Abstract
This paper presents the optimization of building envelope design to minimize thermal load and improve thermal comfort for a two-star green building in Wuhan, China. The thermal load of the building before optimization is 36% lower than a typical energy-efficient building of the same size. A total of 19 continuous design variables, including different concrete thicknesses, insulation thicknesses, absorbance of solar radiation for each exterior wall/roof and different window-to-wall ratios for each façade, are considered for optimization. The thermal load and annual discomfort degree hours are selected as the objective functions for optimization. Two prediction models, multi-linear regression (MLR) model and an artificial neural network (ANN) model, are developed to predict the building thermal performance and adopted as fitness functions for a multi-objective genetic algorithm (GA) to find the optimal design solutions. As compared to the original design, the optimal design generated by the MLRGA approach helps to reduce the thermal load and discomfort level by 18.2% and 22.4%, while the reductions are 17.0% and 22.2% respectively, using the ANNGA approach. Finally, four objective functions using cooling load, heating load, summer discomfort degree hours, and winter discomfort degree hours for optimization are conducted, but the results are no better than the two-objective-function optimization approach.
Highlights
The energy consumption in building sector accounts for about one third of the primary energy consumption in the world [1]
Thermal load and thermal comfort of buildings are affected by a number of factors, among which thermal mass, insulation level, absorptance of solar radiation of the exterior walls/roof, and glazing ratio are four factors that have important impacts [6]: (1) thermal mass can affect the fluctuation of the daily temperature inside the house; (2) insulation can affect the conduction heat gain/loss through the opaque envelope; and (3) the absorptance of solar radiation of the opaque envelope and the location and size of the windows can affect the solar heat gain
Since the prediction results from all models are in good agreement with simulation results, the artificial neural network (ANN) models and multi-linear regression (MLR) models are coupled with a multi-objective genetic algorithm to find the optimal design solutions
Summary
The energy consumption in building sector accounts for about one third of the primary energy consumption in the world [1]. The other way is to use an energy prediction model to characterize building behavior and combine this with genetic algorithm, where most of the time was used to generate the sample data, e.g., it took three weeks to generate the results of thermal load and comfort level of 450 cases for two residential houses in Canada, and it took around 7 min to complete the optimization process which may take 10 years when using simulation software coupled with a genetic algorithm directly [20]. Optimal usage of material for different building components can be selected to achieve a minimum thermal load and discomfort degree hours, which is different from traditional design, and can improve the quality of construction project. This is coincidence with 3D printing technology where the material for each component can be tailored. It is expected that advancement of 3D house printing technology will make it possible for wide application in design practice in the future
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
