During RP1, the project vision, standards, and user requirements were defined. The reference architecture of DATAWiSE was successfully designed, defining and structuring all system components. Data connectors and ETL pipelines were developed to integrate heterogeneous data sources from the pilots and ensure data harmonization. The first version of the Data Sharing Platform (DSP) was implemented and is currently operational, enabling secure and structured data exchange, dataset storage, cataloguing, and metadata management through a unified user interface.
Data analytics backend services were developed utilising AI techniques, with functionalities like data collection, storage and processing, The data storage component was developed and successfully deployed utilising open-source technologies, enabling storage and management of static and dynamic data as well as the communication with the DSP and the data bus. An intermediate data fusion component was developed serving as a processing pipeline for DATAWiSE services, providing homogenised data from raw sensor streams. The first version of the fusion tool is currently operational and able to handle processing requests from energy forecasting services. Also, the methodology for the Explainable AI framework was developed along with a model registry used by AI-based services to manage and control trained models.
The DBPM toolkit includes now a functional user interface with role-based access control. The BIM-Integrated Digital Twin was deployed as a hybrid system combining a node-based data processor with a web-based 3D viewer synchronizing building models and live sensor data. Electrical and thermal flexibility and forecasting services reached an operational level, with models trained and validated on pilot data. Initial versions of the Comfort Balancing and Climate Resilience services were also integrated.
Moreover, the LD2S toolkit, now features a functional user interface and secure access control, with the following components integrated . A key achievement was the delivery of the Circular Renovation Tool, which features a specialized lifecycle assessment logic to calculate circularity indicators and aggregate them into a Global Circularity Score for building materials. In the domain of risk assessment, the Predictive Maintenance service reached an operational state following the successful deployment of AI-based algorithms designed to determine optimal maintenance thresholds and automate the optimization of repair schedules. Furthermore, the Sustainability reporting and Smart Readiness Assessment (SRI) modules reached a functional level, with the sustainability module now employing a hybrid scoring system that combines automated data with qualitative assessments, and with the SRI tool being updated to enable baseline technological evaluations across all pilot sites.
