Calibration of energy simulation using optimization for buildings with dynamic shading systems

Physics-based energy simulation methods are widely used for the prediction of building energy consumption and daylighting in commercial buildings. Such simulations are performed to evaluate the design of new buildings or to predict the benefits of various retrofits to the building's performance. However, there are uncertainties in the results from such simulations, with higher uncertainty if the model used is not validated empirically. This study focuses on the co-simulation of daylight and energy modeling to evaluate the energy performance impacts of dynamic shading. The Generalized Pattern Search algorithm is used to minimize the root mean square error between measured and simulated data to perform the calibration of the baseline energy model while adjusting the building envelope material properties. Next, the results using three different modeling methods for windows and shading devices in EnergyPlus were compared to measured data. The results show that the use of the simple layered model to represent the dynamic shading system performed better than other two models, when compared to the experimental data. They also indicate that part of the difference between the model results and experimental data originates from sky model calculations and ground reflectance inputs. From the results it is also observed that using different modeling methods for the window and shading device can impact the coincidental energy consumption results from cooling and lighting by up to 20%.