Sie befinden Sich nicht im Netzwerk der Universität Paderborn. Der Zugriff auf elektronische Ressourcen ist gegebenenfalls nur via VPN oder Shibboleth (DFN-AAI) möglich.
mehr Informationen...
Multi-objective optimization (MOO) for high-rise residential buildings’ layout centered on daylight, visual, and outdoor thermal metrics in China
Ist Teil von
Building and environment, 2021-11, Vol.205, p.108263, Article 108263
Ort / Verlag
Oxford: Elsevier Ltd
Erscheinungsjahr
2021
Link zum Volltext
Quelle
Elsevier ScienceDirect Journals Complete
Beschreibungen/Notizen
Nowadays building performance optimization is extended to urban planning Multi-Objective Optimization (MOO). Most research focuses on the optimization of energy use and daylight performance of building design. Buildings optimized for performance metrics rarely consider different performances together. Without integrating different building performance areas, the solution found from optimization will not be a balanced or trade-off one. This paper proposes a method to extend the use of optimization to cover multi-discipline areas that optimize visual comfort and outdoor thermal performances on the layout of high-rise residential buildings.
Daylight, sunlight hours, the sky view, and outdoor thermal comfort were the performance objectives. A parametric building model was built to control the buildings’ layout and simulation tools were used to find the performance of objectives. To accelerate the simulation process, an Artificial Neural Network (ANN) was applied to the building simulation models to calculate the performance results rapidly.
ANN model had an average accuracy of 89.9% across all outcomes. The MOO method was conducted to find integrated solutions to the building layouts on site. By ranking the optimized solutions based on five combined performance targets, the top 10 out of 150 building layout options were identified, indicating an almost 21% better performance than the baseline case.
Moreover, the top 30 out of 150 optimum cases performed better than the baseline. The study demonstrates that the proposed MOO method that combines visual comfort and outdoor thermal measurements can improve and contribute to a sustainable building layout design.
•Evaluate indoor and outdoor built environment performances on the layout of high-rise residential buildings.•Artificial Neural Network (ANN) was applied into the building simulation models for reducing running multi simulation.•Parametric building model was established to show how the design layout can be altered to improve performance.•Multi-Objective Optimization (MOO) was conducted to find solutions of layout of high-rise residential buildings.•The optimized solutions shows that the top 10 building layouts were almost 21% better performance than the baseline case.