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A Deep Cognitive Venetian Blinds System for Automatic Estimation of Slat Orientation

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Abstract

Shading devices are used to control solar radiations that penetrate into the occupied environment through the windows with the aim of ensuring visual comfort and saving the building’s energy consumption. Venetian blinds are commonly employed for the practicality and ease of application. However, occupants often do not change slat orientation causing unnecessary consumption and discomfort. Hence, automatic shading control systems can enhance the energy performance and make the environment more comfortable. In this context, a cognitive venetian blinds system, denoted to as CogVBS and based on a deep feed-forward neural network, is proposed for automatic estimation of slat angle. Here, the EnergyPlus software is employed to simulate the test environment. Experimental results demonstrate the promising performance of the proposed deep CogVBS, reporting a root mean square error (RMSE) and correlation coefficient (r) of 0.1018±0.0015 and 0.9319±0.0020, respectively. The achieved outcomes encourage the use of the proposed cognitive system in realistic environments.

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Data Availability

The dataset analyzed during the current study is available from the corresponding author on reasonable request.

References

  1. Lai K, Wang W, Giles H. Solar shading performance of window with constant and dynamic shading function in different climate zones. Sol Energy. 2017;147:113–25.

    Article  Google Scholar 

  2. Luo S, Li H, Mao Y, Yang C. Experimental research on a novel sun shading & solar energy collecting coupling device for inpatient building in hot summer and cold winter climate zone in China. Appl Therm Eng. 2018;142:89–99.

    Article  Google Scholar 

  3. Al-Masrani SM, Al-Obaidi KM, Zalin NA, Isma MA. Design optimisation of solar shading systems for tropical office buildings: challenges and future trends. Sol Energy. 2018;170:849–72.

    Article  Google Scholar 

  4. Huang L, Wu J. Effects of the splayed window type on daylighting and solar shading. Build Environ. 2014;81:436–47.

    Article  Google Scholar 

  5. Evangelisti L, Guattari C, Asdrubali F, de Lieto Vollaro R. An experimental investigation of the thermal performance of a building solar shading device. J Build Eng. 2020;28:101089.

  6. Aste N, Adhikari RS, Del Pero C. An algorithm for designing dynamic solar shading system. Energy Procedia. 2012;30:1079–89.

    Article  Google Scholar 

  7. Hashemi A, Khatami N. Effects of solar shading on thermal comfort in low-income tropical housing. Energy Procedia. 2017;111:235–44.

    Article  Google Scholar 

  8. Grynning S, Time B, Matusiak B. Solar shading control strategies in cold climates-heating, cooling demand and daylight availability in office spaces. Sol Energy. 2014;107:182–94.

    Article  Google Scholar 

  9. Nicoletti F, Carpino C, Cucumo MA, Arcuri N. The control of venetian blinds: a solution for reduction of energy consumption preserving visual comfort. Energies. 2020;13(7):1731.

    Article  Google Scholar 

  10. Paik J, Kim J, Yeo M, Kim K. A study on the occupants use of the blinds in office building. Journal of the Architectural Institute of Korea. 2006;22:311–8.

    Google Scholar 

  11. Koo SY, Yeo MS, Kim KW. Automated blind control to maximize the benefits of daylight in buildings. Build Environ. 2010;45(6):1508–20.

    Article  Google Scholar 

  12. Guillemin A, Molteni S. An energy-efficient controller for shading devices self-adapting to the user wishes. Build Environ. 2002;37(11):1091–7.

    Article  Google Scholar 

  13. Zhang S, Birru D. An open-loop venetian blind control to avoid direct sunlight and enhance daylight utilization. Sol Energy. 2012;86(3):860–6.

    Article  Google Scholar 

  14. Chan YC, Tzempelikos A. Efficient venetian blind control strategies considering daylight utilization and glare protection. Sol Energy. 2013;98:241–54.

    Article  Google Scholar 

  15. Karlsen L, Heiselberg P, Bryn I, Johra H. Solar shading control strategy for office buildings in cold climate. Energy Build. 2016;118:316–28.

    Article  Google Scholar 

  16. Carletti C, Sciurpi F, Pierangioli L, Asdrubali F, Pisello AL, Bianchi F, Sambuco S, Guattari C. Thermal and lighting effects of an external venetian blind: experimental analysis in a full scale test room. Build Environ. 2016;106:45–56.

    Article  Google Scholar 

  17. Al Touma A, Ouahrani D. Shading and day-lighting controls energy savings in offices with fully-glazed façades in hot climates. Energy Build. 2017;151:263–74.

    Article  Google Scholar 

  18. Eltaweel A, Su Y. Controlling venetian blinds based on parametric design; via implementing Grasshopper’s plugins: a case study of an office building in Cairo. Energy Build. 2017;139:31–43.

    Article  Google Scholar 

  19. Kunwar N, Cetin KS, Passe U, Zhou X, Li Y. Energy savings and daylighting evaluation of dynamic venetian blinds and lighting through full-scale experimental testing. Energy. 2020;197:117190.

  20. Hu J, Olbina S. Illuminance-based slat angle selection model for automated control of split blinds. Build Environ. 2011;46(3):786–96.

    Article  Google Scholar 

  21. Yeon S, Yu B, Seo B, Yoon Y, Lee KH. Ann based automatic slat angle control of venetian blind for minimized total load in an office building. Sol Energy. 2019;180:133–45.

    Article  Google Scholar 

  22. Luo Z, Sun C, Dong Q, Yu J. An innovative shading controller for blinds in an open-plan office using machine learning. Build Environ. 2020;107529.

  23. Bellia L, De Falco F, Minichiello F. Effects of solar shading devices on energy requirements of standalone office buildings for Italian climates. Appl Therm Eng. 2013;54(1):190–201.

    Article  Google Scholar 

  24. Crawley DB, Lawrie LK, Pedersen CO, Liesen RJ, Fisher DE, Strand RK, Taylor RD, Winkelmann R, Buhl W, Huang YJ et al. Energyplus, a new-generation building energy simulation program. In: Proceedings of Building Simulation ’99, vol. 1. 1999. p. 81–88.

  25. Ghosh A, Neogi S. Effect of fenestration geometrical factors on building energy consumption and performance evaluation of a new external solar shading device in warm and humid climatic condition. Sol Energy. 2018;169:94–104.

    Article  Google Scholar 

  26. Loutzenhiser PG, Manz H, Carl S, Simmler H, Maxwell GM. Empirical validations of solar gain models for a glazing unit with exterior and interior blind assemblies. Energy and Buildings. 2008;40(3):330–40.

    Article  Google Scholar 

  27. Ieracitano C, Mammone N, Paviglianiti A, Morabito FC. A conditional Generative Adversarial Network and transfer learning-oriented anomaly classification system for electrospun nanofibers. Int J Neural Syst. S012906572250054X. https://doi.org/10.1142/S012906572250054X.

  28. Ieracitano C, Mammone N, Versaci M, Varone G, Ali AR, Armentano A, Calabrese G, Ferrarelli A, Turano L, Tebala C, Hussain Z, Sheikh Z, Sheikh A, Sceni G, Hussain A, Morabito FC. A fuzzy-enhanced deep learning approach for early detection of Covid-19 pneumonia from portable chest X-ray images. Neurocomputing. 2022;481:202–215 S0925231222000741. https://doi.org/10.1016/j.neucom.2022.01.055.

  29. Ieracitano C, Morabito FC, Hussain A, Mammone N. A hybrid-domain deep learning-based BCI for discriminating hand motion planning from EEG sources. Int J Neural Syst. 2021;31(09):2150038. https://doi.org/10.1142/S0129065721500386.

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Funding

This work was supported by the European Commission, the European Social Fund, and the Calabria Region — POR Calabria FESR FSE 2014-2020 — Grant Ref. C39B18000080002; by the Programma Operativo Nazionale (PON) “Ricerca e Innovazione” 2014-2020 (Grant Ref. C35F21001220009, code: I05); and by PON 2014-2020, COGITO project — Grant Ref. ARS01\(\_\)00836.

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Authors and Affiliations

  1. DICEAM, University Mediterranea of Reggio Calabria, Via Graziella Feo di Vito, 89124, Reggio Calabria, Italy

    Cosimo Ieracitano, Mario Versaci, Francesco Carlo Morabito & Nadia Mammone

  2. DIMEG, University of Calabria, Rende, Italy

    Francesco Nicoletti & Natale Arcuri

  3. DIIES, University Mediterranea of Reggio Calabria, Via Graziella Feo di Vito, 89124, Reggio Calabria, Italy

    Giuseppe Ruggeri

Authors
  1. Cosimo Ieracitano
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  2. Francesco Nicoletti
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  3. Natale Arcuri
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  4. Giuseppe Ruggeri
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  5. Mario Versaci
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  6. Francesco Carlo Morabito
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  7. Nadia Mammone
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Corresponding author

Correspondence to Cosimo Ieracitano.

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Ieracitano, C., Nicoletti, F., Arcuri, N. et al. A Deep Cognitive Venetian Blinds System for Automatic Estimation of Slat Orientation. Cogn Comput 14, 2203–2211 (2022). https://doi.org/10.1007/s12559-022-10054-y

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  • DOI: https://doi.org/10.1007/s12559-022-10054-y

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