How Artificial Intelligence Is Transforming Energy Efficiency and Carbon Reduction in Buildings

Artificial Intelligence Tools Drive Energy Efficiency and Cut Carbon Emissions in Buildings

Energy headlines often focus on AI's role in meeting data centers' power demands, but AI is quietly reshaping the building sector too. By optimizing energy use and cutting carbon emissions, AI offers practical benefits for commercial buildings, especially medium-sized offices across the U.S.

A 2024 study from Lawrence Berkeley National Laboratory evaluated AI's impact on building energy efficiency and emissions reduction. It identified key improvement areas: equipment, occupancy, control systems, and design. The study projected AI could reduce energy consumption and emissions by 8% to 19% by 2050. When combined with supportive energy policies and low-carbon power sources, reductions could reach 40% and 90%, respectively, compared to business-as-usual scenarios.

How Utilities Can Leverage AI for Energy Efficiency

Nora Wang Esram, CEO of the New Buildings Institute, emphasizes that many existing building technologies already use machine learning, though not always recognized as AI. These include advanced controls, analytics, and load forecasting tools that can improve efficiency and reduce peak energy use but remain underused due to cost, complexity, or interoperability issues.

New AI capabilities—especially those based on large language models—can simplify these tools by interpreting data, recognizing patterns, and enabling natural language interactions. This makes energy management systems more accessible and connected across platforms. However, challenges persist such as limited high-quality, publicly available building data and a lack of awareness about AI's current and potential applications in building operations.

Esram notes that educating utilities on where to invest and how to implement AI effectively can unlock significant benefits in building efficiency and grid resilience. The New Buildings Institute focuses on scalable solutions to cut emissions while maintaining affordability and safety.

Real-World AI Applications in Building Efficiency

Machine learning is already used for advanced HVAC management by predicting occupancy and adjusting systems accordingly. Weather forecasts and grid signals are incorporated to optimize comfort and reduce energy waste.

AI is also speeding up administrative tasks like permitting by processing documents swiftly. Combining data from satellites, energy use, and building characteristics helps identify upgrade opportunities. AI-driven training tools assist building operators in diagnosing issues and engaging occupants effectively.

Digital twins—virtual models of buildings—paired with AI are becoming more affordable and user-friendly. AI enables operators to interact with these models in plain language, detect issues automatically, and receive actionable recommendations. This integration is crucial, as poorly managed high-performance buildings can fail to deliver expected energy savings.

Beyond energy, AI-enhanced digital twins can improve safety and security, and coordinate other building functions such as food services or airport operations, creating a comprehensive tool for overall building performance.

Schneider Electric’s AI Success in Commercial Real Estate

• Design and Construction: AI-driven design tools help incorporate sustainability metrics early, optimizing use of low-carbon materials and solar exposure to create efficient, attractive buildings.
• Operation and Maintenance: AI optimizes HVAC by analyzing real-time occupancy, weather, and thermal data, balancing efficiency with comfort. Predictive maintenance reduces repair costs and downtime.
• Asset Lifecycle and Tenant Experience: AI analyzes building data to guide facility managers in proactive, data-driven decisions—improving HVAC systems, managing microgrids, and forecasting maintenance needs to enhance tenant satisfaction.

BrainBox AI’s Deep Learning Powers HVAC Optimization

BrainBox AI, now part of Trane, offers platforms like ARIA that simplify building operations by transforming data into real-time insights and actions. The system allows facility managers to control HVAC and other equipment via voice or text commands.

Dollar Tree piloted BrainBox AI’s autonomous HVAC optimization in 600 stores across the U.S., achieving nearly 8 million kWh in electricity savings within a year. This resulted in over $1 million in cost savings and fewer maintenance requests, all without major equipment changes. The success has led to expanded deployment in thousands more stores.

Growing AI Adoption Among Commercial Building Managers

Honeywell’s 2025 study found 84% of U.S. commercial building decision-makers plan to increase AI use for security, energy management, and predictive maintenance. However, 92% face challenges hiring skilled staff to handle AI technologies.

Honeywell launched Connected Solutions, an AI-powered platform integrating building software into one interface. Early users like Verizon and Vanderbilt University report improved system efficiency, energy savings, and easier issue prediction. The platform offers advanced encryption, remote diagnostics, predictive maintenance, and energy management to support decarbonization goals.

Siemens’ Building X Tackles AI Challenges in Buildings

Siemens notes the building sector lags behind others in AI adoption due to data management challenges, scaling smart solutions, and building complexity. Their Building X platform addresses these with a unified data model and open, extendable services. The platform provides a single source of truth for building operations, helping facilities better utilize AI-driven applications during the operations phase.

For building and real estate professionals, these AI tools and platforms offer practical ways to reduce operational costs, meet sustainability targets, and enhance occupant comfort. Exploring AI solutions can position properties for future energy standards and improve asset value.