It is a challenge to design buildings that are both energy efficient and healthy. The factors that contribute to energy efficient and healthy buildings are related to both indoor and outdoor conditions. For example, the airflow around naturally ventilated buildings affects occupant comfort. Wind speed may also have an effect on the cooling load of air-conditioned buildings. Wind modeling is complex and predictions are often inaccurate due to unknown combinations of values of variables that influence climatic conditions. The aim of this work is to increase the accuracy of wind predictions using measurements through a multi-model system identification approach. The success of this approach depends on the location and number of sensors. Therefore, an important initial step is the development of a rational and systematic measurement-system design methodology. The following are stages in the proposed methodology: first a sensitivity analysis is performed to determine the impact that input variables have on the output. Feature selection algorithms are employed to select the combination of variables with the highest impact on predictions. The selected variables are then varied and a discrete population of possible models is generated. The measurement-system design is based on the predictions of these models using computational fluid dynamics. An experimental facility of the Future Cities Laboratory, called “BubbleZERO”, is used to validate the methodology. Optimal sensor configurations are evaluated for their ability to accurately predict wind speeds and directions at other locations. These configurations have been used to demonstrate the potential of using a discrete population of models for simulating the outdoor wind-environment of buildings.