1. Hausladen, G., de Saldanha, M., Liedl, P. Climate Skin. Basel: Birkhauser, 2008.
2. Fathy, H. Natural Energy and Vernacular Architecture. Chicago and London: The University of Chicago Press, 1986.
3. Olgyay, A., Olgyay, V. Solar Control and Shading Devices. New Jersey, 1977.
4. Lechner, N. Sustainable Design Methods for Architects. New Jersey: Wiley, 2015.
5. Du Montier, C., Potvin, A., Demers, C.M. Energy and daylighting potential for Adaptive Façades: Evaluation of movable insulated panels // Proc. of Int. Conf. on Adaptation and Movement in Architecture ICAMA 2013, October 2013, Toronto, Canada.
6. Grobman, Y.J., Capeluto, I.G., Austern, G. External shading in buildings: comparative analysis of daylighting performance in static and kinetic operation scenarios // Architectural Science Review.– 2016. – Vol. 60, No. 2. – P. 1–11, 2016.
7. Kensek, K., Hansanuwat, R. Environment Control Systems for Sustainable Design: A Methodology for Testing, Simulating and Comparing Kinetic Facade Systems // Journal of Creative Sustainable Architecture & Built Environment.– 2011. – Vol. 1, No. 11. – P. 27–46.
8. Lee, D.S., Koo, S.H., Seong, Y.B., Jo, J.H. Evaluating thermal and lighting energy performance of shading devices on kinetic façades // Sustainability (Switzerland).– 2016. – Vol. 8, No. 9. – P. 1–18.
9. Nielsen, M.V., Svendsen, S., Jensen, L.B. Quantifying the potential of automated dynamic solar shading in office buildings through integrated simulations of energy and daylight // Solar Energy.– 2011. – Vol. 85, No. 5. – P. 757–768.
10. El Sheikh, M., Gerber, D.J. Building Skin Intelligence // Proc. of the annual conf. of the Association of Computer Aided Design in Architecture ACADIA, 2011, pp. 170–177.
11. Sharaidin, K., Burry, J., Salim, F. Integration of Digital Simulation Tools With Parametric Designs to Evaluate Kinetic Façades for Daylight Performance // Physical Digitality: Proc. of the 30th eCAADe Conf., 2012, Vol. 2, pp. 701–709.
12. Wortmann, T., Costa, A., Nannicini, G., Schroepfer, T. Advantages of surrogate models for architectural design optimization // Artificial Intelligence for Engineering Design, Analysis and Manufacturing.– 2015. – Vol. 29, No. 4. – P. 471–481.
13. Kazanasmaz, T., Günaydin, M., Binol, S. Artificial neural networks to predict daylight illuminance in office buildings // Building and Environment.– 2009. – Vol. 44, No. 8. – P. 1751–1757.
14. Hu, J., Olbina, S. Illuminance-based slat angle selection model for automated control of split blinds // Building and Environment.– 2011. – Vol. 46, No. 3. – P. 786–796.
15. Skavara, M.E. Adaptive cellular automata façade trained by artificial neural network. Barlett School of Graduate Studies, University Collage of London, 2009.
16. Lee, E.S., DiBartolomeo, D.L., Selkowitz, S.E. Thermal and daylighting performance of an automated venetian blind and lighting system in a full-scale private office // Energy and Buildings. – Vol. 29, No. 1. – P. 47–63.
17. Wagdy, A., Fathy, F., Altomonte, S. Evaluating the Daylighting Performance of Dynamic Façades by Using New Annual Climate- Based Metrics Evaluating the Daylighting Performance of Dynamic Façades by Using New Annual Climate – Based Metrics // Proc. of the 32nd Int. Conf. on Passive and Low Energy Architecture, PLEA 2016. August 2016, Los Angeles, CA. – P. 941–947.
18. Sadeghipour Roudsari, M., Pak, M. Ladybug: a Parametric Environmental Plugin for Grasshopper To Help Designers Create an Environmentally-Conscious Design // 13th Conf. of Int. building Performance Simulation Association, 2013, pp. 3129–3135. 19. Nabil, A., Mardaljevic, J. Useful daylight illuminance: a new paradigm for assessing daylight in buildings // Lighting Research and Technology // 2005. – Vol. 37, No. 1. – P. 41–59.
20. Zhang, G., Patuwo, B.E., Hu, M.Y. Forecasting with artificial neural networks: The state of the art // Int. journal of forecasting.– 1998. – Vol. 14. – P. 35–62.
21. Haykin, S. Neural Networks and Learning Machines. 2009.
22. Riedmiller, M., Braun, H. A direct adaptive method for faster backpropagation learning: The RPROP algorithm // Proc. of IEEE Int. Conf. on Neural Networks, 1993, pp. 586–591.
23. Chatzikonstantinou, I. A Computational Intelligence Decision-Support Environment for Architectural and Building Design: CIDEA // IEEE Congress on Evolutionary Computation 2016, pp. 3887–3894.
24. Bader, J., Zitzler, E. HypE: An Algorithm for Fast Hypervolume-Based Many-Objective Optimization // Evolutionary Computation.– 2008. – Vol. 19, No. 1. – P. 45–76.
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