AI and Machine Learning: Enhancing Efficiency with VEXO S-BMS

Why AI Matters in Building Efficiency and Decarbonisation

Buildings are responsible for nearly 40% of global energy consumption and a substantial share of the UK’s greenhouse gas emissions. Reducing this footprint is a priority – and machine learning offers a high-impact solution. According to a study presented at the 2025 CIBSE Technical Symposium by Arash Izadpanah, machine learning enhanced systems have delivered energy savings of up to 75% through smarter renewable integration and adaptive building controls.

Key applications include:

Real-World Impact: Machine Learning in Action

One compelling example comes from Chakron Dechkrut’s research on classifying heat pump usage across 185 UK homes using self-organising maps, an unsupervised machine learning technique. His study revealed distinct demand shapes for heat pump usage such as morning and evening peaks and used time-series clustering to identify inefficiencies and behavioural patterns. This insight supports better control strategies, improving both comfort and system performance.

Another standout application is from Peter Ehvert and the Foster + Partners team, who applied polynomial regression to simulate outdoor comfort via Physiological Equivalent Temperature. Their model reduced simulation time from five hours to 41 seconds, without compromising accuracy. This kind of acceleration enables faster design iteration and real-time environmental feedback in platforms like Revit and Unreal Engine.

Implementation Challenges and How They’re Being Overcome

Despite its promise, the adoption of ML in the built environment faces hurdles:

Data standardisation and quality

Model interpretability

Integration with legacy systems

Efforts to address these include developing explainable AI approaches, creating unified frameworks for data collection, and advancing sensor and IoT technologies that ensure consistent, high-quality input.

The Future Is Connected: Enter VEXO’s S-BMS

One of the most exciting frontiers for AI-driven efficiency is the integration of IoT through systems like VEXO’s Smart Building Management System (S-BMS). By connecting sensors, equipment, and analytics platforms, S-BMS enables:

Granular real-time energy monitoring

Predictive maintenance

Intelligent control of HVAC and heating systems

Seamless integration with renewable sources

By leveraging VEXO’s S-BMS Open API and rich IoT device data, building managers can seamlessly integrate with third-party software that uses AI and machine learning—enabling deeper optimisation, enhanced analytics, and more intelligent energy management across platforms.