Passive designs and strategies for low-cost housing using simulation-based optimization and different thermal comfort criteria

An optimum design of low-cost housing offers low-income urban inhabitants great opportunities to obtain a shelter at an affordable price and acceptable indoor thermal conditions. In this paper, the design and operation of a low-cost dwelling were numerically optimized using a simulation-based approach. Three multi-objective cost functions including construction cost, thermal comfort performance and 50-year operating cost were applied for naturally ventilated and air-conditioned buildings. Thermal environment inside the house was controlled and assessed by two thermal comfort models. Optimization problems which consist of 18 design parameters and 6 ventilation strategies were examined by two population-based probabilistic optimization algorithms (particle swarm optimization and hybrid algorithm). Optimum designs corresponding to each objective function, differences in optimal solutions, energy saving by the adaptive comfort approach and optimization effectiveness were outlined. The optimization method used in this paper shows a considerable potential of comfort improvement, energy saving and operating cost reduction.