Studies show that movable shading systems have lots of benefits for building performance. Minimizing energy consumption and maximizing daylight usage are natural expectations when using these systems. To find optimal solutions for these systems, different methods have been used. Today, optimization methods are used to solve this problem. In the literature, there are few studies about optimization of movable shading systems. This paper aims to identify different movable shading systems, optimization types, and computational optimization tools that are used. Research findings and future projections based on the reviewed papers are summarized.
1. Tzempelikos A. and Athienitis A.K. The impact of shading design and control on building cooling and lighting demand // Solar Energy. 2007, Vol. 81, #3, pp. 369–382.
2. Shen H. and Tzempelikos A. Daylighting and energy analysis of private offices with automated interior roller shades // Solar Energy. 2012, Vol. 86, #2, pp. 681–704. 3. Bellia L., Marino C., Minichiello F. and Pedace A. An overview on solar shading systems for buildings // Energy Procedia. 2014, Vol. 62, pp. 309–317.
4. Al Thobaiti M.M. Intelligent and adaptive facade system: The impact of intelligent and adaptive facade on the performance and energy efficiency of buildings. 2014.
5. Al-Masrani S.M. and Al-Obaidi K.M. Dynamic shading systems: A review of design parameters, platforms and evaluation strategies // Automation in construction. 2019, Vol. 102, pp. 195–216.
6. Kirimtat A., Koyunbaba B.K., Chatzikonstantinou I., Sarıyıldız S., and Suganthan P.N. Multi-objective optimization for shading devices in buildings by using evolutionary algorithms. in 2016 IEEE Congress on Evolutionary Computation (CEC). 2016. IEEE.
7. Kasinalis C., Loonen R.C.G.M., Costola D., and Hensen J.L.M. Framework for assessing the performance potential of seasonally adaptable facades using multiobjective optimization // Energy and Buildings. 2014, Vol. 79, pp. 106–113.
8. Shen H. and Tzempelikos A. Daylight-linked synchronized shading operation using simplified modelbased control // Energy and Buildings. 2017, Vol. 145, pp. 200–212.
9. Choi S.-J., Lee D.-S., and Jo J.-H. Lighting and cooling energy assessment of multi-purpose control strategies for external movable shading devices by using shaded fraction // Energy and Buildings. 2017, Vol. 150, pp. 328–338.
10. Kirimtat, A., Krejcar, O., Ekici, B. and Taşgetiren, M.F. Multi-objective energy and daylight optimization of amorphous shading devices in buildings // Solar Energy, 2019, Vol. 185, pp. 100–111.
11. Yao, J. An investigation into the impact of movable solar shades on energy, indoor thermal and visual comfort improvements // Building and Environment, 2014, Vol. 71, pp. 24–32.
12. Kazanasmaz, T. Fuzzy logic model to classify effectiveness of daylighting in an office with a movable blind system // Building and Environment, 2013, Vol. 69, pp. 22–34.
13. Uygun, I., Kazanasmaz, Z. and Kale, S. Optimization of energy efficient luminaire layout design in workspaces. in Proceedings of International Conference CISBAT 2015 Future Buildings and Districts Sustainability from Nano to Urban Scale. 2015. LESO-PB, EPFL.
14. González, J. and Fiorito, F. Daylight design of office buildings: Optimisation of external solar shadings by using combined simulation methods // Buildings, 2015, Vol. 5, #2, pp. 560–580.
15. Mukherjee, I. and Ray, P.K. A review of optimization techniques in metal cutting processes // Computers and Industrial Engineering, 2006, Vol. 50, #(1–2), pp. 15–34.
16. Sharaidin, K., Burry, J. and Salim, F. Integration of digital simulation tools with parametric designs to evaluate kinetic facades for daylight performance. in Physical digitality: proceedings of the 30th eCAADe conference. 2012.
17. Chen, J.-Y. and Huang, S.-C. Adaptive building facade optimisation: An integrated Green-BIM approach. 2016.
18. Manzan, M. and Clarich, A. FAST energy and daylight optimization of an office with fixed and movable shading devices // Building and Environment, 2017, Vol. 113, pp. 175–184.
19. Shan, R. and Junghans, L. “Adaptive radiation” optimization for climate adaptive building facade design strategy. in Building Simulation. 2018. Springer.
20. Lim, Y.-W. and Heng, C.Y.S. Dynamic internal light shelf for tropical daylighting in high-rise office buildings // Building and Environment, 2016, Vol. 106, pp. 155–166.
21. Sheikh, M.E. and Gerber, D. Building skin intelligence a parametric and algorithmic tool for daylighting performance design integration // Proceedings of the Annual Conference of the Association of Computer Aided Design in Architecture ACADIA, 2011, pp. 170–177.
22. Grynning, S., Time, B. and Matusiak, B. Solar shading control strategies in cold climates – Heating, cooling demand and daylight availability in office spaces // Solar Energy, 2014, Vol. 107, pp. 182–194.
23. Stazi, F., Marinelli, S., Di Perna, C. and Munafo, P. Comparison on solar shadings: Monitoring of the thermo-physical behaviour, assessment of the energy saving, thermal comfort, natural lighting and environmental impact // Solar Energy, 2014, Vol. 105, pp. 512–528.
24. Olbina, S. and Beliveau, Y. Developing a transparent shading device as a daylighting system // Building Research and Information, 2009, Vol. 37, #2, pp. 148–163.
25. Lee, K., Han, K. and Lee, J. The Impact of Shading Type and Azimuth Orientation on the Daylighting in a Classroom–Focusing on Effectiveness of Facade Shading, Comparing the Results of DA and UDI // Energies, 2017, Vol. 10, #5, p. 635.
26. Chan, Y.-C. and Tzempelikos, A. Daylighting and energy analysis of multi-sectional facades // Energy Procedia, 2015, Vol. 78, pp. 189–194.
27. Appelfeld, D., McNeil, A. and Svendsen, S. An hourly based performance comparison of an integrated micro-structural perforated shading screen with standard shading systems // Energy and Buildings, 2012, Vol. 50, pp. 166–176.
28. Lim, H.S. and Kim, G. Predicted performance of shading devices for healthy visual environment // Indoor and Built Environment, 2010, Vol. 19, #4, pp. 486–496.
29. Kirimtat, A., Koyunbaba B.K., Chatzikonstantinou, I. and Sarıyıldız, S. Review of simulation modeling for shading devices in buildings // Renewable and Sustainable Energy Reviews, 2016, Vol. 53, pp. 23–49.
30. Tzempelikos, A., Athienitis, A.K. and Karava, P. Simulation of facade and envelope design options for a new institutional building // Solar Energy, 2007, Vol. 81, #9, pp. 1088–1103.
31. Tzempelikos, A. and Shen, H. Comparative control strategies for roller shades with respect to daylighting and energy performance // Building and Environment, 2013, Vol. 67, pp. 179–192.
32. Singh, R., Lazarus, I.J. and Kishore, V.V.N. Effect of internal woven roller shade and glazing on the energy and daylighting performances of an office building in the cold climate of Shillong // Applied energy, 2015, Vol. 159, pp. 317–333.
33. Meresi, A. Evaluating daylight performance of light shelves combined with external blinds in south-facing classrooms in Athens, Greece // Energy and Buildings, 2016, Vol. 116, pp. 190–205.
34. Sherif, A.H., Sabry, H.M. and Gadelhak, M.I. The impact of changing solar screen rotation angle and its opening aspect ratios on Daylight Availability in residential desert buildings // Solar Energy, 2012, Vol. 86, #11, pp. 3353–3363.
35. Jayathissa, P., Luzzatto, M., Schmidli, J., Hofer, J., Nagy, Z. and Schlueter, A. Optimising building net energy demand with dynamic BIPV shading // Applied energy, 2017, Vol. 202, pp. 726–735.
36. Yi, Y.K., Yin, J. and Tang, Y. Developing an advanced daylight model for building energy tool to simulate dynamic shading device // Solar Energy, 2018, Vol. 163, pp. 140–149.
37. Ayoub, M. Integrating illuminance and energy evaluations of cellular automata controlled dynamic shading system using new hourly-based metrics // Solar Energy, 2018, Vol. 170, pp. 336–351.
38. Lee, K., Han, K. and Lee, J. Feasibility study on parametric optimization of daylighting in building shading design // Sustainability, 2016, Vol. 8, #12, pp. 1220.
39. Galapagos – Grasshopper. 14.06.2019]; Available from: https://www.grasshopper3d.com/group/galapagos.
40. Deb, K., Pratap, A., Agarwal, S. and Meyarivan, T. A fast and elitist multiobjective genetic algorithm // NSGA-II. IEEE transactions on evolutionary computation, 2002, Vol. 6, #2, pp. 182–197.
41. Optimo – Optimization Algorithm for Dynamo. 24.06.2019]; Available from: https://dynamobim.org/optimo/.
42. Software modeFRONTIER; Integration, Optimization and Post Processing. 29.06.2019]; Available from: https:// www.esss.co/en/modefrontier-optimization-software/.
43. Datta, S. In Optimization in Industry: Present Practices and Future Scopes, pp. 63–65.
44. Li, L., Qu, M. and Peng, S. Performance evaluation of building integrated solar thermal shading system: Building energy consumption and daylight provision // Energy and Buildings, 2016, Vol. 113, pp. 189–201.

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