PLATONIC (deeP LeArning neTwOrks for building’s eNergy effICiency) uses cutting edge technologies, such as Deep Reinforcement Learning (DRL), to unlock up to 44% in cost reduction all while preserving user’s comfort.
In line with REACH goals, PLATONIC will establish a high-value data chain, collecting and exploiting data from CERTH’s indoor and outdoor energy consumption and health dataset, as well as weather predictions and Sensinov’s datasets to model building occupancy and propose a set of recommendations that will improve HVAC operation based on:
- External factors, such as weather and building occupancy;
- Component optimum operation points, which represent the actual (mechanical) components found within HVAC systems.
The combination of these two approaches should yield the highest impact on the efficiency & performance of HVAC systems.
Usage of Standards for data interoperability:
SENSINOV has been working on SAREF for Energy and for Buildings to provide a uniform data structure inside our Building Operating System (also SENSINOV has been one of the contributors to SAREF specifications at ETSI). SAREF is also used for cross actor interoperability in the H2020 project where Sensinov is involved.
The other building and energy-related data interoperability standard that is used for smart building interoperability is BRICK which is a uniform metadata schema for buildings. The SENSINOV BoS can generate or consume data according to these data interoperability schema.
For device interoperability, SENSINOV implements the most commonly used data formats and field networks: e.g., Modbus, Zigbee, etc.
Finally, SENSINOV are currently integrating an IDS connector to allow data publishing metadata and sharing data sets according to IDSA (International Data Spaces Association) architecture. This effort is related to planned participation in a GAIA-X project in France.
