Multi-objective optimization of building design for life cycle cost and CO
passive building, artificial neural network, life cycle CO2 emissions, natural ventilation, multi-criteria optimization
Currently, building construction and operation are responsible for 36% of global final energy usage and nearly 40% of energy-related carbon dioxide (CO2) emissions. From the sustainable development perspective, it is crucial to consider the impact of construction material on the achievement of life cycle benefits. This study proposed a simulation-based multi-objective optimization method to minimize both life cycle cost and CO2 emissions of buildings. We built an energy simulation model with hybrid ventilation and light-dimming control in EnergyPlus based on an operational passive residential building in a severe cold climate. Next, this investigation selected insulation thickness, window type, window-to-wall ratio, overhang depth and building orientation as design variables. The study ran parametric simulations to establish a database and then used artificial neural network models to correlate the design variables and the objective functions. Finally, we used the multi-objective optimization algorithm NSGA-II to search for the optimal design solutions. The results showed potential reductions of 10.9%–18.9% in life cycle cost and 13.5%–22.4% in life cycle CO2 emissions compared with the initial design. The results indicated that the optimization approach in this study would improve building performance. The optimal values of the design variables obtained in this study can guide designers in meeting economic and environmental targets in passive buildings.
Xue, Qingwen; Wang, Zhaojun; and Chen, Qingyan
"Multi-objective optimization of building design for life cycle cost and CO,"
Building Simulation: An International Journal: Vol. 15:
1, Article 7.
Available at: https://dc.tsinghuajournals.com/building-simulation/vol15/iss1/7