Energy Performance of Buildings: Modeling of Dynamic Summer Behavior

In Europe about one third of total annual energy consumption is used in both residential and commercial buildings. In many countries already a building regulation exists to ensure the reduction of energy needs for DHW and space heating. Hence, the interest in reducing summer energy demand has grown in the last few years. The summer behavior of buildings is mostly non-stationary and, therefore, the reliability of simple quasi steady state model predictions can not be taken for granted. Since detailed hourly energy simulations emulate the dynamic interaction between environment, building structure, occupants and indoor conditions, they have the potential to provide relevant information about the building summer behavior and to indicate the possible conservation measures for the reduction of energy consumptions. However, one of the limits for the application of enhanced simulation methods, that sometimes can undermine the reliability of their results, is the difficulty to gather reliable input data. Moreover, if dynamic simulation are used in order to compare different choices, decisions are often suboptimal because of the insufficient knowledge of data that has a large consequence on results. Consequently, in order to broaden the use of building simulation in the design process, it is essentially to clarify some aspects. For instance, one of the biggest objection versus the use of detailed procedure is: "to what extent these methods are meaningful if input data are not reliable?" For this reason, the emphasis of this thesis is on the uncertainties of model predictions. In particular, the research is divided in two parts: the investigation of climate issues and the uncertainty analysis of heat transfer estimation, especially for massive wall. The purpose of the research is to support AE in the choice of the characteristics to which the model predictions are more sensitive. In fact, the results of sensitivity and uncertainty analyzes allow to know the robustness of simulation models and make AE aware if the wrong specifications can lead to uncertain results.