Optimization design of layout dimension for residential buildings weighing up daylighting, thermal comfort, and indoor air quality with a low-carbon decision-making

In response to the growing demand for living environment, enhancing the physical environment of residential buildings has become an imperative priority. This study proposes an optimization and low-carbon decision-making framework. Interestingly, a multi-objective optimization model was developed by integrating a backpropagation neural network with the NSGA-II algorithm, and a carbon emission model was incorporated into the decision-making process to obtain optimal design parameters. To substantiate the applicability of this methodology, it will be applied to a prototypical model of residential buildings, which consists of multiple physical environmental units. Five types of design variables were identified, including layout dimension, window-to-wall ratio, building orientation, building envelope, and openable window area ratio. And the correlation analysis was conducted on three optimization objectives of useful daylight illuminance, percentage of predicted dissatisfaction and indoor CO2 concentration. The results indicate that all design variables have been retained as key design variables, and the three objectives meet the necessity of multi-objective optimization. Moreover, the constructed neural network prediction model has high accuracy. Compared with the prototypical model, layout dimensions of the optimal solution exhibited the greatest changes in the width and depth of kitchen, with respective increases by 25.38 % and 21.46 %. The unit modules of the residential buildings with the worst performance have been effectively optimized, the useful daylight illuminance has increased by 2.23 %, the percentage of predicted dissatisfaction has decreased by 11.5 %, the indoor CO2 concentration has decreased by 48 %, and the operational carbon emissions per unit area has decreased by 14 %.