The construction industry is one of the world’s largest carbon emitters, accounting for around 40% of total emissions. Therefore, reducing carbon emissions from the construction sector is critical to global climate change mitigation. However, traditional architectural design methods have some limitations, such as difficulty in considering complex interaction relationships and a large amount of architectural data, so machine learning can assist architectural design in improving design efficiency and reducing carbon emissions.
This study aims to reduce carbon emissions in the architectural design by using a Transformer with a cross-attention mechanism model. We aim to use machine learning methods to generate optimized building designs that reduce carbon emissions during their use and construction. We train the model on the building design dataset and its associated carbon emissions dataset and use a cross-attention mechanism to let the model focus on different aspects of the building design to achieve the desired outcome. We also use predictive modelling to predict energy consumption and carbon emissions to help architects make more sustainable decisions.
Experimental results demonstrate that our model can generate optimized building designs to reduce carbon emissions during their use and construction. Our model can also predict a building’s energy consumption and carbon emissions, helping architects make more sustainable decisions. Using Transformers with cross-attention mechanism models to reduce carbon emissions in the building design process can contribute to climate change mitigation. This approach could help architects better account for carbon emissions and energy consumption and produce more sustainable building designs. In addition, the method can also guide future building design and decision-making by predicting building energy consumption and carbon emissions.