Mathematical Modeling for Prediction of Heating and Air-Conditioning Energies of Multistory Buildings in Duhok City

  • Neven E. Zaya Department of Information Technology Management, Duhok Polytechnic University, Duhok, Kurdistan Region - Iraq
  • Lokman H. Hassan Department of Electrical and Computer Engineering, University of Duhok, Duhok, Kurdistan Region - Iraq
  • Halis Bilgil Department of Mathematics, Aksaray University Aksaray – Turkey

Abstract

Present endeavor is devoted to estimate the air-conditioning and heating energies or loads of modern buildings in Duhok City, Iraq using new mathematical models. Many parameters have been considered in current modeling, namely, area of building, number of storeys and types of the common materials of the building walls. Regression analysis is performed to formulate new mathematical linear and nonlinear models for the loads. In addition, Fuzzy logic is utilized in the third model employing Sugeno's regulation. The outcomes reveal that the reasonable matching is achieved between the proposed models and mechanical engineering analytical solutions of heating and air-conditioning standards. Consequently, high correlation coefficient as more than 85% is determined between the predicted values of the models and analytical results. The linear model shows perfect matching with the analytical outputs more than the other proposed mathematical formulations.

Author Biography

Neven E. Zaya, Department of Information Technology Management, Duhok Polytechnic University, Duhok, Kurdistan Region - Iraq

Lecturer at 

1. Department of Information Technology Management, Duhok Polytechnic University, Duhok, Kurdistan Region - Iraq

2.Department of Mathematics, Aksaray University Aksaray – Turkey.

References

1. Akaike, H., (1974), A new look at the statistical model identification. IEEE Transactions on Automatic Control 19, 716-723.
2. Al-Abadi, M.M.Y., (2011), Using information of population in estimating parameters of multiple regression models based on quantile regression with application. Iraqi Journal for Statistical Sciences 19, 233-248.
3. Al-Shallawi, S.K.H., (2004), Using Mathematical Models for System of Air-conditioning of Building in Mosul City. Mosul.
4. Al-Taee, F.A., (2009). Smoothing and prediction for time series using transformation with application, Smoothing and prediction for time series using transformation with application, Second Scientific Conference of Mathematics, Statistics and Informatics, Mosul, Iraq.
5. Burnham, K., Anderson, D., (2004), Multimodel Inference: Understanding AIC and BIC in Model Selection. Sociological Methods & Research 33, 261-304.
6. Catalina, T., Iordache, V., Caracaleanu, B., 2013. Multiple regression model for fast prediction of the heating energy demand. Energy and Buildings 57, 302-312.
7. Catalina, T., Virgone, J., Blanco, E., (2008), Development and validation of regression models to predict monthly heating demand for residential buildings. Energy and Buildings 40, 1825-1832.
8. Chaowen, H., Dong, W., (2015), Prediction on Hourly Cooling Load of Buildings Based on Neural Networks. International Journal of Smart Home 9.
9. Chatterjee, S., Hadi, A.S., (2006), Regression Analysis by Example, Fourth ed.
10. Chou, J.-S., Bui, D.-K., (2014), Modeling heating and cooling loads by artificial intelligence for energy-efficient building design. Energy and Buildings 82, 437-446.
11. Committees, A.T., Groups, T., Groups, T.R., (2009), ASHRAE Handbook-Fundamentals (SI). American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc.
12. Costa, H.R.d.N., La Neve, A., (2015), Study on application of a neuro-fuzzy models in air conditioning systems. Soft Computing 19, 929-937.
13. Dexter, A.L., Benouarets, M., (1996), A generic approach to identifying faults in HVAC plants. American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc., Atlanta, GA (United States).
14. Dong, B., Cao, C., Lee, S.E., (2005), Applying support vector machines to predict building energy consumption in tropical region. Energy and Buildings 37, 545-553.
15. Fraisse, G., Virgone, J., (1997), Thermal comfort of discontinuously occupied building using a classical and a fuzzy logic approach. Energy and Buildings 26, 303–316.
16. Freedman, D.A., (2009), Statistical Models: Theory and Practice. Cambridge University Press, New York.
17. Graybill, F.A., lyer, H.K., (1994), Regression analysis: concepts and applications. Duxbury Press, Belmont, CA.
18. Housing, M.o.C., (2012), Iraq Blog for Air-conditioning, Iraq.
19. Huang, G., Wang, S., Xu, X., (2009), A robust model predictive control strategy for improving the control performance of air-conditioning systems. Energy Convers Manage 50, 2650-2658.
20. Hui, S.C.M., (1997), A randomised approach to multiple regression analysis of buildingenergy simulation, BS 1997 – 5th Int. IBPSA Conference, Prague, Czech Republic.
21. Jain, R.K., Smith, K.M., Culligan, P.J., Taylor, J.E., (2014), Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy. Applied Energy 123, 168-178.
22. Jones, W.P., (2005), Air Conditioning Engineering. Elsevier Butterworth-Heinemann.
23. Joudi, K.A., (1996), Principles of air-conditioning and refrigeration, Second ed. University of Basrah, Basrah, Iraq.
24. Kolokotsa, D., Saridakis, G., Pouliezos, A., Stavrakakis, G.S., (2006), Design and installation of an advanced EIB™ fuzzy indoor comfort controller using Matlab™. Energy and Buildings 38, 1084-1092.
25. Korolija, I., Zhang, Y., Marjanovic-Halburd, L., Hanby, V.I.,( 2013), Regression models for predicting UK office building energy consumption from heating and cooling demands. Energy and Buildings 59, 214-227.
26. Kreider, J.F., Curtiss, P.S., A., R., (2009), Heating and Cooling of Buildings: Design for Efficiency, Second ed. CRC Press.
27. Lam, J.C., Wan, K.K.W., Liu, D., Tsang, C.L., (2010), Multiple regression models for energy use in air-conditioned office buildings in different climates. Energy Convers Manage 51, 2692-2697.
28. Li, K., Su, H., Chu, J., (2011), Forecasting building energy consumption using neural networks and hybrid neuro-fuzzy system: A comparative study. Energy and Buildings 43, 2893-2899.
29. Li, Q., Meng, Q., Cai, J., Yoshino, H., Mochida, A., (2009a), Applying support vector machine to predict hourly cooling load in the building. Applied Energy 86, 2249-2256.
30. Li, Q., Meng, Q., Cai, J., Yoshino, H., Mochida, A., (2009b), Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks. Energy Convers Manage 50, 90-96.
31. Lopez, A., Sanchez, L., Doctor, F., Hagras, H., Callaghan, V., (2004), An evolutionary algorithm for the off-line data driven generation of fuzzy controllers for intelligent buildings, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583), pp. 42-47 vol.41.
32. Ministry of Planning, R.o.I., (2013), Technical Specifications for Civil Work, Iraq.
33. Negnevitsky, M., (2005), Artificial Intelligence A Guide to Intelligent Systems, 2nd ed. Addison-Wesley.
34. Ngo, D., Dexter, A.L., (1999), A robust model-based approach to diagnosing faults in air-handling units. American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc., Atlanta, GA (US); Univ. of Oxford (GB).
35. Puchkal, V., Jurmanov, B., (2013), Stochastic Model of the Thermal Regime and Heat Consumption of Residential Buildings for Heating. World Applied Sciences Journal 23, 191-196.
36. Rezeka, S.F., Attia, A.-H., Saleh, A.M., (2015), Management of air-conditioning systems in residential buildings by using fuzzy logic. Alexandria Engineering Journal 54, 91-98.
37. Scotton, F., (2012), Modeling and Identification for HVAC Systems, KTH Electrical Engineering, Stockholm, Sweden.
38. Souza, L.d.A., Carneiro, P.L.S., Malhado, C.H.M., Silva, F.F.e., Silveira, F.G.d., (2013), Traditional and alternative nonlinear models for estimating the growth of Morada Nova sheep. Revista Brasileira de Zootecnia 42, 651-655.
39. Sugeno, M., (1985), Industrial applications of fuzzy control, First ed. Elsevier Science Ltd.
40. Wu, S., Sun, J.-Q., (2012), A physics-based linear parametric model of room temperature in office buildings. Building and Environment 50, 1-9.
41. Wu, X.T., (2012), Considerations in energy efficient design of HAVC systems. Journal of HV&AC 7.
42. Xu, J., Zhou, J.j., 2012. The intelligent control of the central air conditioning, 2012 International Conference on Computer Science and Information Processing (CSIP), pp. 691-694.
43. Yiu, J.C.-M., Wang, S., (2007), Multiple ARMAX modeling scheme for forecasting air conditioning system performance. Energy Convers Manage 48, 2276-2285.
44. Yousif, K.Y., (2009), Production of new lightweight concrete with studying of its mechanical and thermal properties. Iraqi Journal of Civil Engineering 1.
Published
2018-12-21
How to Cite
ZAYA, Neven E.; HASSAN, Lokman H.; BILGIL, Halis. Mathematical Modeling for Prediction of Heating and Air-Conditioning Energies of Multistory Buildings in Duhok City. Academic Journal of Nawroz University, [S.l.], v. 7, n. 4, p. 153-167, dec. 2018. ISSN 2520-789X. Available at: <http://journals.nawroz.edu.krd/index.php/ajnu/article/view/284>. Date accessed: 18 mar. 2019. doi: https://doi.org/10.25007/ajnu.v7n4a284.
Section
Articles