Schneider Electric, a global leader in energy management and automation with operations in over 100 countries, has long been at the forefront of the movement to create more sustainable and efficient buildings. With buildings accounting for approximately 40% of global energy consumption and a significant portion of carbon emissions, the company has recognized that the key to a more sustainable future lies in making buildings smarter, more responsive, and more efficient. In the mid-2020s, with the increasing urgency of the climate crisis, tightening regulations, and the growing demand for sustainable solutions from both governments and corporations, Schneider Electric found itself in a unique position to lead the charge in creating a new generation of intelligent buildings. The company’s own research forecasts that AI could account for as much as 50% of the growth in US electricity demand between 2025 and 2030, making the need for energy-efficient AI solutions even more critical [3].
The Challenge: The Balancing Act of Comfort, Efficiency, and Cost
Schneider Electric’s primary challenge was to find a way to balance the competing demands of occupant comfort, energy efficiency, and cost-effectiveness. Traditional building management systems were often unable to achieve this balance, as they were based on static schedules and simple rule-based logic. An HVAC system might be set to cool a room to 72 degrees Fahrenheit from 8 AM to 6 PM, regardless of whether the room was occupied, what the outside temperature was, or how the building’s thermal mass was behaving. This often resulted in buildings that were either over-cooled or over-heated, leading to wasted energy and uncomfortable occupants.
Furthermore, traditional cloud-based AI solutions for buildings introduced their own set of challenges. They required a constant and reliable internet connection, which was not always available. They introduced latency, meaning that the system could not respond to changes in real time. They raised data privacy concerns, as sensitive occupant data had to be transmitted to and stored in the cloud. And they incurred ongoing subscription costs, which could be a barrier to adoption for smaller buildings. Schneider Electric needed to develop a more intelligent, responsive, and self-contained approach to building management.
Technology Implementation Workflow: The Power of the Edge
To address these challenges, Schneider Electric developed a groundbreaking on-device AI room controller that uses edge-based AI to learn and adapt to the thermal behavior of a building in real time. The implementation workflow can be broken down into several key stages.
The first stage was the development of Edge-Based AI. The core innovation of the solution is its use of edge-based AI, which means that the AI runs directly on the device, rather than in the cloud. This is a fundamental departure from traditional cloud-based AI solutions. By processing data locally, the controller can respond to changes in real time, without the latency associated with cloud-based solutions. It also eliminates the need for a constant internet connection and addresses data privacy concerns, as no occupant data ever leaves the device [1].
The second stage was the implementation of Real-Time Learning and Adaptation. The AI continuously learns and adapts to the unique thermal behavior of a building in real time. It takes into account a variety of factors, including occupancy patterns, weather conditions, the time of day, and the building’s thermal mass, to optimize the operation of the HVAC system. The AI uses this data to build a dynamic model of the building’s thermal behavior, which it uses to predict future conditions and to proactively adjust settings to maintain comfort while minimizing energy use [1].
The third stage was ensuring ASHRAE 55-Compliant Comfort. The AI is designed to maintain a comfortable environment for occupants, in accordance with ASHRAE 55, the leading standard for thermal comfort in buildings. The controller intelligently and dynamically adjusts room temperatures to ensure that occupants are comfortable, even as conditions change throughout the day. In a large-scale quantitative analysis at active building sites, the AI-enabled systems successfully maintained temperature regulation and occupant comfort compliance over 85% of the time, compared to room controllers without AI [1].
The fourth stage was the design for Scalability and Cost-Effectiveness. The on-device AI room controller is designed to be easy to install and maintain. Because the AI runs on the device itself, there is no need for additional central processing capacity or recurring cloud subscription fees. Systems can expand simply by adding more devices, making it a more scalable and cost-effective solution for a wider range of buildings, from commercial offices and hotels to schools and other settings with frequent occupancy shifts [1].
Tools/Platforms/Solutions Used
| Tool/Platform | Description |
| SpaceLogic Touchscreen Room Controller | The physical device that houses the on-device AI, featuring a touchscreen interface for occupant interaction [1]. |
| AI Eco Mode | The on-device AI algorithm that optimizes HVAC operation for energy savings and comfort. |
| Edge-Based AI | The AI runs directly on the device, providing real-time responsiveness, enhanced security, and no cloud dependency. |
| ASHRAE 55 Compliance | The standard for thermal comfort that the AI is designed to meet. |
| EcoStruxure Platform | Schneider Electric’s broader IoT platform for buildings, data centers, and infrastructure, which provides the overarching architecture [2]. |
ROI Achieved
The return on investment from Schneider Electric’s on-device AI room controller has been significant, earning industry recognition and delivering measurable results.
| Metric | Result |
| Energy Savings | 5-15% daily HVAC energy savings, with potential for up to 15% in certain scenarios [1] |
| Comfort Compliance | Temperature regulation maintained over 85% of the time [1] |
| Time to Value | Energy savings achieved in just two weeks of deployment [1] |
| Building Energy Cost Reduction | Up to 40% in broader AI-powered building management scenarios [4] |
| Recognition | Business Intelligence Group’s 2025 AI Excellence Award in the Energy Management AI product category [1] |
The success of Schneider Electric’s on-device AI room controller is a testament to the power of edge-based AI to create more sustainable and efficient buildings. By bringing intelligence directly to the device level, Schneider Electric has created a solution that is more responsive, more secure, and more cost-effective than traditional cloud-based approaches, helping to pave the way for a greener and more comfortable future.
References
- 1] [Schneider Electric Blog: Edge AI Redefines Room Control (https://blog.se.com/buildings/2025/05/23/real-time-comfort-and-efficiency-how-edge-ai-is-redefining-room-control/)
- 2] [Facilities Dive: Schneider Electric debuts unified AI-powered platform for buildings (https://www.facilitiesdive.com/news/schneider-electric-debuts-unified-ai-powered-platform-for-buildings/806535/)
- 3] [Schneider Electric: AI’s impact on energy consumption (https://www.se.com/ww/en/about-us/newsroom/news/press-releases/schneider-electric%E2%80%99s-new-research-forecasts-ai%E2%80%99s-impact-on-energy-consumption-6750713c2f744153950a4943)
- 4] [Chief AI Officer: How Schneider Electric’s AI Cuts Building Energy Costs by 40% (https://chiefaiofficer.com/blog/blog/how-schneider-electrics-ai-cuts-building-energy-costs-by-40/)
