BOCEMON

With the project, BOCEMON responds to urgent global challenges. European climate targets necessitate rapid decarbonization, especially in buildings, which account for a significant portion of energy use and emissions. At the same time, the COVID-19 pandemic has brought new attention to indoor environmental quality and has increased the demand for flexible office spaces, as employees now often alternate between remote and on-site work.

The result is a need for buildings to adapt dynamically to varying occupancy patterns and minimize unnecessary energy consumption, particularly for heating, ventilation, and air conditioning (HVAC) systems.

Vision and approach

BOCEMON’s core innovation involves creating a semantic data model for buildings, unifying diverse data (sources), e.g. sensors and building composition, and makes the relationships explicit. An automatic onboarding service will improve the discovery and mapping of sensors and actuators in a particular building onto this data model. The model will include the capability for advanced reasoning, allowing to dynamically adjust building services in real-time based on factors such as occupancy levels, indoor environmental quality, and energy availability.

Hybrid AI techniques will play a crucial role in transferring insights across different building contexts. Based on a smart analysis of data from various buildings, BOCEMON’s models will quickly adapt to new buildings, for applications such as investment decision support and occupant-centric HVAC control. This reduces the need for extensive data collection and training for each separate building.

BOCEMON’s innovative visualization tools will dynamically map sensor data onto floor plans, allowing facility managers to monitor building performance intuitively without needing specialized technical knowledge. In combination with easy-to-use service design tools this will reduce the complexity and time involved in managing building systems, especially for non-experts.

The BOCEMON project results will be demonstrated and validated in selected office buildings that will serve as real-world testbeds for the various innovations developed within the project.

Project goals and innovation

BOCEMON sets out six primary innovation goals:

1. Service tooling for semantic data models: The project aims to develop tools to support the use of semantic data models for building management, reducing the design time of new services.
2. Automated sensor and actuator mapping: The project will automate the discovery and configuration of building sensors, reducing time and effort to map out these devices
3. Hybrid AI for transfer learning: Hybrid AI tools will facilitate knowledge transfer across buildings, improving the efficiency in predicting thermal characteristics, occupancy patterns, and overall building health, saving time when onboarding new buildings.
4. Dynamic visualization: BOCEMON will develop methodologies to visualize sensor data on floor plans dynamically, eliminating the need for hard-coded mappings.
5. Occupant-centric HVAC control: Development of dynamic, self-adapting HVAC control systems based on occupant behavior, targeting a reduction in energy consumption and an improvement in comfort.
6. Investment decision support: BOCEMON will create decision-support algorithms that recommend sustainable building upgrades based on actual usage, helping to improve energy efficiency and comfort.

“By contributing to both the economic and environmental sustainability of buildings, BOCEMON supports the transition to a climate-neutral future.”