Illuminance-based slat angle selection model for automated control of split blinds

• Published in: Building and environment Vol. 46; no. 3; pp. 786 - 796
• Main Authors: Hu, Jia, Olbina, Svetlana
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.03.2011; Elsevier
• Subjects: Applied sciences; Artificial neural network (ANN); Automated; Automated blind control; Blinds; Building technical equipments; Buildings; Buildings. Public works; Computation methods. Tables. Charts; Control systems; Daylighting; Errors; Exact sciences and technology; External envelopes; Illuminance; Lighting; Mathematical models; Opening. Closure. Circulation (stairs, etc.); Optimization; Sensors; Slats; Split-controlled blinds; Structural analysis. Stresses; Useful daylight illuminance (UDI); Artificial neural network (ANN); Automated blind control; Daylighting; Split-controlled blinds; Useful daylight illuminance (UDI); Office; Validation; System design; Neural network; Forecast model; Modeling; Case study; Daylight; Simulation; Window blind; Automatic control; Illumination; Computing method
• ISSN: 0360-1323; 1873-684X
• DOI: 10.1016/j.buildenv.2010.10.013

Venetian blinds play an important role in controlling daylight in buildings. Automated blinds overcome some limitations of manual blinds; however, the existing automated systems mainly control the direct solar radiation and glare and cannot be used for controlling innovative blind systems such as split blinds. This research developed an Illuminance-based Slat Angle Selection (ISAS) model that predicts the optimum slat angles of split blinds to achieve the designed indoor illuminance. The model was constructed based on a series of multi-layer feed-forward artificial neural networks (ANNs). The illuminance values at the sensor points used to develop the ANNs were obtained by the software EnergyPlus™. The weather determinants (such as horizontal illuminance and sun angles) were used as the input variables for the ANNs. The illuminance level at a sensor point was the output variable for the ANNs. The ISAS model was validated by evaluating the errors in the calculation of the: 1) illuminance and 2) optimum slat angles. The validation results showed that the power of the ISAS model to predict illuminance was 94.7% while its power to calculate the optimum slat angles was 98.5%. For about 90% of time in the year, the illuminance percentage errors were less than 10%, and the percentage errors in calculating the optimum slat angles were less than 5%. This research offers a new approach for the automated control of split blinds and a guide for future research to utilize the adaptive nature of ANNs to develop a more practical and applicable blind control system.