised a roadmap for policymakers, building owners, technology providers, and researchers. Each group plays a distinct role: policymakers in establishing supportive frameworks, building owners in investing in digital readiness, technology providers in developing efficient and compatible solutions, and researchers in advancing validation and real-world applications. Together, these efforts can accelerate AI adoption and unlock its full potential in buildings.
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Stakeholder
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Key Strategic Recommendations
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Policymakers & Regulators
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- Develop national AI in energy roadmaps and standards for interoperability
- Expand funding and incentives for AI-enabled BEMS
- Enforce cybersecurity and data privacy regulations specific to smart energy systems
- Support EaaS by providing clear rules, standard contracts, and ways to share financial risks
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Building Owners & Operators
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- Invest in digital infrastructure (IoT sensors, connectivity)
- Adopt modular, scalable AI systems aligned with energy goals
- Engage occupants through intuitive interfaces and behavioural tools
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Technology Developers & Service Providers
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- Design explainable and trustworthy AI systems (XAI)
- Prioritise energy-efficient AI models for edge devices
- Ensure interoperability with legacy BEMS and third-party platforms
- Promote EaaS models to building owners
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Researchers & Academia
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- Advance hybrid AI models and energy systems
- Create open datasets and living testbeds for validation
- Investigate behavioural, social, and institutional barriers to adoption
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36These recommendations directly respond to the barriers identified earlier, including fragmented data, lack of integration, and limited real-time control. By aligning policy, technology, and research efforts, stakeholders can create a more unified framework for AI-driven energy management. This coordinated approach will accelerate digital transformation and enhance building efficiency, resilience, and long-term sustainability.
37This article highlights the strategic role of AI in transforming building energy management. While its potential is significant, challenges such as passive energy consumers, data fragmentation and lack of integration, limited real-time monitoring, control and connectivity, cybersecurity and privacy concerns, and the need to balance AI’s power consumption with its energy-saving benefits reduce overall performance, particularly in older buildings.
38Nevertheless, AI offers strong opportunities for smarter, more efficient, and sustainable buildings. Advanced AI applications, including integration with IoT and digital twins, enable smarter HVAC control, predictive maintenance, and energy optimization. AI also enhances solar PV, battery management, and demand response management. Real-world implementations from various regions show measurable improvements in energy savings, operational performance, and user comfort.
39AI’s ability to align building operations with occupancy patterns, weather data, and grid signals makes it an essential enabler of low-carbon, resilient infrastructure. Moving forward, policymakers should establish national AI in energy roadmaps, standards for interoperability, and funding mechanisms to support innovation. Building owners need to invest in digital infrastructure such as IoT devices and scalable AI platforms aligned with sustainability goals. Technology providers should focus on developing explainable, energy-efficient AI solutions compatible with existing BEMS. Meanwhile, researchers play a vital role in advancing hybrid AI models, creating open datasets, and addressing behavioural and institutional barriers to adoption.
40Through these coordinated actions, AI can be fully leveraged to enhance energy performance, optimize operations, and support the transition toward intelligent, self-sufficient buildings.
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