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G. Pilati:
"The impact of model input assumptions regarding comfort, occupancy, zoning, and climate on simulated energy use of an office building";
Supervisor: A. Gasparella, A. Mahdavi, G. Pernigotto, F. Tahmasebi; Faculty of Science and Technology - Department of Civil, Environmental, and Mechanical Engineering (Bolzano/Trento), 2018; final examination: 2018-07-19.

Computational modeling of buildings´ energy performance can provide building designers and operators with relevant and useful information toward optimization of design quality and operational performance. However, the reliability of simulation-supported building performance assessment depends on the reliability and consistency of model input assumptions. In this context, the present thesis work focuses on four related topics. The first regards assumptions that influence desirable indoor environmental conditions in buildings on simulation results such as annual and peak heating and cooling loads. Such assumptions are either made based on information in pertinent standards and guidelines or are based on information pertaining to the specific building design or operation task. Accordingly, different ranges of acceptable or desirable indoor conditions may be assumed for variables such as indoor temperature, relative humidity, or air change rates. The second issue is connected to the influence of zoningrelated
model input settings for performance simulation results, in
terms of energy consumption for a detailed dynamic simulation of a
building´s thermal performance. As regards, the present contribution addresses the potential ramifications of thermal zoning resolution. As there is not a unique and universally applicable scheme for the definition of the number, size, and shape of thermal zones in a building simulation model, related decisions could be based on different viewpoints
and could influence the predicted values of performance indicators. As with comfort settings and thermal zone definitions, different input assumptions regarding occupancy (i.e., temporal and spatial distribution of inhabitants in buildings) could lead to different performance simulation results. Related choices significantly affect the level of effort required for the model input generation. Thus, it is necessary to gauge the critical necessity of highly detailed models in the context of their consequences for the value ranges of building performance indicators with respect to base defined models in which the speed of implementation and simulation are the key factors. Moreover, consequences related to the location of simulation building
within different climate zone should be considered.

(no german version)
Computational modeling of buildings´ energy performance can provide building designers and operators with relevant and useful information toward optimization of design quality and operational performance. However, the reliability of simulation-supported building performance assessment depends on the reliability and consistency of model input assumptions. In this context, the present thesis work focuses on four related topics. The first regards assumptions that influence desirable indoor environmental conditions in buildings on simulation results such as annual and peak heating and cooling loads. Such assumptions are either made based on information in pertinent standards and guidelines or are based on information pertaining to the specific building design or operation task. Accordingly, different ranges of acceptable or desirable indoor conditions may be assumed for variables such as indoor temperature, relative humidity, or air change rates. The second issue is connected to the influence of zoningrelated
model input settings for performance simulation results, in
terms of energy consumption for a detailed dynamic simulation of a
building´s thermal performance. As regards, the present contribution addresses the potential ramifications of thermal zoning resolution. As there is not a unique and universally applicable scheme for the definition of the number, size, and shape of thermal zones in a building simulation model, related decisions could be based on different viewpoints
and could influence the predicted values of performance indicators. As with comfort settings and thermal zone definitions, different input assumptions regarding occupancy (i.e., temporal and spatial distribution of inhabitants in buildings) could lead to different performance simulation results. Related choices significantly affect the level of effort required for the model input generation. Thus, it is necessary to gauge the critical necessity of highly detailed models in the context of their consequences for the value ranges of building performance indicators with respect to base defined models in which the speed of implementation and simulation are the key factors. Moreover, consequences related to the location of simulation building
within different climate zone should be considered.