Optimization of Residential Spatial Configuration based on Energy Performance, Daylight Brightness, and Thermal Comfort through Pareto Evolutionary Algorithm, Case Study: Mashhad City Climate

All important decisions that affect the thermal performance of the building are made in the early stages of design. Accordingly, in this research, the initial stage of architectural design which is related to space plan was targeted. The aim of this research is the perfect approach to evaluate, and optimize the energy a set of alternative spatial layout solutions through the functional computational design model. The method of this research includes the production of coherent design solutions and the evaluation and optimization of the energy performance of the selected solutions. In the first part, space allocation at a level produces the plan through an evolutionary technique. In the next step, certain plans were evaluated for energy performance, performance rank, and optimization. The energy simulation tool is Honeybee and Ladybug plugins,. The optimization tool is Pareto Evolutionary Algorithm in the Octopus plugin. The reproduction rate, the mutation rate and the possibility of mutation were 0.9, 0.8 and 0.2, respectively. The results showed that each algorithm is a suitable tool for design solutions, thermal performance of floor plans, helping architects’ perspective in the decision-making process, and speeding up the design process. Finally, based on the optimization, the final result of the research algorithm is 70 elite answers in the Pareto front. Only during the Pareto front optimal responses, energy consumption can be reduced by more than 30%; in daylight time and more than 39% improvement was achieved.