A novel decision support system for designing fixed shading systems in the early design stage: A case study in Egypt

Abstract

Solar shading is a crucial element that can significantly impact lighting conditions and energy consumption, especially in hot regions. However, selecting the best shading design that achieves optimal daylight performance while meeting architects' requirements is very challenging. This is because it involves considering many design parameters and balancing multiple objectives. The paper introduces a decision support system for designing fixed shading systems to enhance interior environments and reduce energy consumption. Parametric simulation, a meta-model based on automatic selection of regression machine learning algorithms (RMLA), and non-dominated sorting genetic algorithm II (NSGA-II) are used to prepare the designs for selection. Three multiple criteria decision-making (MCDM) methods are employed: the analytic hierarchy process (AHP), entropy, and the technique for order preference by similarity to the ideal solution (TOPSIS) in two combinations (AHP-TOPSIS and entropy-TOPSIS) to adapt to different expert availability scenarios. The proposed method is coded and automated with an easy-to-use interactive interface for flexibility and easy application. The method is applied to help design a fixed shading system for hot climate regions in Cairo, Egypt, which involves considering five objectives: useful day light illuminance (UDI), energy use intensity (EUI), solar gains (SG), daylight autonomy (DA), and continuous daylight autonomy (CDA). The proposed method can provide an efficient design that significantly improves daylight performance. Different dataset sizes are used for training the meta model to study their effect on the final selected design. A sensitivity analysis is done to explore the impact of changes in experts’ opinions on the decision-making process.