The concept design stage provides a vital opportunity to cut a building’s carbon, but with most design tools tailored to detailed design, doing so requires new computational tools that can help designers amplify their intuition and discover the unknown. This is where DAISY comes in. DAISY (Digital, Artificial Intelligence and Sustainability) is our computational, parametric design tool. It revolutionises decision-making and helps you choose the optimum, high-performing solutions in conceptual design.

In the traditional linear approach to building design, engineering teams often come on board later in the design process, after the architectural form has been fixed. This misses the opportunity that the earliest stages of the process provide to explore design options and ramifications to their fullest potential, since previous decisions have not yet limited what can be done. It also misses the opportunity to design the building as a synergistic system, with simultaneous analysis from each discipline fed back into the design so that interdependent performance characteristics can inform design decisions.

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.

Through its workflow, algorithms and machine learning, DAISY amplifies intuition through real-time interaction. At the same time, it enables us to discover the unknown so we can unlock the design potential and drastically improve carbon performance by: 

1. Optimising the massing and orientation of the building to cut operational and embodied carbon within the site contexts and constraints.
2. Optimising the glazing ratio and shading to balance carbon and daylight objectives.
3. Understanding the importance of different design variables in driving performance – highlighting the multiple ways to arrive at the desired performance.

With DAISY opening our eyes to a building’s true potential at the concept stage, and the costs of different approaches, we and our clients can make the most informed decisions to cut carbon.