Buildings in the United States consume approximately 40% of energy and 72% of electricity. Commercial buildings consume approximately 1.9 EJ of energy in United States, 50% of which is used for heating, cooling, and lighting applications. It is estimated that windows contribute to 34% of the energy used for heating and cooling. Shading devices help to reduce the energy consumption during the cooling season, and can also impact the energy consumption in the heating season by reducing the solar heat gain entering the building. In addition to this, shading devices also can adjust the amount of daylight that enters a building and the associated visual comfort of the occupant(s). Manually controlled shading device are not operated frequently enough to achieve a balance between energy savings and occupant comfort. Hence, automated controls of shading devices are required to minimize energy consumption without causing visual discomfort to the occupants in a space.

Past studies on automated shading have mostly been based on simulation. Evaluation of the impact of shading devices on both energy and daylight in buildings using experimental methods is highly limited. Further there is also a lack of efforts associated with validation of simulation results using experimental data. Many studies that use simulation for studying dynamic shading devices also used default control strategies available in daylight and/or energy simulation software. Thus, the impact of existing automated solutions used by different industries also needs to be evaluated to determine the energy savings and daylighting impact of these solutions.

In this work, full-scale experimental testing of dynamic shading is performed and the impact on energy consumption, daylighting and glare of a perimeter office space is evaluated. The testing was performed using three different shading devices and four control strategies for a period of approximately 6 months in three different orientations, including East, West and South. The impact of environmental variables such as temperature and solar irradiation was also evaluated when using dynamic shading with two different types of glazing. Secondly, energy and daylight model were created based on the experiments completed. The energy model was calibrated using the measured data to minimize the root mean squared error of energy consumption between the simulated and measured data using a generalized pattern search algorithm for optimization of material properties. Results from different models available for modeling windows and shading devices in the energy simulation software were compared to the measured data. Finally, different automated control strategies used within the shade automation industry were used for annual simulation of three vintages of medium office reference buildings in six different locations within United States. A total of 9 different cases were used for simulation, including the baseline case for evaluating the impact on energy, peak demand, daylighting, glare and shade operation. The results from this study are intended to inform researchers, engineers and designers about different options available for shading control, experimental setup for testing dynamic shading, modeling methods of shading devices and assessment of the annual performance of automated shading under different scenarios.