Login Register Cart 0
Generic placeholder image

Current Alternative Energy

Editor-in-Chief

ISSN (Print): 2405-4631
ISSN (Online): 2405-464X

Back
Journal Home About Journal Editorial Board Current Issue Volumes/Issues
Subscribe
Research Article

An Investigation on the Impacts of Fuel Carrier Price on the Consumer Price Inflation in Iran

Author(s): Nima Norouzi*

Volume 5, Issue 1, 2022

Published on: 27 April, 2021

Article ID: e270421193049 Pages: 10

DOI: 10.2174/2405463104666210427112311

Price: $65

Open Access Journals Promotions 2
Abstract

Introduction: Oil is one of the primary commodities of all countries globally and is, in essence, the energy base of all that we know as transportation. Therefore, price fluctuations of derivatives, especially fuel and oil derivatives, are the policymakers’ main concerns because they can cause serious problems, such as inflation in commodity prices.

Objective: The impact of fuel carriers’ prices on the consumer price index remains a subject of debate and research. This paper aims to develop a model to define the inflation regime in Iran and then investigate the impact of gasoline and diesel price on the total inflation rate.

Methods: In this study, using the central bank time series and available data on energy balance and World Bank data banks, a non-linear distributed online delay regression model is developed to analyze the relationship between fuel price and essential commodity inflation.

Results: The results show that there is an impact of gasoline prices on inflation. It does not have much effect in the long term, but diesel can somewhat influence raising prices, which can exacerbate poverty in the community that needs special attention.

Conclusion: It was also found that increase in diesel’s price is harmful to the economy because it can stimulate inflation in the long term. However, in the short term, diesel does not cause any significant inflation in the prices. While gasoline prices can have many short-term social effects, this paper suggests that the Iranian government's control of diesel fuel prices prevents long-term inflation and inflation in consumer price rate.

Keywords: Fuel prices, gasoline prices, diesel prices, commodity inflation, price growth, inflation.

[1]
Kpodar K, Abdallah C. Dynamic fuel price pass-through: Evidence from a new global retail fuel price database. Energy Econ 2017; 66: 303-12.
[http://dx.doi.org/10.1016/j.eneco.2017.06.017]
[2]
Li J, Xi C, Long H. The roles of inter-fuel substitution and inter-market contagion in driving energy prices: Evidences from China’s coal market. Energy Econ 2019; 84
[http://dx.doi.org/10.1016/j.eneco.2019.104525]
[3]
Jansen DJ, Jonker N. Fuel tourism in Dutch border regions: Are only salient price differentials relevant? Energy Econ 2018; 74: 143-53.
[http://dx.doi.org/10.1016/j.eneco.2018.05.036]
[4]
Balaguer J, Ripollés J. The dynamics pattern of price dispersion in retail fuel markets. Energy Econ 2018; 74: 546-64.
[http://dx.doi.org/10.1016/j.eneco.2018.07.004]
[5]
Ritter N, Schmidt CM, Vance C. Short-run fuel price responses: At the pump and on the road. Energy Econ 2016; 58: 67-76.
[http://dx.doi.org/10.1016/j.eneco.2016.06.013]
[6]
Rivers N, Schaufele B. Gasoline price and new vehicle fuel efficiency: Evidence from Canada. Energy Econ 2017; 68: 454-65.
[http://dx.doi.org/10.1016/j.eneco.2017.10.026]
[7]
Rodrigues N, Losekann L, Filho GS. Demand of automotive fuels in Brazil: Underlying energy demand trend and asymmetric price response. Energy Econ 2018; 74: 644-55.
[http://dx.doi.org/10.1016/j.eneco.2018.07.005]
[8]
Pereira MA, Pereira RM. On the environmental, economic and budgetary impacts of fossil fuel prices: A dynamic general equilibrium analysis of the Portuguese case. Energy Econ 2014; 42: 248-61.
[http://dx.doi.org/10.1016/j.eneco.2014.01.006]
[9]
Gal N, Milstein I, Tishler A. Fuel cost uncertainty, capacity investment and price in a competitive electricity market. Energy Econ 2017; 61: 233-40.
[http://dx.doi.org/10.1016/j.eneco.2016.11.014]
[10]
Balaguer J, Ripollés J. Asymmetric fuel price responses under heterogeneity. Energy Econ 2016; 54: 281-90.
[http://dx.doi.org/10.1016/j.eneco.2015.12.006]
[11]
Di Giacomo M, Piacenza M, Scervini F, Turati G. Should we resurrect “TIPP ‘flottante’ if oil price booms again? Specific taxes as fuel consumer price stabilizers”. Energy Econ 2015; 51: 544-52.
[http://dx.doi.org/10.1016/j.eneco.2015.08.004]
[12]
Kang W, de Gracia FP, Ratti RA. The asymmetric response of gasoline prices to oil price shocks and policy uncertainty. Energy Econ 2019; 77: 66-79.
[http://dx.doi.org/10.1016/j.eneco.2018.09.007]
[13]
Aparicio D, Bertolotto MI. Forecasting inflation with online prices. Int J Forecast 2020; 36: 232-47.
[http://dx.doi.org/10.1016/j.ijforecast.2019.04.018]
[14]
Sun Y, Zhang X, Hong Y, Wang S. Asymmetric pass-through of oil prices to gasoline prices with interval time series modelling. Energy Econ 2019; 78: 165-73.
[http://dx.doi.org/10.1016/j.eneco.2018.10.027]
[15]
Villavicencio AL, Pourroy M. Inflation target and (a)symmetries in the oil price pass-through to inflation. Energy Econ 2019; 80: 860-75.
[http://dx.doi.org/10.1016/j.eneco.2019.01.025]
[16]
Bumpass D, Douglas C, Ginn V. Testing for short and long-run asymmetric responses and structural breaks in the retail gasoline supply chain. Energy Econ 2019; 83: 311-8.
[http://dx.doi.org/10.1016/j.eneco.2019.07.021]
[17]
Ou S, Lin Z, Xu G. The retailed gasoline price in China: Time-series analysis and future trend projection. Energy 2020; 191
[http://dx.doi.org/10.1016/j.energy.2019.116544]
[18]
Baghestani H. Predicting gasoline prices using Michigan survey data. Energy Econ 2015; 50: 27-32.
[http://dx.doi.org/10.1016/j.eneco.2015.04.015]
[19]
Tule M K, Salisu A A, Chiemeke CC. Can agricultural commodity prices predict Nigeria’s inflation? J Commod Mark 2019; 16
[http://dx.doi.org/10.1016/j.jcomm.2019.02.002]
[20]
Akimaya M, Dahl C. Simulation of price controls for different grade of gasoline: The case of Indonesia. Energy Econ 2017; 68: 373-82.
[http://dx.doi.org/10.1016/j.eneco.2017.10.012]
[21]
Banzhaf HS, Kasim MT. Fuel consumption and gasoline prices: The role of assortative matching between households and automobiles. J Environ Econ Manage 2019; 95: 1-25.
[http://dx.doi.org/10.1016/j.jeem.2018.11.010]
[22]
Binder CC. Inflation expectations and the price at the pump. J Macroecon 2018; 58: 1-18.
[http://dx.doi.org/10.1016/j.jmacro.2018.08.006]
[23]
Salah A. Oil price and inflation dynamics in the Gulf Cooperation Council countries. Energy 2019; 181: 997-1011.
[http://dx.doi.org/10.1016/j.energy.2019.05.208]
[24]
Chang K, Zhang C. Asymmetric dependence structure between emissions allowances and wholesale diesel/gasoline prices in emerging China’s emissions trading scheme pilots. Energy 2018; 164: 124-36.
[http://dx.doi.org/10.1016/j.energy.2018.08.155]
[25]
Pilart IC, Correljé AF, Palacios MB. Competition, regulation, and pricing behaviour in the Spanish retail gasoline market. Energy Policy 2009; 37: 219-28.
[http://dx.doi.org/10.1016/j.enpol.2008.08.018]
[26]
Dash P P, Rohi A K, Devaguptapu A. Assessing the (de-)anchoring of ’households’ long-term inflation expectations in the US J Macroecon 2020; 63
[http://dx.doi.org/10.1016/j.jmacro.2019.103183]
[27]
Zhao LT, He LY, Cheng L, Zeng GR, Huang Z. The effect of gasoline consumption tax on consumption and carbon emissions during a period of low oil prices. J Clean Prod 2018; 171: 1429-36.
[http://dx.doi.org/10.1016/j.jclepro.2017.10.117]
[28]
Carpio LGT. The effects of oil price volatility on ethanol, gasoline, and sugar price forecasts. Energy 2019; 181: 1012-22.
[http://dx.doi.org/10.1016/j.energy.2019.05.067]
[29]
Douglas CC, Herrera AM. Dynamic pricing and asymmetries in retail gasoline markets: What can they tell us about price stickiness? Econ Lett 2014; 122: 247-52.
[http://dx.doi.org/10.1016/j.econlet.2013.11.025]
[30]
Yusoff NYM, Bekhet HA. The effect of energy subsidy removal on energy demand and potential energy savings in Malaysia. Procedia Econ Finance 2006; 35: 189-97.
[http://dx.doi.org/10.1016/S2212-5671(16)00024-1]
[31]
Nasab EH, Rezaghilizade M. Investigating the financial roots of inflation in Iran with emphasis on the budget deficit. J Econ Res 2010; 1: 43-70.
[32]
Koch S, Zima M, Andersson G. Potentials and applications of coordinated groups of thermal household appliances for power system control purposes IEEE-PES/IAS Conference on Sustainable Alternative Energy. Valencia Spain. 2009; pp. 17-8.
[33]
Hong J. The development implementation and application of demand side management and control (dsm+c) algorithm for integrating microgeneration system within built environment 2009.
[34]
Geidl M. Integrated modeling and optimization of multi-carrier energy systems. ETH Zurich 2007.
[35]
Geidl M, Koeppel G, Favre-Perrod P, Kloeckl B, Andersson G, Froehlich K. Energy hubs for the future. IEEE Power Energy Mag 2007; 5(1): 24-30.
[http://dx.doi.org/10.1109/MPAE.2007.264850]
[36]
Gellings CW, Chamberlin JH. Demand-Side Management: Concepts and Methods. Farimont Press 1993.
[37]
Guo M, Bu Y, Cheng J. Natural gas security in china: a simulation of evolutionary trajectory and obstacle degree analysis. Sustainability 2019; 11(1): 96.
[http://dx.doi.org/10.3390/su11010096]
[38]
Cabalu H. Indicators of security of natural gas supply in Asia. Energy Policy 2010; 38(1): 218-25.
[http://dx.doi.org/10.1016/j.enpol.2009.09.008]
[39]
Biresselioglu ME, Yelkenci T, Oz IO. Investigating the natural gas supply security: A new perspective. Energy 2015; 80: 168-76.
[http://dx.doi.org/10.1016/j.energy.2014.11.060]
[40]
Guo MJ, Bu Y, Chen CX. Assessment of natural gas security and its impact factors in China. Ziyuan Kexue 2018; 40(12): 2425-37.
[41]
Berk I, Ediger VS. A historical assessment of Turkey’s natural gas import vulnerability. Energy 2018; 145: 540-7.
[http://dx.doi.org/10.1016/j.energy.2018.01.022]
[42]
Wang D, Borthwick AG, He H, et al. A hybrid wavelet de-noising and Rank-Set Pair Analysis approach for forecasting hydro-meteorological time series. Environ Res 2018; 160: 269-81.
[http://dx.doi.org/10.1016/j.envres.2017.09.033] [PMID: 29032311]
[43]
Su MR, Yang ZF, Chen B. Urban ecosystem health assessment based on energy and set pair analysis-A comparative study of typical Chinese cities. Ecol Modell 2009; 220(18): 2341-8.
[http://dx.doi.org/10.1016/j.ecolmodel.2009.06.010]
[44]
Li C, Sun L, Jia J. Risk assessment of water pollution sources based on an integrated k-means clustering and set pair analysis method in the region of Shiyan China. Sci Total Environ 2016; 557-558: 307-16.
[http://dx.doi.org/10.1016/j.scitotenv.2016.03.069]
[45]
BP statistical review of world energy, 2018 Knoema.com. [Online]. Available from: https://knoema.com/infographics/usgdfhg/bp-statistical-review-of-world-energy-2018 [Accessed: 08-Oct-2021]
[46]
China NBoSotPsRo. China Statistical Yearbook, 2006–2016. Beijing, China: China Statistics Press 2017.
[47]
China NBoSotPsRo. China Statistical Yearbook, 2006–2016. Beijing, China: China Statistics Press 2017.
[48]
China NBoSotPsRo. China Urban Construction Statistical Yearbook 2006–2016. Beijing, China: China Statistics Press 2017.
[49]
Hartley P, Medlock KB III, Rosthal J. “The relationship between crude oil and natural gas prices”, Prepared in Conjunction with an Energy Study Sponsored by The James A. Baker III Institute for Public Policy and Mckinsey and Company 2007.
[50]
Erdogdu E. Natural gas demand in Turkey. Appl Energy 2009; 87(1): 211-9.
[http://dx.doi.org/10.1016/j.apenergy.2009.07.006]
[51]
Boug P. Modelling energy demand in Germany a cointegration approach. Statistics Norway, Document 2000; 2000(11): 1-18.
[52]
Techno consultant International limited, People's Republic of Bangladesh: Preparing the clean fuel sector development program, Final report 2009; 2 (Main report), Project number: 38164.
[53]
Annual report 2007-08 Bangladesh Power Development Board 2008.
[54]
Nell WP, Cooper CJ. A critical review on IEA’s oil demand forecast for China. Energy Policy 2008; 36(3): 1096-106.
[http://dx.doi.org/10.1016/j.enpol.2007.11.025]
[55]
Labanderia X, Labeaga JM, Rodriguez M. A residential energy demand system for Spain. Energy J 2006; 27(2): 87-111.
[http://dx.doi.org/10.5547/ISSN0195-6574-EJ-Vol27-No2-6]
[56]
Gujarati DN. Basic Econometrics. (4th ed.), New York, United States: McGraw-Hill 2003.
[57]
Wadud Z. Personal tradable carbon permits for road transport: Heterogeneity of demand responses and distributional analysis 2007.
[58]
Louw K, Conradie B, Howells M, Dekenah M. Determinants of electricity demand for newly electrified low-income African households. Energy Policy 2008; 36(8): 2814-20.
[http://dx.doi.org/10.1016/j.enpol.2008.02.032]
[59]
Kaboudan M A, Liu Q W. Forecasting quarterly US demand for natural gas Information Technology for Economics and Management 2(1)2004;
[60]
Annual report 2009 - ICC Bangladesh energy regulatory commission (BERC), petrobangla (Bangladesh oil, - [PDF document] Vdocumentsmx, 30-Apr-2020 [Online] Available from: https://vdocuments.mx/annual-report-2009-icc-bangladesh-energy-regulatory-commission-berc-petrobangla.html [Accessed: 08-Oct-2021]
[61]
Econstats website", [Online]. Available from: http://www.econstats.com/ [Accessed: 08-Oct-2021]
[62]
Adjaye JA. The relationship between energy consumption, energy prices and economic growth: Time series evidence from Asian developing countries. Energy Econ 2000; 22(6): 615-25.
[http://dx.doi.org/10.1016/S0140-9883(00)00050-5]
[63]
Hang L, Tu M. The impacts of energy prices on energy intensity: Evidence from China. Energy Policy 2006; 35(5): 2978-88.
[http://dx.doi.org/10.1016/j.enpol.2006.10.022]
[64]
“Energy and Climate Change,” Iea.org. [Online]. Available from: https://www.iea.org/reports/energy-and-climate-change [Accessed: 08-Oct-2021]
[65]
Ntdc.org [Online].. Available from: http://switchboard.ntdc.org/blogs/plehner/natural_gas_a_bridge_to_the_ne.html [Accessed: 08-Oct-2021]
[66]
Swan LG, Ugursal VI. Modeling of end-use energy consumption in the residential sector: A review of modeling techniques. Renew Sustain Energy Rev 2009; 13(8): 1819-35.
[http://dx.doi.org/10.1016/j.rser.2008.09.033]
[67]
Action plan for energy efficiency: Realising the potential COM(2006) 546 Brussels 2006.
[68]
Saidur R, Masjuki H, Jamaluddin M. An application of energy and energy analysis in residential sector of Malaysia. Energy Policy 2007; 35(2): 1050-63.
[http://dx.doi.org/10.1016/j.enpol.2006.02.006]
[69]
Viklund M. Energy policy options- from the perspective of public attitudes and risk perceptions. Energy Policy 2004; 32(10): 1159-71.
[http://dx.doi.org/10.1016/S0301-4215(03)00079-X]
[70]
Report on smart gas metering Brussels Tech Rep 2010.
[71]
Fagiani M, Squartini S, Gabrielli L, Pizzichini M, Spinsante S. Computational intelligence in smart water and gas grids: An up-to-date overview Neural Networks (IJCNN) 2014 International Joint Conference IEEE. 921-6.
[http://dx.doi.org/10.1109/IJCNN.2014.6889603]
[72]
Brabec M. O. Konár, M. Maly, E. Pelikán, and J. Vondrلëek, “A statistical model for natural gas standardized load profiles”. J R Stat Soc Ser C Appl Stat 2009; 58(1): 123-39.
[http://dx.doi.org/10.1111/j.1467-9876.2008.00636.x]
[73]
Brabec M. O. Konلr, M. Maly, I. Kasanick‎, and E. Pelikán, “Statistical models for disaggregation and reaggregation of natural gas consumption data”. J Appl Stat 2015; 42(5): 921-37.
[http://dx.doi.org/10.1080/02664763.2014.993365]
[74]
Yang Sl, Shen C. A review of electric load classification in smart grid environment. Renew Sustain Energy Rev 2013; 24: 103-10.
[http://dx.doi.org/10.1016/j.rser.2013.03.023]
[75]
Kim YI, Ko JM, Choi SH. “Methods for generating TLPs (typical load profiles) for smart grid-based energy programs”, Computational Intelligence Applications. Smart Grid. CIASG 2011; pp. 1-6.
[http://dx.doi.org/10.1109/CIASG.2011.5953331]
[76]
Sathiracheewin S, Surapatana V. Daily typical load clustering of residential customers The 8th Electrical Engineering/ Electronics, Computer, Telecommunications and Information Technology (ECTI) Association of Thailand - Conference,. 2011.
[http://dx.doi.org/10.1109/ECTICON.2011.5947960]
[77]
Hossain M J, Kabir A, Rahman M M, Kabir B, Islam M R. Determination of typical load profile of consumers using fuzzy c-means clustering algorithm Int J Soft Comput Eng 2011; 2231-307.
[78]
Lo K, Zakaria Z, Sohod M. Determination of consumers’ load profiles based on two-stage fuzzy c-means Proceedings of the 5th WSEAS International Conference on Power Systems and Electromagnetic Compatibility Greece. 212-7.
[79]
Viegas JL, Vieira SM, Sousa JMC. Fuzzy clustering and prediction of electricity demand based on household characteristics Proceedings of the 16th World Congress of the International Fuzzy Systems Association (IFSA) and the European Society for Fuzzy Logic and Technology (EUSFLAT).
[http://dx.doi.org/10.2991/ifsa-eusflat-15.2015.147]
[80]
Duin RPW, Pekalska E. Dissimilarity representation for pattern recognition, the: Foundations and applications. Singapore World Scientific Publishing 2005.
[81]
Smart metering information paper: gas customer behaviour trial findings report Commission for Energy Regulation (CER) Tech Rep 2011. [Online]. Available from: https://www.ucd.ie/issda/t4media/Gas%20Customer%20Behaviour%20Trial%20Findings%20Report.pdf [Accessed: 08-Oct-2021]
[82]
J. Valente de Oliveira and W. Pedrycz Eds. Advances in Fuzzy Clustering and its Applications. Nashville, TN: John Wiley & Sons 2007.
[http://dx.doi.org/10.1002/9780470061190]
[83]
Ramos S, Duarte JM, Duarte FJ, Vale Z. A data-mining-based methodology to support my electricity customers characterization. Energy Build 2015; 91: 16-25.
[http://dx.doi.org/10.1016/j.enbuild.2015.01.035]
[84]
Wijaya TK, Ganu T, Chakraborty D, Aberer K, Seetharam DP. Consumer segmentation and knowledge extraction from smart meter and survey data The Proceeding of SIAM International Conference on Data Mining (SDM14).
[http://dx.doi.org/10.1137/1.9781611973440.26]
[85]
Rousseeuw PJ. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 1987; 20: 53-65.
[http://dx.doi.org/10.1016/0377-0427(87)90125-7]
[86]
Davies DL, Bouldin DW. A cluster separation measure. IEEE Trans Pattern Anal Mach Intell 1979; PAMI-1(2): 224-7.
[http://dx.doi.org/10.1109/TPAMI.1979.4766909]
[87]
Dunn JC. Well-separated clusters and optimal fuzzy partitions. J Cybern 1974; 4(1): 95-104.
[http://dx.doi.org/10.1080/01969727408546059]
[88]
Strehl A. Relationship-based clustering and cluster ensembles for high-dimensional data mining. 2002. Available at: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.364.9376rep=rep1type=pdf
[89]
Xie XL, Beni G. A validity measure for fuzzy clustering. IEEE Trans Pattern Anal Mach Intell 1991; (8): 841-7.
[http://dx.doi.org/10.1109/34.85677]
[90]
Pal NR, Bezdek JC. On cluster validity for the fuzzy c-means model Fuzzy Systems IEEE Transactions on 1995; 3(3): 370-9.
[http://dx.doi.org/10.1109/91.413225]
[91]
Data from the Commission for Energy Regulation (CER) [Online]. Available from: http://www.ued.ie/issda [Accessed: 08-Oct-2021]
[92]
Heffernan O. The third industrial revolution: How lateral power is transforming energy the economy and the world. Nat Clim Chang 2012; 2(2): 67-8.
[http://dx.doi.org/10.1038/nclimate1391]
[93]
Wang K, Li H, Feng Y. Big data analytics for system stability evaluation strategy in the energy Internet. IEEE Trans Industr Inform 2017; 13(4): 1969-78.
[http://dx.doi.org/10.1109/TII.2017.2692775]
[94]
Zhao F, Sun B, Zhang C. Cooling heating and electrical load forecasting method for CCHP system based on multivariate phase space reconstruction and Kalman filter. Zhongguo Dianji Gongcheng Xuebao 2016; 36(2): 399-406.
[95]
Wang Y, Chen Q, Sun M, Kang C, Xia Q. An ensemble forecasting method for the aggregated load with subprofiles. IEEE Trans Smart Grid 2018; 9(4): 3906-8.
[http://dx.doi.org/10.1109/TSG.2018.2807985]
[96]
Lu Q, Cai Q, Liu S, Yang Y, Yan B, Wang Y. Short-term load forecasting based on load decomposition and numerical weather forecast IEEE Conference on Energy Internet and Energy System Integration (EI2) . 1-5.
[97]
Zhang J, Wang Y, Sun M, Zhang N, Kang C. “Constructing probabilistic load forecast from multiple point forecasts: a bootstrap based approach”, IEEE Innovative Smart Grid Technologies - Asia. ISGT Asia 2018; 184-9.
[98]
Shi J, Tan T, Guo J, Liu Y, Zhang J. Multi-Task learning based on deep architecture for various types of load forecasting in regional energy system integration. Power System Technology China 2018; 42: 698-706.
[99]
Chan SC, Tsui KM, Wu HC. Load/Price forecasting and managing demand response for smart grids: Methodologies and challenges. IEEE Signal Process Mag 2012; 29(5): 68-85.
[http://dx.doi.org/10.1109/MSP.2012.2186531]
[100]
Cheng Y, Zhai N. Electricity price peak and valley periods division based on customer response. Automation of Electric Power Systems China 2012; 36(9): 42-6.
[101]
Zhang Z, Yu D. RBF-NN based short-term load forecasting model considering comprehensive factors affecting demand response. Zhongguo Dianji Gongcheng Xuebao 2018; 38(6): 1631-8.
[102]
Jondri R, Rismala R. Prediction of multi-currency exchange rates using correlation analysis and backpropagation International Conference on ICT For Smart Society (ICISS). 63-8.
[103]
Zhang P, Zhou X, Pelliccione P, Leung H. RBF-MLMR: A multi-label metamorphic relation prediction approach using RBF neural network. IEEE Access 2017; 5: 21791-805.
[http://dx.doi.org/10.1109/ACCESS.2017.2758790]
[104]
Ye G, Li W, Wan H. Study of RBF neural network based on PSO algorithm in nonlinear system identification 8th International Conference on Intelligent Computation Technology and Automation (ICICTA). 852-5.
[105]
Wiyada B, Siraphop T. Minimizing path loss prediction error using k-means clustering and fuzzy logic. Turk J Electr Eng Comput Sci 2018; 26(4): 1989-2002.
[http://dx.doi.org/10.3906/elk-1710-104]

Rights & Permissions Print Cite
Article Metrics
38
3
Wayfinder Image
Open Access Journals Promotions
© 2024 Bentham Science Publishers | Privacy Policy