WeForming software building blocks and technological enablers (digital platforms, digital twins, BIM/SRI models, AI/ML tools) (1st technology release)

The WeForming project aims to revolutionize building energy management by transforming traditional buildings into intelligent Grid Forming Buildings (iGFBs). Through these iGFBs the project aspires to create energy systems that are not only more efficient but also sustainable, scalable, and grid-interactive. This first technology release sets the foundation for achieving these objectives by focusing on the software building blocks and technological enablers essential for the integration, simulation, and control of iGFBs.

A primary focus of this release is the assessment and adaptation of proprietary digital platforms across various project demonstrations. This evaluation, conducted through a detailed survey, provides crucial insights into the technical characteristics, technologies used, and integration readiness of each platform for incorporation into the W-IBRA (WeForming-Interoperable Building Reference Architecture). The analysis has highlighted several key challenges, including the need for data model harmonization to ensure consistent and seamless data exchange across platforms, and addressing security concerns critical to protecting sensitive operational data. Resolving these issues will require coordinated efforts with WP4 to facilitate secure, seamless integration within the W-IBRA middleware. Additionally, this section introduces the Minimum Viable Product (MVP) 1.0 of the W-IBRA API, which will serve as the primary access point for backend resources, data validation, and system interoperability, laying the groundwork for efficient platform integration across different technologies.

The release also discusses the development of digital models for Digital Twins (DTs) and the enhancement of Building Information Models (BIM) specifically for iGFBs. These models adhere to industry standards, notably the Industry Foundation Classes (IFC), and incorporate Smart Readiness Indicators (SRI) to evaluate and enhance building intelligence and adaptability. A structured framework for developing interoperable data models is introduced to simulate what-if scenarios within DT environments, enabling a data-driven approach to exploring and optimizing building operations and interactions with energy networks. The integration process outlined here involves mapping key technologies, assessing simulation tools, and creating interoperable data models that work harmoniously with the W-IBRA middleware. By enabling the simulation of complex energy scenarios before physical implementation, this approach helps mitigate risks, reduce operational costs, and support continuous optimization.

Furthermore, this release provides a comprehensive overview of the development and deployment of AI/ML tools across the project’s various demos, highlighting the use of advanced forecasting and scheduling techniques to optimize energy management. The AI/ML tools deployed include time series forecasting models, neural networks, and optimization algorithms designed to address several critical areas such as energy demand, resource optimization to name a few. Initial results from this release demonstrate the successful deployment of various AI/ML tools, such as forecasting APIs, PV production forecasting tools, and custom models for load and thermal demand forecasting. These tools are customized to meet the unique needs of each demo, using machine learning frameworks like FastAPI and PyTorch for efficient deployment and operation. In addition, third-party forecasting services have been integrated to provide market price forecasts and weather predictions, adding valuable contextual data to the system for further optimization of energy management strategies.

Overall, this first technology release provides a solid foundation for the WeForming project, delivering the initial set of operational software tools necessary to support the early deployment and testing of iGFBs at pilot sites across Europe. By addressing key challenges related to system integration and data harmonization, this release supports the project’s long-term vision of creating intelligent, flexible, and sustainable energy management solutions for buildings. As this is the initial version of the technology release, further iterations will build upon the developments presented here, expanding and refining these capabilities as the platforms undergo rigorous testing in real-world environments. The success of this release will be measured by the operational readiness of its components, establishing a stable platform for the next phases of the WeForming project.