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

Abstract 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.

[1]  L Roche.  Summertime performance of an automated lighting and blinds control system , 2002 .

[2]  J. Scartezzini,et al.  Comparing daylighting performance assessment of buildings in scale models and test modules , 2005 .

[3]  Myoung Souk Yeo,et al.  Automated blind control to maximize the benefits of daylight in buildings , 2010 .

[4]  Jiejin Cai,et al.  Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks , 2009 .

[5]  Tuğçe Kazanasmaz,et al.  Artificial neural networks to predict daylight illuminance in office buildings , 2009 .

[6]  Christoph F. Reinhart,et al.  Findings from a survey on the current use of daylight simulations in building design , 2006 .

[7]  J. Michalsky,et al.  Modeling daylight availability and irradiance components from direct and global irradiance , 1990 .

[8]  J. Mardaljevic.  Examples of Climate-Based Daylight Modelling , 2006 .

[9]  Athanasios Tzempelikos,et al.  A methodology for simulation of daylight room illuminance distribution and light dimming for a room with a controlled shading device , 2002 .

[10]  V. Geros,et al.  Modeling and predicting building's energy use with artificial neural networks: Methods and results , 2006 .

[11]  Teodoro López-Moratalla,et al.  Computing the solar vector , 2001 .

[12]  M.H. Hassoun,et al.  Fundamentals of Artificial Neural Networks , 1996, Proceedings of the IEEE.

[13]  P. Littlefair.  Daylight prediction in atrium buildings , 2002 .

[14]  Anca D. Galasiu,et al.  Impact of window blinds on daylight-linked dimming and automatic on/off lighting controls , 2004 .

[15]  T. Muneer.  Solar radiation and daylight models , 2004 .

[16]  A. Athienitis,et al.  The impact of shading design and control on building cooling and lighting demand , 2007 .

[17]  S. Olbina.  SPLIT CONTROLLED BLINDS AS A THERMAL AND DAYLIGHTING ENVIRONMENTAL CONTROL SYSTEM , 2009 .

[18]  Dhw Li,et al.  Average daylight factor for the 15 CIE standard skies , 2006 .

[19]  John Mardaljevic,et al.  Dynamic Daylight Performance Metrics for Sustainable Building Design , 2006 .

[20]  John R. Wallace,et al.  A Ray tracing algorithm for progressive radiosity , 1989, SIGGRAPH '89.

[21]  Myoung-Souk Yeo,et al.  An experimental study on the environmental performance of the automated blind in summer , 2009 .

[22]  Robert Clear,et al.  Office Worker Response to an Automated Venetian Blind and Electric Lighting System: A Pilot Study , 1998 .

[23]  Stephen Selkowitz,et al.  Thermal and daylighting performance of an automated venetian blind and lighting system in a full-scale private office , 1998 .

[24]  Paul S. Heckbert.  Adaptive radiosity textures for bidirectional ray tracing , 1990, SIGGRAPH.

[25]  F. Topalis,et al.  A CRITICAL REVIEW OF SIMULATION TECHNIQUES FOR DAYLIGHT RESPONSIVE SYSTEMS , 2005 .

[26]  John Mardaljevic,et al.  Useful daylight illuminance: a new paradigm for assessing daylight in buildings , 2005 .

[27]  B. K. Saxena,et al.  Evaluation of spatial illuminance in buildings , 1979 .

[28]  Antoine Guillemin,et al.  An energy-efficient controller for shading devices self-adapting to the user wishes , 2002 .

[29]  Ryohei Yokoyama,et al.  Prediction of energy demands using neural network with model identification by global optimization , 2009 .

[30]  Roberto Grena,et al.  An algorithm for the computation of the solar position , 2008 .

[31]  Moncef Krarti,et al.  Estimation of lighting energy savings from daylighting , 2009 .

[32]  Raja R. A. Issa,et al.  Development of an Automated Split Control System for Blinds , 2009 .

[33]  John Mardaljevic,et al.  Useful daylight illuminances: A replacement for daylight factors , 2006 .