New research from Schneider Electric finds that buildings using artificial intelligence (AI) to optimize heating, ventilation and air conditioning (HVAC) can cut energy consumption by as much as 22 percent compared with conventional control systems.
The study, titled “AI for Climate: Quantifying the Energy and Carbon Impact of Building Optimization,” modeled building energy use based on real-world pilot deployments. It examined properties in Australia, India and the United States to assess how AI-enabled HVAC control affects energy consumption, carbon emissions and operating costs.
Key findings
- AI-based optimization can reduce energy use by 7.2–12.7 percent relative to smart building management systems, and up to 22 percent relative to traditional control systems.
- At current energy prices this translates to annual savings between $13,600 and $49,300 per building.
- Up to 60 metric tons of CO₂e can be avoided per building per year (59,869 kg CO₂e), roughly equivalent to the environmental benefit of planting 2,700 mature trees.
- Some building types and use cases can achieve more than 200 MWh of annual energy savings, which could cover the yearly electricity consumption of several dozen average households.
- The avoided carbon emissions from AI optimization can be more than one hundred times greater than the AI system’s own environmental footprint.
- Both cloud-based and on‑premise AI solutions were found to deliver significant energy and carbon savings.
Why this matters
Buildings account for about 37 percent of global energy-related CO₂ emissions. Schneider Electric’s analysis indicates further reductions are possible when an AI layer is added to existing digital building management systems. That layer can link previously siloed data sources, continuously assess building conditions, and automate HVAC optimization in real time.
AI can synthesize data from occupancy patterns, weather forecasts, equipment performance and other operational inputs. This enables more efficient operation and can reduce the workload on facility managers and technicians.
Accessibility for smaller buildings
The research highlights that small and medium-sized buildings—those under 9,300 square meters—can also benefit substantially from AI optimization. Energy management systems have often been seen as too complex or expensive for smaller facilities, and the necessary expertise may be limited. By automating and simplifying optimization, AI makes advanced building management more accessible, helping reduce energy waste, operating costs and emissions.
Földvári Péter, Schneider Electric’s sales leader for commercial and industrial buildings, commented: “The future of building management will be defined by how effectively operators connect and contextualize data that was previously stored in silos. An AI layer built on existing business systems can turn complexity into intelligence and intelligence into action, while reducing emissions and costs and improving performance.”
Methodology in brief
The study combined modeling with experiences from live deployments: AI-based HVAC controls installed across different geographies and usage scenarios were analyzed to estimate energy, cost and emissions reductions. The research evaluated both the magnitude of savings and the flexibility of AI deployments (cloud and on‑premise).
Schneider Electric’s results suggest AI-driven optimization can be a practical tool for building operators seeking to lower energy consumption, operating expenses and greenhouse gas emissions while improving overall building performance.
