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Journals / Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji / 2017 / Cilt: 5 - Sayı: 3

The Performance Evaluation of Solar Control Methods in Buildings: A Multi-Objective Approach

Binalarda Güneş Kontrol Yöntemlerinin Optimizasyon Temelli Performans Değerlendirilmesi

İpek GÜRSEL DİNO
Pages
71–87
Publication Date
2022-10-18
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

According to the results obtained in the study, it is possible to achieve energy savings up to 25% and to increase the window size by 123% with optimal solar control methods, without decreasing the daylighting performance of a building.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, a tool and method that support the analysis of different solar control methods in buildings through a genetic optimization algorithm are proposed.
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Key Findings & Data:According to the results obtained in the study, it is possible to achieve energy savings up to 25% and to increase the window size by 123% with optimal solar control methods, without decreasing the daylighting performance of a building.
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Conclusion & Impact:The proposed optimization-based analysis method can support numerical comparative evaluation of different solar control alternatives in the design process.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Bina güneş kontrolü · Bina enerji tüketimi · Bina günışığı aydınlatması

Abstract

In this study, a tool and method that support the analysis of different solar control methods in buildings through a genetic optimization algorithm are proposed. First, eight scenarios of different glazing alternatives and standard shading methods were developed. These scenarios were implemented on a sample building design and the genetic optimization algorithm was run. The algorithm optimizes energy consumption and daylight illumination using an existing energy simulation tool, taking window sizes and shading parameters as variables. Using this algorithm, the building's Pareto solutions are calculated for each scenario. Using these Pareto solutions, a comparative analysis of the different solar control scenarios was made. According to the results obtained in the study, it is possible to achieve energy savings up to 25% and to increase the window size by 123% with optimal solar control methods, without decreasing the daylighting performance of a building. The proposed optimization-based analysis method can support numerical comparative evaluation of different solar control alternatives in the design process.

Özet

Bu çalışmada, binalarda farklı güneş kontrol yöntemlerinin karşıtlaştırmalı analizini, bir genetik optimizasyon algoritması ile destekleyen bir tasarım aracı ve yöntem, önerilmektedir. Çalışmada ilk olarak farklı cam alternatifleri ve sıklıkla kullanılan bazı standart gölgeleme yöntemlerinin oluşturduğu sekiz senaryo geliştirilmiştir. Ardından bu senaryolar, örnek bir bina tasarımı üstünde uygulanarak genetik optimizasyon algoritması uygulanmıştır. Algoritma, pencere büyüklüklerini ve gölgelik boyut parametrelerini değişken olarak alarak, enerji tüketimini ve günışığı aydınlatmasını optimize etmektedir. Bu algoritma kullanılarak, her senaryo için binanın Pareto çözümleri hesaplanmıştır. Bu Pareto çözümler kullanılarak, farklı güneş kontrol senaryolarının birbirleriyle olan karşılaştırmalı analizi yapılmıştır. Çalışmada elde edilen sonuçlara göre optimal güneş kontrol yöntemleri ile, bir binanın günışığı aydınlatma performansını azaltmadan %25’e kadar enerji tasarrufu sağlanabilmekte, pencere büyüklükleri ise 123% oranında arttırılabilmektedir. Daha genel bir açıdan bakıldığında ise, önerilen optimizasyon temelli analiz yöntemi, tasarım süreçlerinde farklı güneş kontrol alternatiflerinin sayısal karşılaştırmalı değerlendirilmesini destekleyebilmektedir.

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Cite This Article

DİNO, İ. G. (2022). The Performance Evaluation of Solar Control Methods in Buildings: A Multi-Objective Approach. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, Cilt: 5 - Sayı: 3, 71–87. https://ajindex.com/articles/522156

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References (13)

  1. Tuhus-Dubrow, D. and M. Krarti, Genetic-algorithm based approach to optimize building envelope design for residential buildings. Building and environment, 2010. 45(7): p. 1574-1581
  2. Granadeiro, V., et al., Building envelope shape design in early stages of the design process: Integrating architectural design systems and energy simulation. Automation in construction, 2013. 32: p. 196-209
  3. Lin, S.-H.E. and D.J. Gerber, Designing-in performance: A framework for evolutionary energy performance feedback in early stage design. Automation in Construction, 2014. 38: p. 59-73
  4. Caldas, L., Generation of energy-efficient architecture solutions applying GENE_ARCH: An evolution-based generative design system. Advanced Engineering Informatics, 2008. 22(1): p. 59-70
  5. Kämpf, J.H. and D. Robinson, Optimisation of building form for solar energy utilisation using constrained evolutionary algorithms. Energy and Buildings, 2010. 42(6): p. 807-814
  6. Echenagucia, T.M., et al., The early design stage of a building envelope: Multi-objective search through heating, cooling and lighting energy performance analysis. Applied Energy, 2015. 154: p. 577-591
  7. Lin, Y.-H., et al., Design optimization of office building envelope configurations for energy conservation. Applied Energy, 2016. 171: p. 336-346
  8. Huang, Y., J.-l. Niu, and T.-m. Chung, Comprehensive analysis on thermal and daylighting performance of glazing and shading designs on office building envelope in cooling-dominant climates. Applied energy, 2014. 134: p. 215-228
  9. Manzan, M., Genetic optimization of external fixed shading devices. Energy and Buildings, 2014. 72: p. 431-440
  10. Khoroshiltseva, M., D. Slanzi, and I. Poli, A Pareto-based multi-objective optimization algorithm to design energy-efficient shading devices. Applied Energy, 2016. 184: p. 1400-1410
  11. Lau, A.K.K., et al., Potential of shading devices and glazing configurations on cooling energy savings for high-rise office buildings in hot-humid climates: The case of Malaysia. International Journal of Sustainable Built Environment, 2016. 5(2): p. 387-399
  12. Schaffer, J.D. Multiple objective optimization with vector evaluated genetic algorithms. in Proceedings of the 1st international Conference on Genetic Algorithms. 1985. L. Erlbaum Associates Inc
  13. Horn, J., N. Nafpliotis, and D.E. Goldberg. A niched Pareto genetic algorithm for multiobjective optimization. in Evolutionary Computation, 1994. IEEE World Congress on Comput