ANN based automatic slat angle control of venetian blind for minimized total load in an office building. (1st March 2019) - Yeon, Sanghun; Yu, Byeongho; Seo, Byeongmo; Yoon, Yeobeom; Lee, Kwang Ho
Highlights: ANN based slat angle control did not cause discomfort glare. Optimally controlled blind slat angle using ANN showed slightly higher lighting load than the fixed slat angle cases. As a result of the real-time optimal control of blind slat angle through the ANN, the reduction effect of the cooling load was found to be significant. When the blind slat angle was optimally controlled through the ANN, the total load reduction effect was shown to be remarkably excellent. Abstract: Windows are the only part of a building that can directly penetrate the solar radiation into the occupied space and thus the shading devices are needed to control the solar penetration. A variety of research have been conducted to develop the optimized slat angle control in the existing literature, but the research incorporating artificial intelligence technique with slat angle control is limited thus far. Therefore, in this study, the ANN (Artificial Neural Network) model was applied to minimize the combined total load consisting of lighting, cooling, and heating loads through automatic slat angle control of venetian blinds. A three-story rectangular office building was simulated using EnergyPlus, and dimming control was applied to control the lighting. The interlocked simulation between Matlab and EnergyPlus was conducted through BCVTB. As a result of comparing automatic blind control via the ANN to fixed blind slat angle, the automatic blind control via the ANN showed 9.1% lower total load than the blind angle fixed at 50°. It was confirmed that the cooling and heating load could be significantly reduced by real-time automatic control via the ANN under various operating conditions, rather than fixing the blinds at one angle.