LAUSR

This research presents a synthetic case study for multi-objective optimization for an active and passive design procedure based on dynamic programming using genetic algorithms (GAs) and computational fluid dynamics (CFD). Both active and passive optimized variables are indispensable for efficient building design. This paper shows how to deal with these two different types of variables in the multi-objective optimization frame. Energy saving, thermal comfort, and indoor air quality are selected as objective functions. While demonstrating a synthetic multi-objective optimization with active and passive variables, this research analyzes the trade-off relation among objective functions in the indoor environment. In this research, representing fluctuating outdoor conditions as random variables, optimization of the building geometry as the passive design variable and an HVAC system as the active design variable was performed using the dynamic programming approach. This research consists of several tasks. First, multi-objective optimization is carried out by genetic algorithms and computational fluid dynamics, and then dynamic programming is applied to the control system with random variables.