Research Article
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Year 2017, Volume: 3 Issue: 4, 1358 - 1374, 21.07.2017
https://doi.org/10.18186/journal-of-thermal-engineering.330179

Abstract

References

  • [1] J.S. Hygh, J.F. DeCarolis, D.B. Hill, S.R.Ranjithan, Multivariate regression as an energy assessment tool in early building design, Building and Environment 57 (2012) 165-175.
  • [2] I. Korolija, Y. Zhang, L.M. Halburd, V.I. Harby, Regression models for predicting UK office building energy consumption from heating and cooling demand, Energy and Buildings 59 (2013) 214-227.
  • [3] T. Catalina, V.Iordache, B. Caracaleanu, Multiple regression models for fast prediction of the heating energy demand, Energy and Buildings 57 (2013) 302-312.
  • [4] M.Manfren, N.Aste, R.Moshksar, Calibration and uncertainty analysis for computer models- A meta-model based approach for integrated building energy simulation, Applied Energy 103 (2013) 627-641.
  • [5] Y.Heo, R. Choudhary, G. Augenbroe, Calibration of building energy models for retrofit analysis under uncertainty, Energy and Buildings 47 (2012) 550-560.
  • [6] A. Boyano, P. Hernandez, O.Wolf, Energy demands and potantial savings in European office buildings: Case studies based on EnergyPlus simulations, Energy and Buildings 65 (2013) 19-28.
  • [7] C.Turhan, T.Kazanasmaz, İ.Erlalelitepe Uygun, K.E.Ekmen, G.Gökçen Akkurt, Comparative study of a building energy performance software (KEP-IYTE-ESS) and ANN-based building heat load estimation, Energy and Buildings 85, 115-125.
  • [8] G. Mavromatidis, S. Acha, N.Shah, Diagnotic tools of energy performance for supermarkets using Artificial Neural Network algorithms, Energy and Buildings 62 (2013) 304-14.
  • [9] S.K. Kwok, Y. E.W.M.Lee, A study of the importance of occupancy to bulding cooling load in prediction by intelligent approach, Energy Conversion and Management 52 (2011) 2555-2564.
  • [10] Q. Li, Q.L. Meng, J.J. Cai, H. Yoshino, A. Mochida, Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks, Energy Conversion and Management 50 (2009) 90-96.
  • [11] B.B.Ekici, U.T.Aksoy. Prediction of building energy needs in early stage of design by using ANFIS, Expert Systems with Applications, 38 (2011) 5352-5358.
  • [12] K.Li, H. Su. Forecasting building energy consumption with hybrid genetic algorithm-hierarchical adaptive network-based fuzzy inference system, Energy and buildings, 42 (2010), 2070-2076.
  • [13] F. Boithias, M. El Mankibi, P. Michel, Genetic algorithms based optimization of artificial neural network architecture for buildings’ indoor discomfort and energy consumption prediction, Building Simulation 5 (2) (2012), 95-106.
  • [14] S. A. Kalogirou, Artificial Neural Networks and Genetic Algorithms in Energy Applications in Buildings, Advances in Building Energy Research 3 (1) (2009), 83-119.
  • [15] T.Kazanasmaz, Fuzzy logic model to classify effectiveness of daylighting in an office with a movable blind system, Building and Environment, 69 (2013), 22-34.
  • [16] L. Ciabottoni, M.Grisostomi,G.Ippoliti, S.Longhi, Fuzzy logic home energy consumption modelling for residential photovoltaic plant sizing in the new Italian scenario, Energy74 (2014), 359-367.
  • [17] M.Kabak, E.Köse, O.Kırılmaz, S.Burmaoğlu, A fuzzy multi-criteria decision making approach to access building energy performance, Energy and Buildings 72 (2014), 382-389.
  • [18] C.Li, G.Zhang,M.Wang,J.Yi, Data-driven modelling and optimization of thermal comfort and energy consumption using type-2 fuzzy method, Soft Computing 17 (2013), 2075-2088.
  • [19] Republic of Turkey ministry of energy and natural resources, Retrieved 05st March 2014, fromhttp://www.enerji.gov.tr/yayinlar_raporlar_EN/ETKB_2010_2014_Stratejik_Plani_EN.pdf
  • [20] T. Kazanasmaz, İ.Erlalelitepe Uygun, G.Gökçen Akkurt, C.Turhan, K.E.Ekmen, On the relation between architectural considerations and heating energy performance of Turkish residential buildings in Izmir, Energy and Buildings 72 (2014) 38-50.
  • [21] H.X.Zhao, F.Magoules, A review on the prediction of building energy consumption, Renewable and Sustainable Energy Reviews16 (3) (2012) 3586-3592.
  • [22] B.B.Ekici, U.T.Aksoy, Prediction of building energy consumption by using artificial neural networks. Advanced Engineering Software 2011; 40: 356-362.
  • [23] J.S.Chou , D.K. Bui, Modelling heating and cooling loads by artificial intelligence for energy efficient building design. Energy and Buildings 2014; 82: 437-446.
  • [24] G. Tayfur., S. Özdemir, P.V. Singh, Fuzzy logic algorithm for runoff-induced sediment transport from bare soil surfaces, Advances in Water Resources 26 (2003), 1249-1256.
  • [25] National Building Energy Performance Calculation Methodology of Turkey. (No: YİG/2010-02)), Turkish Official Journal (2010).
  • [26] S. Kajl, M.A. Roberge, L.Lamarche, P.Malinovski, Evaluation of building energy consumption based on fuzzy logic and neural network applications, Proceedings of CLIMA 2000 conference (1997) 264-274.
  • [27] DOE- 2.2 Version 47d Edition, James J. Hirsch & Associates (JJH), 2009.
  • [28] K.Li, H.Su,J.Chu, Forecasting building energy consumption using neural networks and hybrid neuro-fuzzy system: A comparative study, Energy and Buildings 43(10):2893-2899.
  • [29] M.K. Goyal, B. Bharti , J. Quilty , J. Adamowski, A. Pandey, Modeling of daily pan evaporation in sub-tropical climates using ANN, LS-SVR, Fuzzy Logic, and ANFIS, Expert Systems with Applications 41 (2014), 5267–5276.
  • [30] M. Firat , Comparison of Artificial Intelligence Techniques for river flow forecasting. Hydrol. Earth Syst 2008;12: 123-129.
  • [31] A.K. Singh, M.C. Deo, S.V. Kumar, Neural network–genetic programming for sediment transport. Proceeding of the ICE 2007; 160: 113-119.
  • [32] U.I.B. Islam, Comparison of Conventional and Modern Load Forecasting Techniques Based on Artificial Intelligence and Expert Systems. IJCSI International Journal of Computer Science Issues 2011; 8 (5): 1694-0814.
  • [33] C.W. Wang, W.K. Chau, C.T. Cheng, L. Qui, A comparison of performance several artificial intelligence methods for forecasting monthly discharge time series. Journal of Hydrology 2009; 374: 294-306.
  • [34] S. Grieu , O.Faugeroux , O.Traure, B.Claudet, J.L. Bodnar, Artificial intelligence tools and inverse methods for estimating the thermal diffusivity of building materials. Energy and Buildings 2011; 43:543-554.
  • [35] KEP-SDM. Dwelling Energy Performance-Standart Assessment Procedure, Chambers of Mechanical Engineers. Izmir,Turkey. 2008.
  • [36] W. McCulloch, P. Walter, A Logical Calculus of Ideas Immanent in Nervous Activity. Bulletin of Mathematical Biophysics 1943; 5 (4): 115–133.
  • [37] L.A. Zadeh, Fuzzy Logic = Computing with Words, IEEE TRANSACTIONS ON FUZZY SYSTEMS 4 (2), 1996, 103-111.
  • [38] T. Munakata, Fundamentals of the New Artificial Intelligence: Beyond Traditional Paradigms, Springer-Verlag, New York, USA, 1998.
  • [39] G. Tayfur, Soft computing methods in water research engineering, WIT Press, Southampton, UK, 2012.
  • [40] S. Sivanandam , S. Sumathi, S. Deepe, “Introduction of Fuzzy Logic using MATLAB ,Springer, New York, USA, 2007.
  • [41] A.D. Kulkarni, Computer Vision and Fuzzy-Neural Systems, Printice Hall, New Jersey, USA, 2001.
  • [42] K. Hirota, W. Pedrycz, Fuzzy logic neural networks: Design and computations, in: Proc. Int. Joint Conf. Neural Networks Singapore (1991) 152-157.
  • [43] H.Esen, M. Inalli, A.Sengur, M. Esen, Predicting performance of a ground-source heat pump system using fuzzy weighted pre-processing-based ANFIS, Building and Environment 43, 2008, 2178-2187.
  • [44] T. Kazanasmaz ,İ. Erlalelitepe Uygun , G. Gokcen Akkurt ,C. Turhan , K.E. Ekmen , On the relation between architectural considerations and heating energy performance of Turkish residential buildings in Izmir. Energy and Buildings 2014; 72 :38-50.
  • [45] J.Neymark , R. Judkoff , G. Knabe , H.T. Le, M. Durig , A.Glass et al. ,Applying the building energy simulation test (BESTEST) diagnostic method to verification of space conditioning equipment models used in whole-building energy simulation programs. Energy and Buildings 2012; 34, 917–931.
  • [46] G. Gokcen Akkurt, C.D. Sahin, S. Takan , Z.D. Arslan, Testing a simplified building energy simulation program via building energy simulation test (BESTTEST). CLIMAMED 7th Mediterranean Congress of Climatization October 2013: Istanbul, Turkey; 49–57.
  • [47] R.Pachedo, J.Ordonez, G.Martinez, Energy efficient design of building: A review, Renewable and Sustainable Energy Reviews 16 (6) (2012) 3559-3573.
  • [48] L.P. Wang, J. Gwilliam, P. Jones, Case study of zero energy house design in UK, Energy and Buildings 41 (2009) 1215-1222.
  • [49] C.W.Dawson, R.Wilby, An artificial neural network approach to rainfall-runoff modelling, Hydrological Sciences Journal 43 (1), 1998, 47-66.
  • [50] V.M.P. Antognetti. Neural Networks, Concepts Applications and Implementations. New Jersey, USA: Prentice Hall;1991.
  • [51] A. Kaur, A. Kaur, Comparison of Mamdani-Type and Sugeno-Type Fuzzy Inference Systems for Air Conditioning System, International Journal of Soft Computing and Engineering (IJSCE) 2 (2) (2012) 2231-2307.
  • [52] A. A. Shleeg, IM. Ellabib, Comparison of Mamdani and Sugeno Fuzzy Interference Systems for the Breast Cancer Risk, Engineering and Technology International Journal of Computer, Information, Systems and Control Engineering 7 (10) (2013) 695-699.

PERFORMANCE INDICES OF SOFT COMPUTING MODELS TO PREDICT THE HEAT LOAD OF BUILDINGS IN TERMS OF ARCHITECTURAL INDICATORS

Year 2017, Volume: 3 Issue: 4, 1358 - 1374, 21.07.2017
https://doi.org/10.18186/journal-of-thermal-engineering.330179

Abstract

This study estimates the heat load of
buildings in Izmir/Turkey by three soft computing (SC) methods; Artificial
Neural Networks (ANNs), Fuzzy Logic (FL) and Adaptive Neuro-based Fuzzy
Inference System (ANFIS) and compares their prediction indices. Obtaining
knowledge about what the heat load of buildings would be in architectural
design stage is necessary to forecast the building performance and take
precautions against any possible failure. The best accuracy and prediction
power of novel soft computing techniques would assist the practical way of this
process. For this purpose, four inputs, namely, wall overall heat transfer
coefficient, building area/ volume ratio, total external surface area and total
window area/total external surface area ratio were employed in each model of
this study. The predicted heat load is evaluated comparatively using simulation
outputs. The ANN model estimated the heat load of the case apartments with a
rate of 97.7% and the MAPE of 5.06%; while these ratios are 98.6% and 3.56% in
Mamdani fuzzy inference systems (FL); 99.0% and 2.43% in ANFIS. When these
values were compared, it was found that the ANFIS model has become the best
learning technique among the others and can be applicable in building energy
performance studies.

References

  • [1] J.S. Hygh, J.F. DeCarolis, D.B. Hill, S.R.Ranjithan, Multivariate regression as an energy assessment tool in early building design, Building and Environment 57 (2012) 165-175.
  • [2] I. Korolija, Y. Zhang, L.M. Halburd, V.I. Harby, Regression models for predicting UK office building energy consumption from heating and cooling demand, Energy and Buildings 59 (2013) 214-227.
  • [3] T. Catalina, V.Iordache, B. Caracaleanu, Multiple regression models for fast prediction of the heating energy demand, Energy and Buildings 57 (2013) 302-312.
  • [4] M.Manfren, N.Aste, R.Moshksar, Calibration and uncertainty analysis for computer models- A meta-model based approach for integrated building energy simulation, Applied Energy 103 (2013) 627-641.
  • [5] Y.Heo, R. Choudhary, G. Augenbroe, Calibration of building energy models for retrofit analysis under uncertainty, Energy and Buildings 47 (2012) 550-560.
  • [6] A. Boyano, P. Hernandez, O.Wolf, Energy demands and potantial savings in European office buildings: Case studies based on EnergyPlus simulations, Energy and Buildings 65 (2013) 19-28.
  • [7] C.Turhan, T.Kazanasmaz, İ.Erlalelitepe Uygun, K.E.Ekmen, G.Gökçen Akkurt, Comparative study of a building energy performance software (KEP-IYTE-ESS) and ANN-based building heat load estimation, Energy and Buildings 85, 115-125.
  • [8] G. Mavromatidis, S. Acha, N.Shah, Diagnotic tools of energy performance for supermarkets using Artificial Neural Network algorithms, Energy and Buildings 62 (2013) 304-14.
  • [9] S.K. Kwok, Y. E.W.M.Lee, A study of the importance of occupancy to bulding cooling load in prediction by intelligent approach, Energy Conversion and Management 52 (2011) 2555-2564.
  • [10] Q. Li, Q.L. Meng, J.J. Cai, H. Yoshino, A. Mochida, Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks, Energy Conversion and Management 50 (2009) 90-96.
  • [11] B.B.Ekici, U.T.Aksoy. Prediction of building energy needs in early stage of design by using ANFIS, Expert Systems with Applications, 38 (2011) 5352-5358.
  • [12] K.Li, H. Su. Forecasting building energy consumption with hybrid genetic algorithm-hierarchical adaptive network-based fuzzy inference system, Energy and buildings, 42 (2010), 2070-2076.
  • [13] F. Boithias, M. El Mankibi, P. Michel, Genetic algorithms based optimization of artificial neural network architecture for buildings’ indoor discomfort and energy consumption prediction, Building Simulation 5 (2) (2012), 95-106.
  • [14] S. A. Kalogirou, Artificial Neural Networks and Genetic Algorithms in Energy Applications in Buildings, Advances in Building Energy Research 3 (1) (2009), 83-119.
  • [15] T.Kazanasmaz, Fuzzy logic model to classify effectiveness of daylighting in an office with a movable blind system, Building and Environment, 69 (2013), 22-34.
  • [16] L. Ciabottoni, M.Grisostomi,G.Ippoliti, S.Longhi, Fuzzy logic home energy consumption modelling for residential photovoltaic plant sizing in the new Italian scenario, Energy74 (2014), 359-367.
  • [17] M.Kabak, E.Köse, O.Kırılmaz, S.Burmaoğlu, A fuzzy multi-criteria decision making approach to access building energy performance, Energy and Buildings 72 (2014), 382-389.
  • [18] C.Li, G.Zhang,M.Wang,J.Yi, Data-driven modelling and optimization of thermal comfort and energy consumption using type-2 fuzzy method, Soft Computing 17 (2013), 2075-2088.
  • [19] Republic of Turkey ministry of energy and natural resources, Retrieved 05st March 2014, fromhttp://www.enerji.gov.tr/yayinlar_raporlar_EN/ETKB_2010_2014_Stratejik_Plani_EN.pdf
  • [20] T. Kazanasmaz, İ.Erlalelitepe Uygun, G.Gökçen Akkurt, C.Turhan, K.E.Ekmen, On the relation between architectural considerations and heating energy performance of Turkish residential buildings in Izmir, Energy and Buildings 72 (2014) 38-50.
  • [21] H.X.Zhao, F.Magoules, A review on the prediction of building energy consumption, Renewable and Sustainable Energy Reviews16 (3) (2012) 3586-3592.
  • [22] B.B.Ekici, U.T.Aksoy, Prediction of building energy consumption by using artificial neural networks. Advanced Engineering Software 2011; 40: 356-362.
  • [23] J.S.Chou , D.K. Bui, Modelling heating and cooling loads by artificial intelligence for energy efficient building design. Energy and Buildings 2014; 82: 437-446.
  • [24] G. Tayfur., S. Özdemir, P.V. Singh, Fuzzy logic algorithm for runoff-induced sediment transport from bare soil surfaces, Advances in Water Resources 26 (2003), 1249-1256.
  • [25] National Building Energy Performance Calculation Methodology of Turkey. (No: YİG/2010-02)), Turkish Official Journal (2010).
  • [26] S. Kajl, M.A. Roberge, L.Lamarche, P.Malinovski, Evaluation of building energy consumption based on fuzzy logic and neural network applications, Proceedings of CLIMA 2000 conference (1997) 264-274.
  • [27] DOE- 2.2 Version 47d Edition, James J. Hirsch & Associates (JJH), 2009.
  • [28] K.Li, H.Su,J.Chu, Forecasting building energy consumption using neural networks and hybrid neuro-fuzzy system: A comparative study, Energy and Buildings 43(10):2893-2899.
  • [29] M.K. Goyal, B. Bharti , J. Quilty , J. Adamowski, A. Pandey, Modeling of daily pan evaporation in sub-tropical climates using ANN, LS-SVR, Fuzzy Logic, and ANFIS, Expert Systems with Applications 41 (2014), 5267–5276.
  • [30] M. Firat , Comparison of Artificial Intelligence Techniques for river flow forecasting. Hydrol. Earth Syst 2008;12: 123-129.
  • [31] A.K. Singh, M.C. Deo, S.V. Kumar, Neural network–genetic programming for sediment transport. Proceeding of the ICE 2007; 160: 113-119.
  • [32] U.I.B. Islam, Comparison of Conventional and Modern Load Forecasting Techniques Based on Artificial Intelligence and Expert Systems. IJCSI International Journal of Computer Science Issues 2011; 8 (5): 1694-0814.
  • [33] C.W. Wang, W.K. Chau, C.T. Cheng, L. Qui, A comparison of performance several artificial intelligence methods for forecasting monthly discharge time series. Journal of Hydrology 2009; 374: 294-306.
  • [34] S. Grieu , O.Faugeroux , O.Traure, B.Claudet, J.L. Bodnar, Artificial intelligence tools and inverse methods for estimating the thermal diffusivity of building materials. Energy and Buildings 2011; 43:543-554.
  • [35] KEP-SDM. Dwelling Energy Performance-Standart Assessment Procedure, Chambers of Mechanical Engineers. Izmir,Turkey. 2008.
  • [36] W. McCulloch, P. Walter, A Logical Calculus of Ideas Immanent in Nervous Activity. Bulletin of Mathematical Biophysics 1943; 5 (4): 115–133.
  • [37] L.A. Zadeh, Fuzzy Logic = Computing with Words, IEEE TRANSACTIONS ON FUZZY SYSTEMS 4 (2), 1996, 103-111.
  • [38] T. Munakata, Fundamentals of the New Artificial Intelligence: Beyond Traditional Paradigms, Springer-Verlag, New York, USA, 1998.
  • [39] G. Tayfur, Soft computing methods in water research engineering, WIT Press, Southampton, UK, 2012.
  • [40] S. Sivanandam , S. Sumathi, S. Deepe, “Introduction of Fuzzy Logic using MATLAB ,Springer, New York, USA, 2007.
  • [41] A.D. Kulkarni, Computer Vision and Fuzzy-Neural Systems, Printice Hall, New Jersey, USA, 2001.
  • [42] K. Hirota, W. Pedrycz, Fuzzy logic neural networks: Design and computations, in: Proc. Int. Joint Conf. Neural Networks Singapore (1991) 152-157.
  • [43] H.Esen, M. Inalli, A.Sengur, M. Esen, Predicting performance of a ground-source heat pump system using fuzzy weighted pre-processing-based ANFIS, Building and Environment 43, 2008, 2178-2187.
  • [44] T. Kazanasmaz ,İ. Erlalelitepe Uygun , G. Gokcen Akkurt ,C. Turhan , K.E. Ekmen , On the relation between architectural considerations and heating energy performance of Turkish residential buildings in Izmir. Energy and Buildings 2014; 72 :38-50.
  • [45] J.Neymark , R. Judkoff , G. Knabe , H.T. Le, M. Durig , A.Glass et al. ,Applying the building energy simulation test (BESTEST) diagnostic method to verification of space conditioning equipment models used in whole-building energy simulation programs. Energy and Buildings 2012; 34, 917–931.
  • [46] G. Gokcen Akkurt, C.D. Sahin, S. Takan , Z.D. Arslan, Testing a simplified building energy simulation program via building energy simulation test (BESTTEST). CLIMAMED 7th Mediterranean Congress of Climatization October 2013: Istanbul, Turkey; 49–57.
  • [47] R.Pachedo, J.Ordonez, G.Martinez, Energy efficient design of building: A review, Renewable and Sustainable Energy Reviews 16 (6) (2012) 3559-3573.
  • [48] L.P. Wang, J. Gwilliam, P. Jones, Case study of zero energy house design in UK, Energy and Buildings 41 (2009) 1215-1222.
  • [49] C.W.Dawson, R.Wilby, An artificial neural network approach to rainfall-runoff modelling, Hydrological Sciences Journal 43 (1), 1998, 47-66.
  • [50] V.M.P. Antognetti. Neural Networks, Concepts Applications and Implementations. New Jersey, USA: Prentice Hall;1991.
  • [51] A. Kaur, A. Kaur, Comparison of Mamdani-Type and Sugeno-Type Fuzzy Inference Systems for Air Conditioning System, International Journal of Soft Computing and Engineering (IJSCE) 2 (2) (2012) 2231-2307.
  • [52] A. A. Shleeg, IM. Ellabib, Comparison of Mamdani and Sugeno Fuzzy Interference Systems for the Breast Cancer Risk, Engineering and Technology International Journal of Computer, Information, Systems and Control Engineering 7 (10) (2013) 695-699.
There are 52 citations in total.

Details

Journal Section Articles
Authors

Gülden Gökçen Akkurt

Publication Date July 21, 2017
Submission Date July 21, 2017
Published in Issue Year 2017 Volume: 3 Issue: 4

Cite

APA Akkurt, G. G. (2017). PERFORMANCE INDICES OF SOFT COMPUTING MODELS TO PREDICT THE HEAT LOAD OF BUILDINGS IN TERMS OF ARCHITECTURAL INDICATORS. Journal of Thermal Engineering, 3(4), 1358-1374. https://doi.org/10.18186/journal-of-thermal-engineering.330179
AMA Akkurt GG. PERFORMANCE INDICES OF SOFT COMPUTING MODELS TO PREDICT THE HEAT LOAD OF BUILDINGS IN TERMS OF ARCHITECTURAL INDICATORS. Journal of Thermal Engineering. July 2017;3(4):1358-1374. doi:10.18186/journal-of-thermal-engineering.330179
Chicago Akkurt, Gülden Gökçen. “PERFORMANCE INDICES OF SOFT COMPUTING MODELS TO PREDICT THE HEAT LOAD OF BUILDINGS IN TERMS OF ARCHITECTURAL INDICATORS”. Journal of Thermal Engineering 3, no. 4 (July 2017): 1358-74. https://doi.org/10.18186/journal-of-thermal-engineering.330179.
EndNote Akkurt GG (July 1, 2017) PERFORMANCE INDICES OF SOFT COMPUTING MODELS TO PREDICT THE HEAT LOAD OF BUILDINGS IN TERMS OF ARCHITECTURAL INDICATORS. Journal of Thermal Engineering 3 4 1358–1374.
IEEE G. G. Akkurt, “PERFORMANCE INDICES OF SOFT COMPUTING MODELS TO PREDICT THE HEAT LOAD OF BUILDINGS IN TERMS OF ARCHITECTURAL INDICATORS”, Journal of Thermal Engineering, vol. 3, no. 4, pp. 1358–1374, 2017, doi: 10.18186/journal-of-thermal-engineering.330179.
ISNAD Akkurt, Gülden Gökçen. “PERFORMANCE INDICES OF SOFT COMPUTING MODELS TO PREDICT THE HEAT LOAD OF BUILDINGS IN TERMS OF ARCHITECTURAL INDICATORS”. Journal of Thermal Engineering 3/4 (July 2017), 1358-1374. https://doi.org/10.18186/journal-of-thermal-engineering.330179.
JAMA Akkurt GG. PERFORMANCE INDICES OF SOFT COMPUTING MODELS TO PREDICT THE HEAT LOAD OF BUILDINGS IN TERMS OF ARCHITECTURAL INDICATORS. Journal of Thermal Engineering. 2017;3:1358–1374.
MLA Akkurt, Gülden Gökçen. “PERFORMANCE INDICES OF SOFT COMPUTING MODELS TO PREDICT THE HEAT LOAD OF BUILDINGS IN TERMS OF ARCHITECTURAL INDICATORS”. Journal of Thermal Engineering, vol. 3, no. 4, 2017, pp. 1358-74, doi:10.18186/journal-of-thermal-engineering.330179.
Vancouver Akkurt GG. PERFORMANCE INDICES OF SOFT COMPUTING MODELS TO PREDICT THE HEAT LOAD OF BUILDINGS IN TERMS OF ARCHITECTURAL INDICATORS. Journal of Thermal Engineering. 2017;3(4):1358-74.

IMPORTANT NOTE: JOURNAL SUBMISSION LINK http://eds.yildiz.edu.tr/journal-of-thermal-engineering