Development a Hybrid Computational Model Based on Artificial Neural Network to Predict Natural Gas Global Demand

[1] Hafezi, Reza, et al. "A Layered Uncertainties Scenario Synthesizing (LUSS) Model Applied to Evaluate Multiple Potential Long-Run Outcomes for Iran’s Natural Gas Exports." Energy , 169: pp. 646-659, 2018. [2] Erdogdu, E., "Natural Gas Demand in Turkey". Applied Energy, 87(1): pp. 211-219, 2010. [3] Shaffer, B., "Natural Gas Supply Stability and Foreign Policy". Energy policy, 56: pp. 114-125, 2013. [4] Smith, W.J., "Projecting EU Demand for Natural Gas to 2030: A Meta-Analysis". Energy policy, 58: pp. 163-176, 2013. [5] Heidari, H., S.T. Katircioglu, and L. Saeidpour, "Natural Gas Consumption and Economic Growth: Are We Ready to Natural Gas Price Liberalization in Iran?" Energy Policy, 63: pp. 638-645, 2013. [6] Esen, V. and B. Oral, "Natural Gas Reserve/Production Ratio in Russia, Iran, Qatar and Turkmenistan: A Political and Economic Perspective". Energy Policy, 93: pp. 101-109, 2016. [7] Matsumoto, K.i. and V. Voudouris", Potential Impact of Unconventional Oil Resources on Major Oil-Producing Countries: Scenario Analysis with the ACEGES Model". Natural Resources Research, 24(1): pp. 107-119, 2015. [8] Alipour, Mohammad, et al. "Long-Term Policy Evaluation: Application of a New Robust Decision Framework for Iran’s Energy Exports Security." Energy , 157: pp. 914-931, 2018. [9] Alipour, M., et al. "A New Hybrid Decision Framework for Prioritizing Funding Allocation to Iran's Energy Sector." Energy 121: 388-402, 2017. [10] Alipour, M., et al., "A New Hybrid Fuzzy Cognitive Map-Based Scenario Planning Approach for Iran's Oil Production Pathways in the Post–Sanction Period". Energy,135, pp. 851-864, 2017. [11] Hafezi, R., A. Akhavan, and S. Pakseresht, "Projecting Plausible Futures for Iranian Oil and Gas Industries: Analyzing of Historical Strategies". Journal of Natural Gas Science and Engineering, 39: pp. 15-27, 2017. [12] Panapakidis, I.P. and A.S. Dagoumas, "Day-Ahead Natural Gas Demand Forecasting Based on the Combination of Wavelet Transform and ANFIS/Genetic Algorithm/Neural Network Model". Energy, 118: pp. 231-245, 2017. [13] Wadud, Z., et al., "Modeling and Forecasting Natural Gas Demand in Bangladesh". Energy Policy, 39(11): pp. 7372-7380, 2011. [14] Izadyar, N., et al., "Intelligent Forecasting of Residential Heating Demand for the District Heating System based on the Monthly Overall Natural Gas Consumption". Energy and Buildings, 104: pp. 208-214, 2015. [15] Hafezi, R, et al., "A Bat-Neural Network Multi-Agent System (BNNMAS) for Stock Price Prediction: Case Study of DAX Stock Price." Applied Soft Computing 29: pp. 196-210, 2015. [16] Soldo, B., et al., "Improving the Residential Natural Gas Consumption Forecasting Models by Using Solar Radiation". Energy and buildings, 69: pp. 498-506, 2014. [17] Szoplik, J., "Forecasting of Natural Gas Consumption With Artificial Neural Networks". Energy, 85: p. 208-220, 2015. [18] Askari, S., N. Montazerin, and M.F. Zarandi, "Forecasting Semi-Dynamic Response of Natural Gas Networks to Nodal Gas Consumptions Using Genetic Fuzzy Systems". Energy, 83: pp. 252-266, 2015. [19] Ervural, B.C., O.F. Beyca, and S. Zaim, "Model Estimation of ARMA Using Genetic Algorithms: A Case Study of Forecasting Natural Gas Consumption". Procedia-Social and Behavioral Sciences, 235: pp. 537-545, 2016. [20] Fagiani, M., et al., "A Review of Datasets and Load Forecasting Techniques for Smart Natural Gas and Water Grids: Analysis and Experiments". Neurocomputing, 170: pp. 448-465, 2015. [21] Amasyali, K. and N. El-Gohary, "Building Lighting Energy Consumption Prediction for Supporting Energy Data Analytics". Procedia Engineering, 145: pp. 511-517, 2016. [22] Paudel, S., et al., "A Relevant Data Selection Method for Energy Consumption Prediction of Low Energy Building Based on Support Vector Machine". Energy and Buildings, 138: pp. 240-256, 2017. [23] Zhu, L., et al., "Short-Term Natural Gas Demand Prediction Based on Support Vector Regression With False Neighbours Filtered". Energy, 80: pp. 428-436, 2015. [24] Ramanathan, R., "A Multi-Factor Efficiency Perspective to the Relationships Among World GDP, Energy Consumption and Carbon Dioxide Emissions". Technological Forecasting and Social Change, 73(5): pp. 483-494, 2006. [25] Arsenault, E., et al., "A Total Energy Demand Model of Québec: Forecasting Properties". Energy Economics, 17(2): pp. 163-171, 1995. [26] Tolmasquim, M.T., C. Cohen, and A.S. Szklo, "CO2 Emissions in the Brazilian Industrial Sector According to the Integrated Energy Planning Model (IEPM)". Energy Policy, 29(8): pp. 641-651, 2001. [27] Intarapravich, D., et al., 3. "Asia-Pacific Energy Supply and Demand to 2010". Energy, 21(11): pp. 1017-1039, 1996. [28] Raghuvanshi, S.P., A. Chandra, and A.K. Raghav, "Carbon Dioxide Emissions from Coal Based Power Generation in India". Energy Conversion and Management', 47(4): pp. 427-441, 2006. [29] Mackay, R. and S. Probert, "Crude Oil and Natural Gas Supplies and Demands up to the Year AD 2010 for France". Applied energy, 50(3): pp. 185-208, 1995. [30] Parikh, J., P. Purohit, and P. Maitra, "Demand Projections of Petroleum Products and Natural Gas in India". Energy, 32(10): pp. 1825-1837, 2007. [31] Nel, W.P. and C.J. Cooper, "A Critical Review of IEA's Oil Demand Forecast For China". Energy Policy, 36(3): pp. 1096-1106, 2008. [32] Zhang, M., et al., "Forecasting the Transport Energy Demand Based on PLSR Method in China". Energy, 2009. 34(9): pp. 1396-1400. [33] Dincer, I. and S. Dost, "Energy and GDP". International Journal of Energy Research, 21(2): pp. 153-167, 1997. [34] Sözen, A. and E. Arcaklioglu, "Prediction of Net Energy Consumption Based on Economic Indicators (GNP and GDP) in Turkey". Energy policy, 35(10): pp. 4981-4992, 2007. [35] Ekonomou, L., "Greek Long-Term Energy Consumption Prediction Using Artificial Neural Networks". Energy, 35(2): pp. 512-517, 2010. [36] Toksarı, M.D., "Ant Colony Optimization Approach to Estimate Energy Demand of Turkey". Energy Policy, 35(8): pp. 3984-3990, 2007. [37] Ünler, A., "Improvement of Energy Demand Forecasts Using Swarm Intelligence: The case of Turkey With Projections to 2025". Energy Policy, 36(6): pp. 1937-1944, 2008. [38] Kankal, M., et al., "Modeling and Forecasting of Turkey’s Energy Consumption Using Socio-Economic and Demographic Variables". Applied Energy, 88(5): pp. 1927-1939, 2011. [39] Iniyan, S., L. Suganthi, and A.A. Samuel, "Energy Models for Commercial Energy Prediction and Substitution of Renewable Energy Sources". Energy Policy, 34(17): pp. 2640-2653, 2006. [40] Suganthi, L. and T. Jagadeesan, "A Modified Model for Prediction of India's Future Energy Requirement". Energy & Environment, 3(4): pp. 371-386, 1992. [41] Suganthi, L. and A. Williams, "Renewable Energy in India—A Modelling Study for 2020–2021". Energy policy, 28(15): pp. 1095-1109, 2000. [42] Sözen, A., E. Arcaklioğlu, and M. Özkaymak, "Turkey’s Net Energy Consumption". Applied Energy, 81(2): pp. 209-221, 2005. [43] Gorucu, F., "Evaluation and Forecasting of Gas Consumption by Statistical Analysis". Energy Sources, 26(3): pp. 267-276, 2004. [44] Ceylan, H. and H.K. Ozturk, "Estimating Energy Demand of Turkey Based on Economic Indicators Using Genetic Algorithm Approach". Energy Conversion and Management, 45(15): pp. 2525-2537, 2004. [45] Canyurt, O.E. and H.K. Ozturk, "Application of Genetic Algorithm (GA) Technique on Demand Estimation of Fossil Fuels in Turkey". Energy Policy, 36(7): pp. 2562-2569, 2008. [46] Persaud, A.J. and U. Kumar, "An Eclectic Approach in Energy Forecasting: A Case of Natural Resources Canada's (NRCan's) Oil and Gas Outlook". Energy policy, 29(4): pp. 303-313, 2001. [47] Hall, M.A., "Correlation-Based Feature Selection for Machine Learning". 1999. [48] Rich, E. and K. Knight, "Artificial Intelligence". McGraw-Hill, 1991. [49] Freitag, D. "Greedy Attribute Selection". in Machine Learning Proceedings. pp. 28-36, 1994. [50] Holland, J.H., "Adaptation in Natural and Artificial Systems", University of Michigan Press, Ann Arbor, 1975. [51] Zhang, J., et al., "Prediction of LBB Leakage for Various Conditions by Genetic Neural Network and Genetic Algorithms". Nuclear Engineering and Design, 325: pp. 33-43, 2017. [52] Anemangely, M., A. Ramezanzadeh, and B. Tokhmechi, "Shear Wave Travel Time Estimation from Petrophysical Logs Using ANFIS-PSO Algorithm: A Case Study From Ab-Teymour Oilfield". Journal of Natural Gas Science and Engineering, 38: pp. 373-387, 2017. [53] Zendehboudi, A., X. Li, and B. Wang, "Utilization of ANN and ANFIS Models to Predict Variable Speed Scroll Compressor With Vapor Injection". International Journal of Refrigeration, 74: pp. 473-485, 2017. [54] Abdi, J., et al., "Forecasting of Short-Term Traffic-Flow Based on Improved Neurofuzzy Models Via Emotional Temporal Difference Learning Algorithm". Engineering Applications of Artificial Intelligence, 25(5): pp. 1022-1042, 2012. [55] Fath, A.H., "Application of Radial Basis Function Neural Networks in Bubble Point Oil Formation Volume Factor Prediction for Petroleum Systems". Fluid Phase Equilibria, 437: pp.14-22, 2017. [56] Mohammadi, R., S.F. Ghomi, and F. Zeinali, "A New Hybrid Evolutionary Based RBF Networks Method for Forecasting Time Series: A Case Study of Forecasting Emergency Supply Demand Time Series". Engineering Applications of Artificial Intelligence, 36: pp. 204-214, 2014. [57] Park, J. and K.-Y. Kim, "Meta-Modeling Using Generalized Regression Neural Network and Particle Swarm Optimization". Applied Soft Computing, 51: pp. 354-369, 2017. [58] Hu, R., et al., "A Short-Term Power Load Forecasting Model Based on the Generalized Regression Neural Network With Decreasing Step Fruit Fly Optimization Algorithm". Neurocomputing, 221: pp. 24-31, 2017. [59] Heidari, E., M.A. Sobati, and S. Movahedirad, "Accurate Prediction of Nanofluid Viscosity Using a Multilayer Perceptron Artificial Neural Network (MLP-ANN)". Chemometrics and Intelligent Laboratory Systems, 155: pp. 73-85, 2016. [60] Kasabov, N.K., "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", Marcel Alencar, 1996. [61] Hagan, M.T., H.B. Demuth, and M. Beale, "Neural Network Design". PWS Publishing Company, USA, Boston, 1996. [62] Jang, J.-S.R. "Fuzzy Modeling Using Generalized Neural Networks and Kalman Filter Algorithm". in AAAI. 91: pp. 762-767, 1991. [63] Jang, J.-S., "ANFIS: Adaptive-Network-Based Fuzzy Inference System". IEEE transactions on systems, man, and cybernetics, 23(3): pp. 665-685, 1993. [64] Lotfinejad, Mohammad Mehdi, et al. "A Comparative Assessment of Predicting Daily Solar Radiation Using Bat Neural Network (BNN), Generalized Regression Neural Network (GRNN), and Neuro-Fuzzy (NF) System: A Case Study." Energies 11(5): pp. 1188-1203, 2018. [65] Abraham, A., "Adaptation of Fuzzy Inference System Using Neural Learning". Fuzzy systems engineering, pp. 914-914, 2005. [66] Kamari, A., et al., "Decline Curve Based Models for Predicting Natural Gas Well Performance". Petroleum, 3(2): pp. 242-248, 2017. [67] Jang, J.-S.R., C.-T. Sun, and E. Mizutani, "Neuro-fuzzy and Soft Computing: S Computational Approach to Learning and Machine Intelligence", 1997. [68] Schalkoff, R.J., "Artificial Neural Networks", McGraw-Hill Higher Education, 1997. [69] Dua, D., K. Li, and MinruiFei, "A Fastmulti-Output RBF Neural Network Construction Method". Neurocomputing, 73: pp. 2196–2202, 2010. [70] Park, J. and I.W. Sandberg, Universal "Approximation Using Radial-Basis-Function Networks". Neural Computation, 3: pp. 246-257, 1991. [71] Specht, D.F., "A General Regression Neural Network". IEEE transactions on neural networks, 2(6): pp. 568-576, 1991. [72] Hafezi, Reza, and Amir Akhavan. "Forecasting Gold Price Changes: Application of an Equipped Artificial Neural Network." AUT Journal of Modeling and Simulation, 50)1): pp. 71-82, 2018.