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Modeling heating and cooling loads by regression-based machine learning techniques for energy-efficient building design

Year 2017, Volume: 10 Issue: 4, 443 - 449, 30.10.2017
https://doi.org/10.17671/gazibtd.310154

Abstract

Today,
information technology is used almost in every field. The energy sector is one
of these areas. As the population grew day by day, the number of buildings and
the energy demands of the buildings increased. One way to decrease energy
demand is to design efficient buildings with energy-saving features. In this study,
an analysis of a data set which has eight input values ​​(relative compactness,
surface area, wall area, roof area, overall height, orientation, glazing area and
glazing area distribution) and two output values ​​(heating load (HL), cooling
load (CL)), has been carried out using machine learning algorithms. The aim is
to create a model that predicts the heating and cooling load of the houses.
Accurate estimation of these parameters facilitates controlling of energy
consumption and also helps in selecting an energy supplier that better suits
the energy requirement, which is considered a significant problem in the energy
market. In this context, when analyzing the data set, regression algorithms
(Support Vector Machine (SVM) Regression, Linear Regression, Random Forest
Regression and Nearest Neighbor Regression) are used among machine learning
algorithms. For the two output values, the results have been calculated
experimentally for each algorithm and the results have been compared. For the
data set we analyzed according to the results, the algorithm that found the
closest result in terms of prediction success is Random Forest regression
algorithm.

References

  • Summary of analysis using the National Energy Efficiency Data-Framework (NEED), https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/437093/National_Energy_Efficiency_Data-Framework__NEED__Main_Report.pdf, 20.04.2017.
  • G. Özyurt, K. Karabalık, “Enerji verimliliği, binaların enerji performansı ve Türkiye’deki durum”, TMMOB İnşaat Mühendisleri Odası Türkiye Mühendislik Haberleri, 457(54), 32-34, 2009.
  • E. Göçmen, Ş. Özdemir, “Farklı Tip Aydınlatma Aygıtlarının Harmonik Etkilerinin Karşılaştırılması”, V. Enerji Verimliliği ve Kalitesi Sempozyumu, Kocaeli, 261-265, 2013.
  • C. Perdahçı, U. Hanlı, “Verimli Aydınlatma Yöntemleri”, 3E Elektrotech Dergisi, 323-327, 2010.
  • M. Beerepoot, M. Sunikka, “The contribution of the EC energy certificate in improving sustainability of the housing stock”, Environment and Planning B: Planning and Design, 32(1), 21-31, 2005.
  • Z. Yu, F. Haghighat, B. C.M. Fung, H. Yoshino, “A decision tree method for building energy demand modeling”, Energy and Buildings, 42(10), 1637-1646, 2010.
  • W.G. Cai, Y. Wu, Y. Zhong, H. Ren, “China building energy consumption: situation, challenges and corresponding measures”, Energy Policy, 37(6), 2054-2059, 2009.
  • National Academy of Sciences; National Academy of Engineering; National Research Council, Real Prospects for Energy Efficiency in the United States, Washington: The National Academies Press, 2010.
  • M. Castelli, L. Trujillo, L. Vanneschi, A. Popovic, “Prediction of energy performance of residential buildings: A genetic programming approach”, Energy and Buildings, 102, 67-74, 2015.
  • F. Khayatian, L. Sarto ve G. Dall'O', “Application of neural networks for evaluating energy performance certificates of residential buildings”, Energy and Buildings, 125, 45-54, 2016.
  • L. Wei, E.A. Silva, R. Choudhary, Q. Meng, S. Yang, “Comparative study on machine learning for urban building energy analysis”, Procedia Engineering, 121, 285-292, 2015.
  • W. Tian, “A review of sensitivity analysis methods in building energy analysis”, Renewable and Sustainable Energy Reviews, 20, 411-419, 2013.
  • B. Dong, C. Cao, S. E. Lee., “Applying support vector machines to predict building energy consumption in tropical region”, Energy and Buildings, 37(5), 545-553, 2005.
  • P. Wilde, W. Tian, “Predicting the performance of an office under climate change: A study of metrics, sensitivity and zonal resolution” Energy and Buildings, 42(10), 1674-1684, 2010.
  • W. Tian, P. Wilde, “Uncertainty and sensitivity analysis of building performance using probabilistic climate projections: A UK case study”, Automation in construction, 20(8), 1096-1109, 2011.
  • B. Eisenhower, Z. O'Neill, V.A. Fonoberov, I. Mezić, “Uncertainty and sensitivity decomposition of building energy models”, Journal of Building Performance Simulation, 5(3), 171-184, 2012.
  • Open Data Lombardia, https://www.dati.lombardia.it/, 24.04.2017.
  • D. Zhai, Y. C. Soh, W. Cai, “Operating Points as Communication Bridge between Energy Evaluation with Air Temperature and Velocity based on Extreme Learning Machine (ELM) Models”, IEEE 11th Conference on Industrial Electronics and Applications, Hefei, CHINA, 712-716, 2016.
  • E. Blomqvist, P. Thollander, “An integrated dataset of energy efficiency measures published as linked open data”, Energy Efficiency, 8(6), 1125–1147, 2015.
  • A. Tsanas, A. Xifara, “Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools”, Energy and Buildings, 49, 560-567, 2012.
  • A. Xifara, “Energy efficiency Data Set”, https://archive.ics.uci.edu/ml/datasets/Energy+efficiency, 19.04.2017.
  • Y. Arima, R. Ooka, H. Kikumoto, “Proposal of typical and design weather year for building energy simulation”, Energy and Buildings, 139, 517-524, 2017.
  • J. Lei, K. Kumarasamy, K. T. Zingre, J. Yang, M. P. Wan, E.-H. Yang, “Cool colored coating and phase change materials as complementary cooling strategies for building cooling load reduction in tropics”, Applied Energy, 190, 57-63, 2017.
  • Support Vector Machine - Regression (SVR), http://www.saedsayad.com/support_vector_machine_reg.htm. 19.04.2017.
  • N. Demir, “Lineer Regresyon’a Giriş”, http://www.necatidemir.com.tr/2015/09/lineer-regresyona-giris-bolum-1/, 19.04.2017.
  • L. Breiman, “Random Forests”, https://www.stat.berkeley.edu/~breiman/randomforest2001.pdf. 19.04.2017
  • M. Akman, Y. Genç, H. Ankaralı, “Random Forests Methods and an Application in Health Science”, Turkiye Klinikleri Journal of Biostatistics, 3(1), 36-48, 2011.
  • H. Sökün, H. Kalkan, B. Cetişli, “Classification of physical activities using accelerometer signals”, 20th Signal Processing and Communications Applications Conference, Mugla, Turkey, 2012.

Enerji Tasarruflu Bina Tasarımı İçin Isıtma ve Soğutma Yüklerini Regresyon Tabanlı Makine Öğrenmesi Algoritmaları İle Modelleme

Year 2017, Volume: 10 Issue: 4, 443 - 449, 30.10.2017
https://doi.org/10.17671/gazibtd.310154

Abstract

Günümüzde
bilişim teknolojileri hemen hemen her alanda kullanılmaktadır. Enerji sektörü
de bu alanlardan birisidir. Nüfusun gün geçtikçe artmasıyla birlikte bina
sayısı ve binaların enerji talebi de artmıştır. Enerji talebini hafifletmenin
bir yolu enerji tasarrufu özelliklerine sahip verimli binalar tasarlamaktır. Bu
çalışmada sekiz giriş değeri (nispi yoğunluk, yüzey alanı, duvar alanı, çatı
alanı, toplam yükseklik, yönlendirme, cam alanı ve cam alanı dağılımı) ve iki
çıkış değeri (ısıtma yükü (HL), soğutma yükü (CL)) olan bir veri setinin,
makine öğrenmesi algoritmaları kullanarak analizi yapılmıştır. Amaç, konutların
ısıtma ve soğutma yükünü tahmin edebilen bir model oluşturmaktır. Bu
parametrelerin doğru bir şekilde tahmin edilmesi, enerji tüketiminin daha iyi
kontrol edilmesini kolaylaştırmakta ve ayrıca, enerji piyasasında önemli bir
sorun olarak görülen enerji ihtiyacına daha iyi uyan enerji tedarikçisinin
seçiminde yardımcı olmaktadır. Bu kapsamda, veri seti analiz edilirken makine
öğrenmesi algoritmalarından regresyon algoritmaları (Destek Vektör Makinesi (SVM)
Regresyonu, Doğrusal Regresyon, Rasgele Orman Regresyonu ve En Yakın Komşu
Regresyonu) kullanılmıştır. İki çıkış değeri için sonuçlar deneysel olarak her
algoritma için ayrı ayrı hesaplanmış ve elde edilen sonuçlar
karşılaştırılmıştır. Çıkan sonuçlara göre analiz yaptığımız veri seti için,
tahmin başarımı açısından en yakın sonucu bulan algoritma Rastgele Orman
Regresyon algoritması olmuştur.

References

  • Summary of analysis using the National Energy Efficiency Data-Framework (NEED), https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/437093/National_Energy_Efficiency_Data-Framework__NEED__Main_Report.pdf, 20.04.2017.
  • G. Özyurt, K. Karabalık, “Enerji verimliliği, binaların enerji performansı ve Türkiye’deki durum”, TMMOB İnşaat Mühendisleri Odası Türkiye Mühendislik Haberleri, 457(54), 32-34, 2009.
  • E. Göçmen, Ş. Özdemir, “Farklı Tip Aydınlatma Aygıtlarının Harmonik Etkilerinin Karşılaştırılması”, V. Enerji Verimliliği ve Kalitesi Sempozyumu, Kocaeli, 261-265, 2013.
  • C. Perdahçı, U. Hanlı, “Verimli Aydınlatma Yöntemleri”, 3E Elektrotech Dergisi, 323-327, 2010.
  • M. Beerepoot, M. Sunikka, “The contribution of the EC energy certificate in improving sustainability of the housing stock”, Environment and Planning B: Planning and Design, 32(1), 21-31, 2005.
  • Z. Yu, F. Haghighat, B. C.M. Fung, H. Yoshino, “A decision tree method for building energy demand modeling”, Energy and Buildings, 42(10), 1637-1646, 2010.
  • W.G. Cai, Y. Wu, Y. Zhong, H. Ren, “China building energy consumption: situation, challenges and corresponding measures”, Energy Policy, 37(6), 2054-2059, 2009.
  • National Academy of Sciences; National Academy of Engineering; National Research Council, Real Prospects for Energy Efficiency in the United States, Washington: The National Academies Press, 2010.
  • M. Castelli, L. Trujillo, L. Vanneschi, A. Popovic, “Prediction of energy performance of residential buildings: A genetic programming approach”, Energy and Buildings, 102, 67-74, 2015.
  • F. Khayatian, L. Sarto ve G. Dall'O', “Application of neural networks for evaluating energy performance certificates of residential buildings”, Energy and Buildings, 125, 45-54, 2016.
  • L. Wei, E.A. Silva, R. Choudhary, Q. Meng, S. Yang, “Comparative study on machine learning for urban building energy analysis”, Procedia Engineering, 121, 285-292, 2015.
  • W. Tian, “A review of sensitivity analysis methods in building energy analysis”, Renewable and Sustainable Energy Reviews, 20, 411-419, 2013.
  • B. Dong, C. Cao, S. E. Lee., “Applying support vector machines to predict building energy consumption in tropical region”, Energy and Buildings, 37(5), 545-553, 2005.
  • P. Wilde, W. Tian, “Predicting the performance of an office under climate change: A study of metrics, sensitivity and zonal resolution” Energy and Buildings, 42(10), 1674-1684, 2010.
  • W. Tian, P. Wilde, “Uncertainty and sensitivity analysis of building performance using probabilistic climate projections: A UK case study”, Automation in construction, 20(8), 1096-1109, 2011.
  • B. Eisenhower, Z. O'Neill, V.A. Fonoberov, I. Mezić, “Uncertainty and sensitivity decomposition of building energy models”, Journal of Building Performance Simulation, 5(3), 171-184, 2012.
  • Open Data Lombardia, https://www.dati.lombardia.it/, 24.04.2017.
  • D. Zhai, Y. C. Soh, W. Cai, “Operating Points as Communication Bridge between Energy Evaluation with Air Temperature and Velocity based on Extreme Learning Machine (ELM) Models”, IEEE 11th Conference on Industrial Electronics and Applications, Hefei, CHINA, 712-716, 2016.
  • E. Blomqvist, P. Thollander, “An integrated dataset of energy efficiency measures published as linked open data”, Energy Efficiency, 8(6), 1125–1147, 2015.
  • A. Tsanas, A. Xifara, “Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools”, Energy and Buildings, 49, 560-567, 2012.
  • A. Xifara, “Energy efficiency Data Set”, https://archive.ics.uci.edu/ml/datasets/Energy+efficiency, 19.04.2017.
  • Y. Arima, R. Ooka, H. Kikumoto, “Proposal of typical and design weather year for building energy simulation”, Energy and Buildings, 139, 517-524, 2017.
  • J. Lei, K. Kumarasamy, K. T. Zingre, J. Yang, M. P. Wan, E.-H. Yang, “Cool colored coating and phase change materials as complementary cooling strategies for building cooling load reduction in tropics”, Applied Energy, 190, 57-63, 2017.
  • Support Vector Machine - Regression (SVR), http://www.saedsayad.com/support_vector_machine_reg.htm. 19.04.2017.
  • N. Demir, “Lineer Regresyon’a Giriş”, http://www.necatidemir.com.tr/2015/09/lineer-regresyona-giris-bolum-1/, 19.04.2017.
  • L. Breiman, “Random Forests”, https://www.stat.berkeley.edu/~breiman/randomforest2001.pdf. 19.04.2017
  • M. Akman, Y. Genç, H. Ankaralı, “Random Forests Methods and an Application in Health Science”, Turkiye Klinikleri Journal of Biostatistics, 3(1), 36-48, 2011.
  • H. Sökün, H. Kalkan, B. Cetişli, “Classification of physical activities using accelerometer signals”, 20th Signal Processing and Communications Applications Conference, Mugla, Turkey, 2012.
There are 28 citations in total.

Details

Subjects Engineering
Journal Section Articles
Authors

Musa Peker

Osman Özkaraca 0000-0002-0964-8757

Betül Kesimal This is me

Publication Date October 30, 2017
Submission Date May 2, 2017
Published in Issue Year 2017 Volume: 10 Issue: 4

Cite

APA Peker, M., Özkaraca, O., & Kesimal, B. (2017). Enerji Tasarruflu Bina Tasarımı İçin Isıtma ve Soğutma Yüklerini Regresyon Tabanlı Makine Öğrenmesi Algoritmaları İle Modelleme. Bilişim Teknolojileri Dergisi, 10(4), 443-449. https://doi.org/10.17671/gazibtd.310154