OPTIMIZING CAMERA PLACEMENT FOR A LUMINANCE-BASED SHADING CONTROL SYSTEM

Shading control strategies are nowadays employed in office environments to improve the visual comfort of the user. These strategies are often solely illuminance-based whereas comfort metrics as the Daylight Glare Probability (DGP) also need luminance values. In previous studies, daylight glare has been assessed by calculating the DGP from luminance maps obtained via a luminance camera or from a High Dynamic Range (HDR) image obtained with a commercially available camera. These detectors are traditionally mounted close to the user and aligned with the viewing direction. In real office environments, this camera position is impractical, and simulations based on machine learning techniques have shown a relation between the DGP from an observer's viewpoint and the DGP calculated from a ceiling camera. This paper experimentally validates this method in a real office environment by using two different cameras and two different illuminance sensors, i.e., a low-cost illuminance sensor and a calibrated sensor. Both cameras render similar results, although one camera overestimates the DGP. Moreover, the shortcomings of the simulation results are pinpointed and the obstacles for a realistic application are addressed. Furthermore, it was found that when moving the cameras to different positions, the sun position was shown to be an informative additional input for correlating the two DGP values. In future work, additional data will be analysed to determine the performance in other weather conditions and window orientations.

Numerical simulation on year-round performance of water-flow window with different shading control modes

Water-flow window combines the functions of transparent building envelop, solar collector, as well as sun shading. Coloured particles are added to the flowing water within window cavity in order to provide extra shading, prevent indoor glare and enhance solar collection. The energy consumption of both air-conditioning system and water heating device can be further reduced. The aim of present investigation is to predict and analyze the year-round energy performance of a well-insulated water-flow window with different shading control modes. Accordingly, physical model of water-flow window is built up. FORTRAN program is developed and utilized in the numerical simulation. Results show that water-flow window can achieve exceptional sun blocking and cooling load reduction without occupying extra space. The year-round solar energy collection efficiency is within the range of 17.95∼21.06%. At the same time, indoor heat gain through the window can be reduced by around 50% compared with common double-layer window. The water-flow window under discussion has great application potential in buildings with constant hot water demand and high-density air-conditioning cooling load. In practice, the shading control mode should be carefully decided, and factors including climate region, hot water demand, window size and occupants' preference on indoor light environment should be taken into consideration. Numerical simulation proves to be an efficient method in predicting the energy performance of similar innovative-designed windows and a good assist in decision-making of real projects.