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Journals / Uludağ Üniversitesi Mühendislik Fakültesi Dergisi / 2017 / Cilt: 22 Sayı: 3

Energy Performance Classes Prediction of Concrete Buildings with Artificial Intelligence Models

YAPAY ZEKÂ MODELLERİ İLE BETONARME YAPILARA AİT ENERJİ PERFORMANS SINIFLARININ TAHMİNİ

Ersin Namlı(İSTANBUL ÜNİVERSİTESİ)0000-0001-5980-9152
Melda Yücel
Pages
325–346
Publication Date
2017-12-01
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AI Summary & Key Takeaways

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

In cooling classification prediction model, with ANN algorithm and application of parameter optimizations 62% accuracy rate is obtained.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Today, increasingly important energy efficiency and effective use of energy necessitate more conscious and efficient use of the energy and resources are necessary in every area of ​​life.
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Key Findings & Data:When considered from this point of view, in recent years, energy performance which is an indicator of energy usage of a system and how effective and efficiently it is consumed takes an important place especially in the construction sector.
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Conclusion & Impact:In cooling classification prediction model, with ANN algorithm and application of parameter optimizations 62% accuracy rate is obtained.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Enerji verimliliği · Bina enerji performansı · Yapay Zekâ

Abstract

Today, increasingly important energy efficiency and effective use of energy necessitate more conscious and efficient use of the energy and resources are necessary in every area of ​​life. Especially the construction sector has the most remarkable applications of energy efficiency and various studies on energy area are carried out in many construction activities. When considered from this point of view, in recent years, energy performance which is an indicator of energy usage of a system and how effective and efficiently it is consumed takes an important place especially in the construction sector. In addition to difficulty in calculating of building energy performance, the calculation process requires expertness and calculation takes long time. In this study, an artificial intelligence based model developed for predicting energy classes which represents energy systems' efficiency level. With this aim, Input and output attributes are determined from architecture projects and energy performance certificates of 127 buildings' which are reinforced concrete structures with various qualities, so an original data set generated. In heating class model, multilayer artificial neural networks (ANN), Bayesian classifier, k-nearest neighbor classifier and C4.5 algorithm; for cooling class, only ANN based model was used. In heating classification prediction model the best classification accuracy value 92.126% were achieved by ANN algorithm, 117 of 127 instances correctly classified. In cooling classification prediction model, with ANN algorithm and application of parameter optimizations 62% accuracy rate is obtained.

Özet

Günümüzde giderek önemi artan enerji verimliliği ve etkin enerji kullanımı, hayatın her alanında ihtiyaç duyulan enerji ve kaynaklarının daha bilinçli ve verimli kullanılmasını gerektirmiştir. Özellikle inşaat sektörü enerji verimliliği konusunun en dikkat çeken uygulamalarına sahip alanıdır ve birçok inşai faaliyette enerji konusunda çeşitli çalışmalar gerçekleştirilmektedir. Bu açıdan bir sistemin enerjiyi kullanımı ve ne ölçüde etkin/verimli davrandığının göstergesi sayılabilecek performans, özellikle inşaat sektöründe önemli bir konuma gelmiştir. Ancak bina enerji performansının hesaplanma zorluğu, hesaplama işleminin uzmanlık istemesi ve uzun sürmesi enerji verimliliği ve etkin kullanımı konusunda yaşanacak gelişmeleri yavaşlatmaktadır. Bu çalışmada binaların enerji performansını belirleyen enerji sistemlerinin ve mimari özelliklerinin verimlilik seviyesini gösteren enerji sınıflarının yapay zekâ algoritmaları kullanılarak doğru ve kolayca tahmin edilebilmesi için bir sistem geliştirilmiştir. Bu amaçla, betonarme yapıda çeşitli niteliklerdeki 127 binanın mimari proje ve enerji kimlik belgeleriyle girdi ve çıktı nitelikleri belirlenerek özgün bir veri seti oluşturulmuştur. Isıtma sınıfı modelinde çok katmanlı yapay sinir ağı (YSA), Bayes sınıflandırıcı, k-en yakın komşu sınıflandırıcısı ve C4.5 algoritması; soğutma sınıfında ise yalnızca YSA modeli kullanılmıştır. Uygulamalar sonucunda ısıtma sınıfı modelinde 127 veriden 117’sinin doğru sınıflandırılmasıyla en yüksek sınıflandırma doğruluk değeri %92.126 ile YSA’nda gerçekleşmiştir. Soğutma sınıfındaysa YSA modelinin uygulanıp gerekli optimizasyonların gerçekleştirilmesiyle %62’ye varan bir oran elde edilmiştir.

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Cite This Article

Namlı, E., Yücel, M. (2017). Energy Performance Classes Prediction of Concrete Buildings with Artificial Intelligence Models. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, Cilt: 22 Sayı: 3, 325–346. https://doi.org/10.17482/uumfd.332320

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References (42)

  1. Aditya, L., Mahliaa, TMI., Rismanchi, B., Ng, HM., Hasan, MH., Metselaar, HSC., Murazaf, O. ve Aditiya, HB. (2017) A review on insulation materials for energy conservation in buildings, Renewable and Sustainable Energy Reviews, 73, 1352-1365. doi: 10.1016/j.rser.2017.02.034.
  2. Aydoğmuş, H. Y., Erdal, H. İ., Karakurt, O., Namli, E., Türkan, Y. S., & Erdal, H. (2015). A comparative assessment of bagging ensemble models for modeling concrete slump flow. Computers and Concrete, 16(5), 741-757. doi: 10.12989/cac.2015.16.5.741
  3. Bakar, NNA., Hassan, M.Y., Abdullah, H., Rahman, H.A., Abdullah, M.P. ve Hussin, F., Bandi, M. (2015) Energy efficiency index as an indicator for measuring building energy performance: A review, Renewable and Sustainable Energy Reviews, 44, 1-11. doi: 10.1016/j.rser.2014.12.018
  4. Chandel, S.S., Sharma, A. ve Marwaha, B.M. (2016) Review of energy efficiency initiatives and regulations for residential buildings in India, Renewable and Sustainable Energy Reviews, 54, 1443-1458. doi: 10.1016/j.rser.2015.10.060
  5. Chou, J. S., Lin, C. W., Pham, A. D., & Shao, J. Y. (2015). Optimized artificial intelligence models for predicting project award price. Automation in Construction, 54, 106-115. doi: 10.1016/j.autcon.2015.02.006
  6. Chou, J.S., Tsai, C.F., Pham, A.D. ve Lu, Y.H. (2014) Machine learning in concrete strength simulations: multi-nation data analytics, Construction and Building Materials, 73, 771-780. doi: 10.1016/j.conbuildmat.2014.09.054
  7. Clarke, J.A., Johnstone, C.M., Kelly, N.J., Strachan, P.A. ve Tuohy, P. (2008) The role of built environment energy efficiency in a sustainable UK energy economy, Energy Policy, 36(12), 4605-4609. doi: 10.1016/j.enpol.2008.09.004
  8. Durmuş, G. ve Önal, S. (2015) Assessment of energy performance of buildings constructed in different regions of Turkey, 2nd International Sustainable Buildings Symposium, Gazi Üniversitesi, Ankara, Türkiye, 920-924.
  9. Düzgün, B. ve Kömürgöz, G. (2014) Turkey’s energy efficiency assessment: white certificates systems and their applicability in Turkey, Energy Policy, 65(47), 465-474. doi: 10.1016/j.enpol.2013.10.036
  10. Elsland, R., Divrak, C., Fleiter, T. ve Wietschel, M. (2014) Turkey’s strategic energy efficiency plan–an ex anten impact assessment of the residential Sector, Energy Policy, 70 (2), 14-29. doi: 10.1016/j.enpol.2014.03.010
  11. Ercan, T. (2006). Performance Analysis of Desicion Tree Alghorithms on Water-Consumptıon Domain, Yüksek Lisans Tezi, Çankaya Üniversitesi, Türkiye.
  12. Erdal, H. (2015). Makine Öğrenmesi Yöntemlerinin İnşaat Sektörüne Katkısı: Basınç Dayanımı Tahminlemesi. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 21(3), 109-114. doi: 10.5505/pajes.2014.26121
  13. Erdal, H.I., Karakurt, O. ve Namli, E. (2013) High performance concrete compressive strength forecasting using ensemble models based on discrete wavelet transform, Engineering Applications of Artificial Intelligence, 26(4), 1246-1254. doi: 10.1016/j.engappai.2012.10.014
  14. Fossati, M., Scalco, V.A., Linczuk, V.C.C. ve Lamberts, R. (2016) Building energy efficiency: An overview of the Brazilian residential labeling scheme, Renewable and Sustainable Energy Reviews, 65, 1216-1231. doi: 10.1016/j.rser.2016.06.048
  15. Frank E., Hall, M.A., Witten, I.H. ve Pal CJ. (2016) Data Mining: Practical Machine Learning Tools and Techniques, 4th ed, Morgan Kaufmann, Cambridge, Massachusetts, USA.
  16. Gellert, R. (2010) “Inorganic Mineral Materials for Insulation in Buildings”, Materials for Energy Efficiency and Thermal Comfort in Buildings, Editör: Hall, MR., Woodhead Publishing Limited and CRC Press LLC, Cambridge, UK, 193-228.
  17. Han, J., Kamber, M. (2006) Data Mining: Concepts and Techniques, 2nd ed., Morgan Kaufmann, California, USA.
  18. Heravi, G. ve Qaemi, M. (2014) Energy performance of buildings: The evaluation of design and construction measures concerning building energy efficiency in Iran, Energy and Buildings, 75, 456-464. doi: 10.1016/j.enbuild.2014.02.035
  19. Hwang, W.J. ve Wen, K.W. (1998) Fast kNN classification algorithm based on partial distance search, Electronics Letters, 34(21), 2062-2063. doi: 10.1049/el:19981427
  20. Jha, S.K., Bilalovic, J., Jha, A., Patel, N. ve Zhang, H. (2017) Renewable energy: Present research and future scope of Artificial Intelligence, Renewable and Sustainable Energy Reviews, 77, 297-317. doi: 10.1016/j.rser.2017.04.018
  21. Kumar, V. ve Wu, X. (2009) The Top Ten Algorithms in Data Mining, Chapman & Hall/CRC Press, Boca Raton, Florida, USA.
  22. Laustsen, J. (2010) Energy Performance Certification of Building, The IEA Policy Pathway, Paris, Fransa.
  23. Li, B. ve Yao, R. (2009) Urbanisation and its impact on building energy consumption and efficiency in China, Renewable Energy, 34(9), 1994-1998. doi: 10.1016/j.renene.2009.02.015
  24. Li, J., Colombier, M. ve Giraud, P.N. (2009) Decision on optimal building energy efficiency standard in China—the case for Tianjin, Energy Policy, 37(7), 2546-2559. doi: 10.1016/j.enpol.2009.01.014
  25. Luger, G.F. (2009) Artificial Intelligence: Structures and Strategies for Complex Problem Solving, 6th ed., Pearson Education, Boston, USA.
  26. Mangan, S.D. ve Oral, G.K. (2016) Assessment of residential building performances for the different climate zones of Turkey in terms of life cycle energy and cost efficiency, Energy and Buildings, 110, 362-376. doi: 10.1016/j.enbuild.2015.11.002
  27. Martínez-Molina, A., Tort-Ausina, I., Cho, S. ve Vivancos, J.L. (2016) Energy efficiency and thermal comfort in historic buildings: A review, Renewable and Sustainable Energy Reviews, 61, 70-85. doi: 10.1016/j.rser.2016.03.018
  28. Mccarthy, J., (2007). What is Artificial Intelligence?. Erişim adresi: http://www-formal.stanford.edu/jmc/whatisai.html (Erişim Tarihi: 28.01.2017)
  29. Mitchell, T.M. (1997) “Bayesian Learning”, Machine Learning, Editör: Eric, M., McGraw Hill Education, Boston, Massachusetts, USA, 154-200.
  30. Namlı, E., Erdal, H.İ. ve Erdal, H. (2016) Dalgacık dönüşümü ile beton basınç dayanım tahmininin iyileştirilmesi, Politeknik Dergisi, 19(4), 471-480. doi: 10.2339/2016.19.4
  31. Nilsson, NJ. (1998) Introduction To Machine Learning An Early Draft of A Proposed Textbook. Erişim adresi: http://ai.stanford.edu/~nilsson/MLBOOK.pdf (Erişim Tarihi: 30.01.2017)
  32. Seyhan, A.T., Tayfur, G., Karakurt, M. ve Tanoğlu, M. (2005) Artificial neural network (ANN) prediction of compressive strength of VARTM processed polymer composites, Computational Materials Science, 34(1), 99-105. doi: 10.1016/j.commatsci.2004.11.001
  33. Rokach, L. ve Maimon, O. Data Mining and Knowledge Discovery Handbook, 2nd ed, Springer, New York, USA.
  34. Ruggieri, S. (2002) Efficient C4.5 [classification algorithm], IEEE Transactions on Knowledge and Data Engineering, 14(2), 438-444. doi: 10.1109/69.991727
  35. Russell, S.J. ve Norvig, P. (1995) Artificial Intelligence: A Modern Approach, Prentice-Hall, Egnlewood Cliffs, New Jersey, USA.
  36. Shaikh, P.H., Nor, N.B.M., Sahito, A.A., Nallagownden, P., Elamvazuthi, I. ve Shaikh, M.S (2017) Building energy for sustainable development in Malaysia: A review, Renewable and Sustainable Energy Reviews, 75, 1392-1403. doi: 10.1016/j.rser.2016.11.128
  37. T.C. Çevre ve Şehircilik Bakanlığı ve Enerji Verimliliği Daire Başkanlığı Mesleki Hizmetler Genel Müdürlüğü, Binalarda Enerji Performans Yönetmeliği ve Enerji Etkin Bina Tasarım Prensipleri. Erişim adresi: http://www.bep.gov.tr/MERKEZIISITMA/Sunumlar.aspx (Erişim Tarihi: 20.04.2017)
  38. T.C. Çevre ve Şehircilik Bakanlığı, (2010). Binalarda Enerji Performansı Yönetmeliğinde Değişiklik Dair Yönetmelik. Erişim adresi: http://www.enerjikimlikbelgesi.com/wp-content/uploads/2017/05/Binalarda_ Enerji Performansi Yonetmeligi-28-04-2017.pdf, (Erişim Tarihi: 11.05.2017)
  39. Wang, Z. ve Srinivasan, R.S. (2017) A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models, Renewable and Sustainable Energy Reviews, 75, 796-808. doi: 10.1016/j.rser.2016.10.079
  40. Xu, P., Chan, E.H.W. ve Qian, Q.K. (2011) Success factors of energy performance contracting (EPC) for sustainable building energy efficiency retrofit (BEER) of hotel buildings in China, Energy Policy, 39(11), 7389-7398. doi: 10.1016/j.enpol.2011.09.001
  41. Yıldız, Y. ve Arsan, Z.D. (2011) Identification of the building parameters that influence heating and cooling energy loads for apartment buildings in hot-humid climates, Energy, 36(7), 4287-4296. doi: 10.1016/j.energy.2011.04.013
  42. Yiğit, K. ve Acarkan, B. (2016) Assessment of energy performance certificate systems: a case study for residential buildings in Turkey, Turkish Journal of Electrical Engineering & Computer Sciences, 24(6), 4839-4848. doi: 10.3906/elk-1407-87