Forecasting of natural gas consumption with neural network and neuro fuzzy system

Energy Education Science and Technology Part A: Energy Science and Research

Volumn 26, Issue 2, 2011, Pages 221-238

  Kaynar, Oguz ^a   Yilmaz, Isik ^a   Demirkoparan, Ferhan ^a  

^a CUMHURIYET UNIVERSITY   (Turkey)

Author keywords

Anfis; Ann; Arima; Forecasting; Natural gas

Indexed keywords

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM; ANFIS; ANN; ARIMA; AUTO-REGRESSIVE INTEGRATED MOVING AVERAGE; FORECASTING PERFORMANCE; FUZZY INFERENCE SYSTEMS; MULTI LAYER PERCEPTRON; NATURAL GAS CONSUMPTION; NEUROFUZZY SYSTEM; PREDICTION ACCURACY; PREDICTION MODEL; SERIES ANALYSIS;

FORECASTING; FUZZY SYSTEMS; NEURAL NETWORKS; RADIAL BASIS FUNCTION NETWORKS; TIME SERIES ANALYSIS;

NATURAL GAS;

EID: 77957983577     PISSN: 1308772X     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (62)

1. Kecebas A, Alkan MA. Educational and consciousness-raising movements for renewable energy in Turkey. Energy Educ Sci Technol Part B 2009;1:157-170.
2. Darici B, Ocal FM. The structure of European financial system and financial integration. Energy Educ Sci Technol Part B 2010;2:133-145.
3. Demirbas A. Energy concept and energy education. Energy Educ Sci Technol Part B 2009;1:85-101.
4. Demirbas A. Concept of energy conversion in engineering education. Energy Educ Sci Technol Part B 2009;1:183-197.
5. Demirbas B. Biomass business and operating. Energy Educ Sci Technol Part A 2010;26:37-47.
6. Demirbas A. Social, economic, environmental and policy aspects of biofuels. Energy Educ Sci Technol Part B 2010;2:75-109.
7. Cybenko G. Approximation by superpositions of a sigmoidal function. Mathematical Control Signals Systems 1989;2:303-314.
8. Hornik K, Stinchcombe M, White H. Multilayer feedforward networks are universal approximators. Neural Networks 1989;2:359-366.
9. Hornik K. Approximation capability of multilayer feedforward networks. Neural Networks 1991;4:251-257.
10. Zhang G, Patuwo BE, Hu MY. Forecasting with artificial neural networks: the state of the art. Int J Forecast 1998;14:35-62.
11. Jang JS. ANFIS: adaptive-network-based fuzzy inference system, IEEE Transact Syst Man Cybernetics 1993;23:665-685.
12. Ying L-C, Pan M-C. Using adaptive network based fuzzy inference system to forecast regional electricity loads. Energy Convers Manage 2008;49:205-211.
13. Azadeh A, Saberi M, Gitiforouz A, Saberi Z. A hybrid simulation-adaptive network based fuzzy inference system for improvement of electricity consumption estimation. Expert Syst Applic 2009;36:11108-11117.
14. Sfetsos A. A comparison of various forecasting techniques applied to mean hourly wind Speedy time series. Renew Energy 2000;21:23-35.
15. Nayak P.C, Sudheer K. P, Ragan D.M, Ramasastri K.S. A neuro fuzzy computing technique for modeling hydrological time series. J Hydrol 2004;29:52-66.
16. Chang F-J, Chang Y-T. Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Adv Water Res 2006;29:1-10.
17. Cheng C-H, Wei L-Y, Chen Y-S. Fusion ANFIS models based on multi-stock volatility causality for TAIEX forecasting. Neurocomputing 2009; 72; 3462-3468.
18. Atsalakis GS, Valavanis KP. Forecasting stock market short-term trends using a neuro-fuzzy based methodology. Expert Sys Appl 2009;36:10696-10707.
19. Kaynar O, Tastan S, Demirkoparan F. Yapay Sinir Aglari{dotless} ile Dogal Gaz Tuketim Tahmini. 10. Ekonometri ve Istatistik Sempozyumu; Erzurum, 2009, 106 (in Turkish).
20. Ivezic D. Short-Term Natural Gas Consumption Forecast. FME Transactions 2006;34:165-169.
21. Garcia A, Mohagheg SD. Forecasting US Natural Gas Production into year 2020: A Comparative Study. Eastern Society of Petroleum Engineers Regional Conference, SPE 91413, 2004.
22. MS. A New Methodology for Analyzi{dotless}ng and Predicting US. Liquefied Natural Gas Imports Using Neural Networks. Texas A&M University; Master Thesis 2005.
23. Khotanzad A, Elragal H, Lu Tsun-Liang Combination of Artificial Neural-Network Forecasters for Prediction of Natural Gas Consumption. IEEE Transact Neural Networks 2000;11:464-473.
24. Brown RH, Matin L, Kharout P, Piessens LP. Development of artificial neural-network models to predict daily gas consumption. AGA. Forecas Rev 1996;5:1-22.
25. Viet NH, Mandzuik J. Neural and Fuzzy Neural Networks for Natural Gas Consumption Prediction. IEEE XIII Workshop on Neural Networks for Signal Processing 2003, pp. 759-768.
26. Oztemel E. Yapay Sinir Aglari. Istanbul: Papatya Yayincilik, 2003 (in Turkish).
27. Zhang G, Hu M, Patuwo BE, Indro DC. Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis. Europ J Operat Res 1999;116:16-32.
28. Fausett L. Fundamentals of Neural Networks: Architectures, Algorithms and Applications. Prentice Hall, 1994.
29. Tang Z, Fishwick PA. Feedforward neural nets as models for time series forecasting. ORSA J Computing 1993;5:374-385.
30. Park J, Sandberg IW. Approximation and radial basis function networks. Neural Comput 1993;5:305-316.
31. Broomhead DS, Lowe D. Multivariable functional interpolation and adaptive networks. Complex Systems 1988;2:321-355.
32. Moody J, Darken CJ. Fast learning in networks of locally-tunes processing units. Neural Comput 1989;1:281-294.
33. Park J, Sandberg I.W. Universal approximations using Radial-Basis-Function Network. Neural Comput 1991;3:246-257.
34. Xu K, Xie M, Tang LC, Ho SL. Application of neural networks in forecasting engine systems reliability. Applied Soft Computing 2003;2:255-268.
35. Bianchini M, Frasconi P, Gori M. Learning without local minima in radial basis function networks. IEEE Trans. Neural Networks 1995;6:749-755.
36. Chen S, Cowan CFN, Grant PM. Orthogonal least squares learning algorithm for radial basis function networks. IEEE Trans. Neural Networks 1991;2:302-309.
37. Sheta AF, Jong KD. Time-series forecasting using GA-tuned radial basis function. Inform Sci 2001;133:221-228.
38. Harpham C, Dawson CW. The effect of different basis functions on a radial basis function network for time series prediction: A comparative study. Neurocomput 2006;69;2161-2170.
39. Rivas VM, Merelo JJ, Castillo PA, Arenas MG, Castellano JG. Evolving RBF neural networks for time-series forecasting with EvRBF. Inform Sci 2004;165:207-220.
40. Yu L, Lai KK, Wang S. Multistage RBF neural network ensemble learning for exchange rates forecasting. Neurocomput 2008;71:3295-3302.
41. Foody GM. Supervised image classification by MLP and RBF neural networks with and without an exhaustively defined set of classes. Int J Remote Sensing 2004;25:3091-3104.
42. Sarimveis H, Doganis P, Alexandridis A. A classification technique based on radial basis function neural Networks. Adv Engin Software 2006;37:218-221.
43. Zhang R, Huang G, Sundararajan N, Saratchandran P. Improved GAP-RBF network for classification problems. Neurocomput 2007;70:3011-3018.
44. Haykin S. Neural Networks - A Comprehensive Foundation. Prentice Hall; 1999.
45. Jang JSR, Sun CT, Mizutani E. Neuro-Fuzzy and Soft Computing, Prentice Hall: United States of America, 1997.
46. Yurdusev MA, Firat M. Neuro-Fuzzy Inference System and Artificial Neural Networks for Municipal Water Consumption Prediction. J Hydroinformat 2009;365:225-234.
47. Mamdani EH, Assilian S. An experiment in linguistic synthesis with a fuzzy logiccontroller. Int J Man-Machine Stud 1975;7:1-13.
48. Takagi T, Sugeno M. Fuzzy identification of systems and its applications to modeling and control. IEEE Transact Syst Man Cybernetics 1985;15:116-132.
49. Frechtling D. C. Practical Tourism Forecasting. Elsevier; 1996.
50. Yumurtaci M, Kecebas A. Renewable energy and its university level education in Turkey. Energy Educ Sci Technol Part B 2011;3:143-152.
51. Ulusarslan D, Gemici Z, Teke I. Currency of district cooling systems and alternative energy sources. Energy Educ Sci Technol Part A 2009;23:31-53.
52. Kirtay E. The role of renewable energy sources in meeting Turkey's electrical energy demand. Energy Educ Sci Thecnol Part A 2009;23:15-30.
53. Kan A. General characteristics of waste management: A review. Energy Educ Sci Technol Part A 2009:23:55-69.
54. Demirbas AH. Inexpensive oil and fats feedstocks for production of biodiesel. Energy Educ Sci Technol Part A 2009;23:1-13.
55. Sahin Y. Environmental impacts of biofuels. Energy Educ Sci Technol Part A 2011;26:129-142.
56. Demirbas AH. Biofuels for future transportation necessity. Energy Educ Sci Technol Part A 2010;26:13-23.
57. Saidur R. Energy, economics and environmental analysis for chillers in office buildings. Energy Educ Sci Technol Part A 2010;25:1-16.
58. Balat H. Prospects of biofuels for a sustainable energy future: A critical assessment. Energy Educ Sci Technol Part A 2010;24:85-111.
59. Saidur R, Lai YK. Parasitic energy savings in engines using nanolubricants. Energy Educ Sci Technol Part A 2010;26:61-74.
60. Saidur R, Lai YK. Nanotechnology in vehicle's weight reduction and associated energy savings. Energy Educ Sci Technol Part A 2011;26:87-101.
61. Ertas M, Alma MH. Slow pyrolysis of chinaberry (Melia azedarach L.) seeds: Part I. The influence of pyrolysis parameters on the product yields. Energy Educ Sci Technol Part A 2011;26:143-154.
62. Altun S. Fuel properties of biodiesels produced from different feedstocks. Energy Educ Sci Technol Part A 2011;26:165-174.