A Multi-Criteria Decision-Support Workflow for Early-Stage Neighborhood Design based on Predicted Solar Performance

Despite recent developments, the need for adequate guidance and support in the early decision-making process of urban planners, designers, and architects has recurrently been recognized. Traditional performance assessment methods, which are often based on partial and independent dynamic simulations evaluating individual metrics, are better suited for detailed design and are particularly complex and time-consuming at the urban scale. They typically follow a linear design-and-test approach, limiting the user-guidance features. Taking a different approach, this paper proposes a multi-criteria decision-support workflow that evaluates the daylight and passive and active solar potential of early-stage neighborhood designs. The performance evaluation is done through a predictive mathematical function for the passive solar and daylight potential, requiring little information from the user. The implemented workflow is introduced, and the development of the underlying performance assessment engine is summarized, along with the results from a proof-of-concept study to probe the validity boundaries of the predictive functions. Results show the proposed workflow to be promising as an interactive and real-time performance-based design support.