Â鶹ÉçÇø

Project Description

The trend in the majority of existing buildings renovation projects is that retrofits are carried out when needed on a building-by-building basis, without systematic assessment, and employing static tools with very large assumptions and generic inputs. This has resulted in an annual energy renovation rate as low as 1% of the building stock. Thus, new approaches are needed, and innovative up-to-date tools must be introduced to scale up efforts and aid optimized and accelerated energy retrofitting of the Danish existing building stock. The project will design and develop the first-of-its kind Digital Twin solution (DanRETwin) for optimal energy retrofit decision-making support, retro-commissioning, and data-driven performance optimization of non-residential existing buildings. The proposed solution is driven by building operational data, employing machine learning and artificial intelligence methods to develop scalable data-driven building energy models, along with data collection means using Clamp-on IoT sensors to serve as a basis for a full automated and flexible solution.

The project is carried out as a collaborative project between SDU Center for Energy Informatics at University of Southern Denmark, ReMoni A/S, NRGi A/S and Aarhus Municipality. The proposed Digital Twin solution will first be tested Â鶹ÉçÇø living lab OU44 building, and then will be demonstrated and evaluated in two case studies from Aarhus Municipality portfolio of buildings, a public school, and an office building.

Project Summary