Conference Year



Sensitivity analysis, optimizazion design, energy simulation, building refurbishment


Statistics and, in particular, sensitivity analysis represent an important tool for the building designer to find information about which parameters have the largest potential in reducing the energy consumption or which are the most crucial to focus on in the different phases of the project process. The application of dynamic simulation to a large number of configurations (i.e. the extensive simulation) to find the optimal solution either considering only the energy aspects or also the economic impacts and not only to evaluate a small set of possible alternatives, represents in that perspective a new approach to the building design, in particular for refurbishment considerations. This work aims to evaluate and generalize this kind of approach with its application to the refurbishment of residential buildings. The considered design solutions represent the most common measures of improvement of the performance of opaque and transparent envelope, such as the insulation level of walls and windows and the solar properties of the glazings. Different starting cases characterized by different envelope thermal inertia, corresponding to three massive materials (timber, clay and concrete), and different geometrical features were studied. A large number of environmental conditions, envelope characteristics and refurbishment interventions have been analyzed within a factorial simulation plan. Among those parameters, the ratios between the dispersing envelope and the volume conditioned (considering 3 different floors of a building – the top, the intermediate and the ground floor), the windows size (small or large size) and their distribution (South, East or West oriented), the level of insulation of the opaque envelope (starting cases without insulation, poor insulated and high insulated cases) and the kind of glazings (starting cases with single glasses and improved cases with double or triple glasses with high or low Solar Heat Gain Coefficient, SHGC) have been examined. Three Italian climates (Milan in the North, Rome in the center region and Messina in the South) were considered as representative of the Southern Europe. The inferential statistical analysis has been used to identify the predominant factors in each refurbishment solution and the economical savings both in heating and in cooling.