Evaluation of the most influential parameters of heat load in district heating systems

Energy and Buildings

Volumn 104, Issue , 2015, Pages 264-274

  Petković, Dalibor ^a   Protić, Milan ^a   Shamshirband, Shahaboddin ^b   Akib, Shatirah ^c   Raos, Miomir ^a   Marković, Dušan ^a  

^a UNIVERSITY OF NI   (Serbia)

^b UNIVERSITY OF MALAYA   (Malaysia)

^c Faculty of Science   (Malaysia)

Author keywords

ANFIS; District heating systems; Heat load; Variable selection

Indexed keywords

FUZZY INFERENCE; FUZZY SYSTEMS; HEATING; HEATING EQUIPMENT; SOFT COMPUTING; THERMAL LOAD;

ANFIS; ANFIS (ADAPTIVE NEURO FUZZY INFERENCE SYSTEM); DISTRICT HEATING SYSTEM; MULTI-STEP PREDICTION; OUTDOOR TEMPERATURE; SIMULATION PROCESS; SOFT COMPUTING METHODS; VARIABLE SELECTION;

DISTRICT HEATING;

EID: 84939514333     PISSN: 03787788     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.enbuild.2015.06.074     Document Type: Article

References (45)

1. H. Gadd, and S. Werner Thermal Energy Storage Systems for District Heating and Cooling in Advances in Thermal Energy Storage Systems 2015 Elsevier Sawston, Cambridge, UK 467 478
2. B. Rezaie, and M.A. Rosen District heating and cooling: review of technology and potential enhancements Appl. Energy 93 2012 2 10
3. H. Lund, S. Werner, R. Wiltshire, S. Svendsen, J.E. Thorsen, F. Hvelplund, and B.V. Mathiesen 4th Generation District Heating (4GDH) Energy 68 2014 1 11 10.1016/j.energy.2014.02.089
4. A. Dyrelund, and H. Lund Heat plan Denmark 2010: a road map for implementing the EU directive on renewable energy (Varmeplan Danmark) 2010
5. H. Lund, B. Möller, B.V. Mathiesen, and A. Dyrelund The role of district heating in future renewable energy systems Energy 35 2010 1381 1390
6. M. Münster, P.E. Morthorst, H.V. Larsen, L. Bregnbæk, J. Werling, H.H. Lindboe, and et al. The role of district heating in the future Danish energy system Energy 48 2012 47 55
7. D. Connolly, H. Lund, B.V. Mathiesen, S. Werner, B. Möller, U. Persson, and et al. Heat roadmap Europe: combining district heating with heat savings to decarbonise the EU energy system Energy Policy 65 2014 475 489
8. M. Brand, and S. Svendsen Renewable-based low-temperature district heating for existing buildings in various stages of refurbishment Energy 62 2013 311 319
9. S. Provatas, N. Lavesson, and C. Johansson An online machine learning algorithm for heat load forecasting in district heating systems Proc. 14th International Symposium on District Heating and Cooling 2014
10. P. Bacher, H. Madsen, H.A. Nielsen, and B. Perers Short-term heat load forecasting for single family houses Energy and Buildings 65 2013 101 112
11. S. Werner The Heat Load in District Heating Systems (PhD Thesis) 1984 Chalmers University of Technology, Department of Energy Conversion
12. H. Madsen, O.P. Palsson, K. Sejling, and H.T. Segaard Models and Methods for Optimization of District Heating Systems. Part I: Models and Identification Methods 1990 IMSOR, The Technical University of Denmark
13. H. Madsen, K. Sejling, H.T. Søgaard, and O.P. Palsson On flow and supply temperature control in district heating systems Heat Recovery Syst. CHP 14 6 1994 613 620
14. E. Dotzauer Simple model for prediction of loads in district-heating systems Appl. Energy Dec. 73 3e4 2002 277 284
15. H.A. Nielsen, and H. Madsen Modelling the heat consumption in district heating systems using a grey-box approach Energy Build. 38 1 2006 63 71 10.1016/j.enbuild.2005.05.002
16. T. Mestekemper Energy Demand Forecasting and Dynamic Water Temperature Management (PhD Thesis) 2011 Bielefeld University
17. S. Grosswindhager, A. Voigt, and M. Kozek Online short-term forecast of system heat load in district heating networks Proceedings of the 31st International Symposium on Forecasting 2011
18. K. Kato, M. Sakawa, K. Ishimaru, S. Ushiro, and T. Shibano Heat load prediction through recurrent neural network in district heating and cooling systems Systems, Man and Cybernetics 2008 1401 1406
19. G.H. John, R. Kohavi, and K. Pfleger Irrelevant features and the subset selection problem Machine Learning: Proceedings of the Eleventh International Conference 1994 121 129
20. S.F. Crone, and N. Kourentzes Feature selection for time series prediction - a combined filter and wrapper approach for neural networks Neurocomputing 73 10 2010 1923 1936
21. http://www.nstoplana.rs/o-nama/licna-karta?result=1/lat (in Serbian).
22. G. Castellano, and A.M. Fanelli Variable selection using neural-network models Neurocomputing 31 2000 1 13
23. F. Dieterle, S. Busche, and et al. Growing neural networks for a multivariate calibration and variable selection of time-resolved measurements Anal. Chim. Acta 490 2003 71 83
24. T. Cibas, F.F. Soulie, and et al. Variable selection with neural networks Neurocomputing 12 1996 223 248
25. F.O. Anderson, M. Aberg, and et al. Algorithmic approaches for studies of variable influence, contribution and selection in neural networks Chemom. Intell. Lab. Syst. 51 2000 61 72
26. A.S. Donald Using genetic algorithm based variable selection to improve neural network models for real-world systems Proceedings of the 2002 International Conference on Machine Learning & Applications 2002 16 19
27. K.Y. Chan, S.H. Ling, T.S. Dillon, and H.T. Nguyen Diagnosis of hypoglycemic episodes using a neural network based rule discovery system Expert Syst. Appl. 38 8 2011 9799 9808
28. C.K. Kwong, T.C. Wong, and K.Y. Chan A methodology of generating customer satisfaction models for new product development using a neuro-fuzzy approach Expert Syst. Appl. 36 8 2009 11262 11270
29. J.-S.R. Jang ANFIS: adaptive-network-based fuzzy inference systems IEEE Trans. Systems Man, and Cybernetics, vol. 23 1993 665 685
30. A. Al-Ghandoor, and M. Samhouri Electricity consumption in the industrial sector of Jordan: application of multivariate linear regression and adaptive neuro-fuzzy techniques Jordan J. Mech. Ind. Eng. 3 1 2009 69 76
31. R. Singh, A. Kianthola, and T.N. Singh Estimation of elastic constant of rocks using an ANFIS approach Appl. Soft Comput. 12 2012 40 45
32. D. Petković, M. Issa, N.D. Pavlović, N.T. Pavlović, and L. Zentner Adaptive neuro-fuzzy estimation of conductive silicone rubber mechanical properties Expert Syst. Appl. 39 2012 9477 9482 ISSN 0957-4174
33. D. Petković, and Z. Ćojbašić Adaptive neuro-fuzzy estimation of automatic nervous system parameters effect on heart rate variability Neural Comput. Appl. 2011 10.1007/s00521-011-0629-z
34. M. Hosoz, H.M. Ertunc, and H. Bulgurcu An adaptive neuro-fuzzy inference system model for predicting the performance of a refrigeration system with a cooling tower Expert Syst. Appl. 38 2011 14148 14155
35. A. Khajeh, H. Modarress, and B. Rezaee Application of adaptive neuro-fuzzy inference system for solubility prediction of carbon dioxide in polymers Expert Syst. Appl. 36 2009 5728 5732
36. R. Sivakumar, and K. Balu ANFIS based distillation column control Evolut. Comput. Optim. Tech. 2010 67 73 (IJCA Special Issue)
37. S. Kurnaz, O. Cetin, and O. Kaynak Adaptive neuro-fuzzy inference system based autonomous flight control of unmanned air vehicles Expert Syst. Appl. 37 2010 1229 1234
38. S. Ravi, M. Sudha, and P.A. Balakrishnan Design of intelligent self-tuning GA ANFIS temperature controller for plastic extrusion system Model. Simul. Eng. 2011 1 8
39. F.G. Areed, A.Y. Haikal, and R.H. Mohammed Adaptive neuro-fuzzy control of an induction motor Ain Shams Eng. J. 1 2010 71 78
40. D. Petković, M. Issa, N.D. Pavlović, L. Zentner, and Z. Ćojbašić Adaptive neuro fuzzy controller for adaptive compliant robotic gripper Expert Syst. Appl. 2012 10.1016/j.eswa.2012.05.072
41. L. Tian, and C. Collins Adaptive neuro-fuzzy control of a flexible manipulator Mechatronics 15 2005 1305 1320
42. A.A. Aldair, and W.J. Wang Design an intelligent controller for full vehicle nonlinear active suspension systems Int. J. Smart Sens. Intell. Syst. 4 2 2011 224 243
43. M.R. Dastranj, E. Ebroahimi, N. Changizi, and E. Sameni Control DC motorspeed with adaptive neuro-fuzzy control (ANFIS) Aust. J. Basic Appl. Sci. 5 10 2011 1499 1504
44. S. Javanmardi, and et al. FR trust: a fuzzy reputation-based model for trust management in semantic P2P grids Int. J. Grid Util. Comput. 6 1 2014 57 66
45. H. Omranpour, and et al. Distributed coloring of the graph edges Cybernetic Intelligent Systems, 2008. CIS 2008. 7th IEEE International Conference on. IEEE 2008