For example, choosing to fully glazed tower based on aesthetic and daylight performance criteria could lock in high carbon emissions from heating and cooling demand. But with DAISY, we can now assess these choices in parallel with energy demands and net zero requirements, finding a balanced and informed trade-off between objectives. As we develop the tool further we will start to bring in cost performance. DAISY’s performance-driven workflow means we can identify optimum concept designs based on key performance objectives including social and environmental factors: carbon, thermal comfort, daylight, energy efficiency, as well as quality, risk and programme time.
Difficult design questions, such as the interaction and trade-off between architectural performance objectives and engineering performance objectives can be formulated, practically assessed, and answered quantitatively. How do the morphological properties of a building affect long-term operational carbon and the up-front embodied carbon? What are the carbon trade-offs in choosing a better performing façade system versus changing the glazing ratio and using solar shading? How do both interact with daylight and thermal comfort? DAISY enables us to answer these questions.
How does DAISY do it?
DAISY uses a combination of a performance-driven computational workflow, genetic algorithms for multi-objective optimisation, and machine learning for real-time interactions.