This research aims at developing a generalized framework for a fast and accurate determination of building transition strategies to achieve zero carbon compliance and support decision-making at Concordia. The study aims to determine an optimal methodology that can combine a physics-based model and data-driven techniques in order to achieve a lightweight, reusable, accessible, and accurate model. A combination of simulation and machine learning is used to optimize the process and provide fast results for zero-carbon compliance. The output of the study will serve as a breakthrough for the broader adoption of building retrofit strategies for a wide range of assets in the building sector.
Themes: Energy, Resources & Technology, Education & Research