期刊
JOURNAL OF BUILDING ENGINEERING
卷 82, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.jobe.2023.108181
关键词
Living lab; Fully glazed facades; Control strategy; Optimization; Cylindrical illuminance
This study develops an optimized glare-based control strategy for Venetian blinds in real-life buildings, aiming to improve visual conditions while saving energy. The results show that the optimized algorithm can significantly improve visual conditions for different activities in the building, although it may increase the use of electric lighting in certain cases.
environment, aiming simultaneously at energy savings. Control systems for shading devices applied in practice are mostly driven by a rule-based approach, that is usually tested under simplified conditions and hence its effectiveness in complex real-life cases is questionable. The present study develops an optimized glare-based control strategy for Venetian blinds in real-life open-space building with totally transparent facades. The research is based on the case study of the Co-Creation Center at the TU Delft campus, which can host three different types of events: presentations, meetings and workshops. The control strategy is developed within Grasshopper, a tool for parametric and optimization problems. Radial Basis Function Optimization (RBFOpt) is utilized for the computation of the optimal blinds' states. Within the developed control strategy, cylindrical illuminance (E,y,) is used as a glare index, giving the opportunity to evaluate its performance. Results show that the optimized algorithm can improve the existing visual conditions in the building by an average of 80% for all activity types, although it leads to an average increase of 7% of the time when electric lighting is needed, in comparison to the current rule-based control. Finally, E,y, displayed an overall agreement of 92.5% with DGP-based glare assessments, proving that in spaces with multiple windows and uncertain occupants' view direction, a view-independent index can predict glare risks as well as a state-of-the-art view-dependent metric.
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