Yıl 2017, Cilt 22, Sayı 3, Sayfalar 325 - 346 2018-01-19

Energy Performance Classes Prediction of Concrete Buildings with Artificial Intelligence Models
YAPAY ZEKÂ MODELLERİ İLE BETONARME YAPILARA AİT ENERJİ PERFORMANS SINIFLARININ TAHMİNİ

Ersin NAMLI [1] , Melda YÜCEL [2]

130 142

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.

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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Konular Mühendislik ve Temel Bilimler
Dergi Bölümü Araştırma Makaleleri
Yazarlar

Orcid: 0000-0001-5980-9152
Yazar: Ersin NAMLI
Kurum: İstanbul Üniversitesi
Ülke: Turkey


Yazar: Melda YÜCEL
Ülke: Turkey


Bibtex @araştırma makalesi { uumfd332320, journal = {Uludağ University Journal of The Faculty of Engineering}, issn = {2148-4147}, eissn = {2148-4155}, address = {Uludağ Üniversitesi}, year = {2018}, volume = {22}, pages = {325 - 346}, doi = {10.17482/uumfd.332320}, title = {YAPAY ZEKÂ MODELLERİ İLE BETONARME YAPILARA AİT ENERJİ PERFORMANS SINIFLARININ TAHMİNİ}, key = {cite}, author = {YÜCEL, Melda and NAMLI, Ersin} }
APA NAMLI, E , YÜCEL, M . (2018). YAPAY ZEKÂ MODELLERİ İLE BETONARME YAPILARA AİT ENERJİ PERFORMANS SINIFLARININ TAHMİNİ. Uludağ University Journal of The Faculty of Engineering, 22 (3), 325-346. DOI: 10.17482/uumfd.332320
MLA NAMLI, E , YÜCEL, M . "YAPAY ZEKÂ MODELLERİ İLE BETONARME YAPILARA AİT ENERJİ PERFORMANS SINIFLARININ TAHMİNİ". Uludağ University Journal of The Faculty of Engineering 22 (2018): 325-346 <http://dergipark.gov.tr/uumfd/issue/31375/332320>
Chicago NAMLI, E , YÜCEL, M . "YAPAY ZEKÂ MODELLERİ İLE BETONARME YAPILARA AİT ENERJİ PERFORMANS SINIFLARININ TAHMİNİ". Uludağ University Journal of The Faculty of Engineering 22 (2018): 325-346
RIS TY - JOUR T1 - YAPAY ZEKÂ MODELLERİ İLE BETONARME YAPILARA AİT ENERJİ PERFORMANS SINIFLARININ TAHMİNİ AU - Ersin NAMLI , Melda YÜCEL Y1 - 2018 PY - 2018 N1 - doi: 10.17482/uumfd.332320 DO - 10.17482/uumfd.332320 T2 - Uludağ University Journal of The Faculty of Engineering JF - Journal JO - JOR SP - 325 EP - 346 VL - 22 IS - 3 SN - 2148-4147-2148-4155 M3 - doi: 10.17482/uumfd.332320 UR - http://dx.doi.org/10.17482/uumfd.332320 Y2 - 2017 ER -
EndNote %0 Uludağ University Journal of The Faculty of Engineering YAPAY ZEKÂ MODELLERİ İLE BETONARME YAPILARA AİT ENERJİ PERFORMANS SINIFLARININ TAHMİNİ %A Ersin NAMLI , Melda YÜCEL %T YAPAY ZEKÂ MODELLERİ İLE BETONARME YAPILARA AİT ENERJİ PERFORMANS SINIFLARININ TAHMİNİ %D 2018 %J Uludağ University Journal of The Faculty of Engineering %P 2148-4147-2148-4155 %V 22 %N 3 %R doi: 10.17482/uumfd.332320 %U 10.17482/uumfd.332320
ISNAD NAMLI, Ersin , YÜCEL, Melda . "YAPAY ZEKÂ MODELLERİ İLE BETONARME YAPILARA AİT ENERJİ PERFORMANS SINIFLARININ TAHMİNİ". Uludağ University Journal of The Faculty of Engineering 22 / 3 (Ocak 2018): 325-346. http://dx.doi.org/10.17482/uumfd.332320