A Data-Driven Approach to Achieve Low-Carbon Building Energy Optimization by Using BIM Technology

Low-carbon building energy optimization is the environmental aspect that is impacted due to the construction sector. The integration of Building Information Modelling (BIM) with Artificial Intelligence (AI) techniques offers the design and operation of buildings with a reduced carbon footprint. Usually, such systems lack flexibility and the precision of dynamically optimizing energy usage. This work proposes a novel data-driven framework that merges AI and BIM to optimize the building energy system for low-emission design using Carbon Major Emission Datasets. It aids materials and energy source selections by identifying highly emitting commodities to reduce operational carbon footprints. Initially, data acquisition and emission analysis are performed on the Carbon Majors database to identify high-emission materials. Subsequently, emission factors are linked with the BIM elements using plug-ins such as One Click LCA, which allow the annotation of embodied carbon values. Further, operational energy is optimized by Multi-Agent Assisted NSGA-II, which optimizes parameters and material selection. Additionally, AI-assisted energy prediction supported by the SqueezeNet model and energy simulation techniques was used for minimizing building energy consumption. The results reveal a high-energy prediction accuracy of 0.0212 for MAE, 0.0376 for MSE, and 0.9814 for the R² score. It further helps to reduce carbon emissions by 1155 tons and improve the cost efficiency of 570.25 million, promoting low-carbon building solutions from the earliest stages of design.

@article{Yu2025,
  title     = {A Data-Driven Approach to Achieve Low-Carbon Building Energy Optimization by Using BIM Technology},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {8},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Xin Yu and Guoliang Ren and Jie Niu},
  doi       = {10.14569/IJACSA.2025.0160870},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160870}
}