Modeling Factors Affecting Residential Natural Gas Consumption Using Fuzzy Cognitive Map

Document Type : Research Paper


1 Ph.D. in Operations Research, Faculty of Management, University of Tehran,

2 Prof. of Industrial Management, University of Tehran, Tehran, Iran

3 Associate Prof. of Industrial Management, Shahid Beheshti University, Tehran,

4 Associate Prof. of Industrial Management, University of Tehran, Tehran, Iran


Today, energy is an important part of the economic cycle of various countries in the international relations. Also, given the volatility of oil prices in world markets and its impact on the global economy, it is expected that world's gas industry and the use of gas as an alternative energy becomes more important. Therefore, study and research on supply and demand of energy, especially natural gas, and studying factors affecting energy consumption in various sectors is very important. In this research, first factors affecting the consumption of natural gas in the residential sector is investigated and identified, by interviewing experts of gas industry and through content analysis method, and then the causal model of direct and indirect relationships between the variables and their intensity of influence through fuzzy cognitive map is determined. So, for the first time qualitative cultural and social variables along with the climate, social and economic variables is included in causal modeling.


Alberini, A., Gans, W., & Velez-Lopez, D. (2011). Residential consumption of gas and electricity in the US: The role of prices and income. Energy Economics, 33(5), 870-881.
Asgharpour, H., Behboudi, D., & Ghazvinian, M.H. (2009). Structural Failure: Case of Natural Gas Consumption and Economic Growth in Iran. Quarterly Journal of Quantitative Economic, 5 (19), 105-121. (in Persian)
Ashraqnia Jahromi, A., Ighani Yazdli, R. (2008). Modeling of natural gas and petroleum products, and the substitution of natural gas instead of petroleum products in Iran. Sharif Journal, 24(45), 65-75. (in Persian)
Assari, M.R., Assareh, E., Behrang, M.A., & Ghanbarzadeh, A. (2010). Application of Combination of Genetic Algorithm and Artificial Neural Network for Estimation of Natural Gas Consumption in Iran. Journal of Energy Conversion. 1(1), 25-31. (in Persian)
Azadeh, A., Asadzadeh, S. M., Saberi, M., Nadimi, V., Tajvidi, A., & Sheikalishahi, M. (2011). A neuro-fuzzy-stochastic frontier analysis approach for long-term natural gas consumption forecasting and behavior analysis: the cases of Bahrain, Saudi Arabia, Syria, and UAE. Applied Energy, 88(11), 3850-3859.
Babazadeh, M., Ghadimi Dizaj, Kh., & Ghorbani, V. (2014). Estimation Long Run and Short Run Natural Gas Demand in Home Consumption. Quarterly Journal of Economical Modeling, 8(1), 101-113. (in Persian)
Bakhtiari, S., & Yazdani, M. (2012). Strategic Importance of Natural Gas and Necessity of Management and Reform on its Consumption Pattern.  Quarterly Journal of Economic Strategy, 1(2), 71-92. (in Persian)
Behrang, M. A., Assareh, E., Assari, M. R., & Ghanbarzadeh, A. (2011). Total energy demand estimation in Iran using bees algorithm. Energy Sources, Part B: Economics, Planning, and Policy, 6(3), 294-303.
Brounen, D., Kok, N., & Quigley, J. M. (2012). Residential energy use and conservation: Economics and demographics. European Economic Review, 56(5), 931-945.
Chen, J., Wang, X., & Steemers, K. (2013). A statistical analysis of a residential energy consumption survey study in Hangzhou, China. Energy Build, 66, 193–202.
Emami Meibodi, A., Mohammadi, T., Soltanololamaii, S.M.H. (2010). An Estimation of Natural Gas Domestic Demand Function by Using Kalman Filter Method (A Case Study of Residential Gas Demand Function in Tehran). Quarterly Journal of Quantitative Economic, 7(3), 41-23.
(in Persian)
Fu, C., Wang, W., Tang, J. (2014). Exploring the sensitivity of residential energy consumption in China: Implications from a micro-demographic analysis. Energy Research & Social Science, 2, 1–11.
Gerogiannis, V. C., Papadopoulou, S., & Papageorgiou, E. I. (2012). Identifying factors of customer satisfaction from Smartphones: A fuzzy cognitive map approach. In International Conference on Contemporary Marketing Issues (ICCMI) (Vol. 271).
Glykas, M. (Ed.). (2010). Fuzzy Cognitive Maps: Advances in theory, methodologies, tools and applications (Vol. 247). Springer Science and Business Media.
Gonseth, C., & Vielle, M. (2012). Modeling the impacts of climate change on the energy sector: a Swiss perspective. In GTAP Events: 15th Annual Conference on Global Economic Analysis (No. EPFL-CONF-177610).
Gray, S., Gray, S., De Kok, J. L., Helfgott, A., O'Dwyer, B., Jordan, R., & Nyaki, A. (2015). Using fuzzy cognitive mapping as a participatory approach to analyze change, preferred states, and perceived resilience of social-ecological systems. Ecology and Society, 20(2).
Groumpos, P. P. (2010). Fuzzy cognitive maps: Basic theories and their application to complex systems. In Fuzzy cognitive maps (pp. 1-22). Springer Berlin Heidelberg.
Haj Mola Ali Kani, A.R., Abbaspour, M., & Abedi, Z. (2013). Estimation of Residential and Commercial Natural Gas Function in Iran: Non Linear Modeling Approach. Practical Economic, 4(12), 59-75. (in Persian)
Heinonen, J., & Junnila, S. (2014). Residential energy consumption patterns and the overall housing energy requirements of urban and rural households in Finland. Energy and buildings, 76, 295-303.
Isaac, M., & Van Vuuren, D. P. (2009). Modeling global residential sector energy demand for heating and air conditioning in the context of climate change. Energy policy, 37(2), 507-521.
Jalaee, S.A., Ghassemi, A., & Sattari, A. (2015). Simulating Consumption Function and Forecasting Iran's Consumption until1404 Horizon Using Genetic and Particle Swarm Optimization Algorithm. The Economic Research (Scientific Research Quarterly), 15(2), 27-47. (in Persian)
Karimi, T., Sadeghi Moghadam, M.R., & Rahnama Falavarjani, R. (2010). Investigating the Effect of Tempreture onNatural Gas Consumption in Iran. Quarterly Energy Economics Review, 7(24), 193-218. (in Persian)
Kazemi, H., & Shavalpour, S. (2016). Investigation of Iranian Consumption Function in Residential and Industrial and Electricity Production. 12th International Conference on Industrial Engineering (PP. 2060-2066). Tehran, Iran. (in Persian)
Keshavarz Haddad, Gh. R., & Mirbagheri Jam, M. (2009). Estimition of Residential and Commercial Demand for Natural Gas in Iran Using the Structural Time Series Model. Economic Research, 9(32), 137-160. (in Persian)
Khan, M. A. (2015). Modelling and forecasting the demand for natural gas in Pakistan. Renewable and Sustainable Energy Reviews, 49, 1145-1159.
Lin, W., Chen, B., Luo, S., & Liang, L. (2014). Factor analysis of residential energy consumption at the provincial level in China. Sustainability, 6(11), 7710-7724.
Nie, H., & Kemp, R. (2014). Index decomposition analysis of residential energy consumption in China: 2002–2010. Applied Energy, 121, 10-19.
Oliver, R., Duffy, A., Enright, B., & O'Connor, R. (2017). Forecasting peak-day consumption for year-ahead management of natural gas networks. Utilities Policy, 44, 1-11.
Papageorgiou, E. I., & Salmeron, J. L. (2014). Methods and algorithms for fuzzy cognitive map-based modeling. In Fuzzy cognitive maps for applied sciences and engineering (pp. 1-28). Springer Berlin Heidelberg.
Papageorgiou, E. I., Parsopoulos, K. E., Stylios, C. S., Groumpos, P. P., & Vrahatis, M. N. (2005). Fuzzy cognitive maps learning using particle swarm optimization. Journal of Intelligent Information Systems, 25(1), 95-121.
Poczeta, K., Kubuś, Ł., Yastrebov, A., & Papageorgiou, E. I. (2018). Application of Fuzzy Cognitive Maps with Evolutionary Learning Algorithm to Model Decision Support Systems Based on Real-Life and Historical Data. In Recent Advances in Computational Optimization (pp. 153-175). Springer, Cham.
Reyna, J. L., & Chester, M. V. (2017). Energy efficiency to reduce residential electricity and natural gas use under climate change. Nature Communications, 8. doi:10.1038/ncomms14916.
Rodriguez-Repiso, L., Setchi, R., & Salmeron, J. L. (2007). Modelling IT projects success with fuzzy cognitive maps. Expert Systems with Applications, 32(2), 543-559.
Sadeghi, S.K., & Mosavian, S.M. (2015). Statistical Analysis and Costruction of Prediction Intervals for A Hybrid Neural Network in: A Case Study of Natural Gas Consumption in the Household Sector. Journal of Economic Modeling Research, (20), 76-106. (in Persian)
Shaikh, F., & Ji, Q. (2016). Forecasting natural gas demand in China: Logistic modelling analysis. International Journal of Electrical Power and Energy Systems, 77, 25-32.
Spoladore, A., Borelli, D., Devia, F., Mora, F., & Schenone, C. (2016). Model for forecasting residential heat demand based on natural gas consumption and energy performance indicators. Applied Energy, 182, 488-499.
Stylios, C. D., & Groumpos, P. P. (1999, June). Mathematical formulation of fuzzy cognitive maps. In Proceedings of the 7th Mediterranean Conference on Control and Automation (pp. 2251-2261).
Suo, C., Yang, Y., Solvang, W. (2014). Analysis of influence factors of rural residence transformation on residential energy consumption. Modern Management, 4, 493–515.
Tso, G. K., & Guan, J. (2014). A multilevel regression approach to understand effects of environment indicators and household features on residential energy consumption. Energy, 66, 722-731.
Zhao, X., Li, N., Ma, C. (2011). Residential energy consumption in urban China: A decomposition analysis. Energy Policy, 41, 644–653.
Zhu, D., Tao, S., Wang, R., Shen, H., Huang, Y., Shen, G., Wang, B., Li, W., Zhang, Y., Chen, H., et al. (2013). Temporal and spatial trends of residential energy consumption and air pollutant emissions in China. Applied Energy, 106, 17–24.