Sensitivity Analysis of Building Comfort and Energy Design Variables

First building comfort, energy and environmental design optimization (BECEDO) research efforts are dated back to the early 70’s 1 , however most in-depth analysis is carried out in non-architectural fields (IT, mathematics and operation technologies). Among building design optimization studies, most attention is focussed on the application of active (mechanical) design variables (HVAC systems), renewable energy harvesting and supply system variables and the combination of active and passive (architectural) design input variables 2 . Regarding the passive design variables, which should be determined for an optimal comfort-energy, as well as environmental impact balance performance, only the numerically easy to beparametrized design variables are taken into consideration, such as opaque and transparent envelope structures and materials, e.g., the thicknesses and thermal properties of the insulation and walls, as well as wall-window ratios (WWR), orientation (ORI), materials, structures (STR) and shading for instance 3 . Though building shape has significant impact on building operation cost 4 , i.e., up to 60-80% energy conservation 56 and up to 80% LCA savings are possible, the investigations dealing with building geometry as a design variable (BGDV) in the BECEDO process is still in its infancy 7 . Estimations 8 predict 60-70% energy consumptions reduction in HVAC and artificial lighting system improvements and up to 20% savings by using intelligent automation systems, however the energy saving potential of optimized space organization and complete building shape design is still missing. Another issue evolves after analysis of the existing BECEDO literature: the stochastic behaviour of the most most frequently applied evolutionary technique (generic algorithms GA) randomly moves in the search space and only near optimum solutions delivers.