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Modeling Electricity Expenditures using BSOM based on Techno-Socio Economic: A Case Study of Urban Households of Iran’s Provinces | ||
| Iranian Economic Review | ||
| دوره 24، شماره 3، آذر 2020، صفحه 591-620 اصل مقاله (1.3 M) | ||
| شناسه دیجیتال (DOI): 10.22059/ier.2020.77639 | ||
| نویسندگان | ||
| Neda Bayat* 1؛ Ali Asghar Salem2 | ||
| 1Department of Economics, Qazvin Branch, Islamic Azad University, Qazvin, Iran. | ||
| 2Faculty of Economics, Allameh Tabataba'i University, Tehran, Iran. | ||
| چکیده | ||
| Electricity has particular importance in the national economy and provides socio-economic welfare. It is considered an essential infrastructure of the countries' development. This is why managing electricity consumption and formulating proper policies for it is very important for policy-makers. To do this successfully, it is necessary to identify energy consumption patterns and relevant influential factors. This study aims to identify the qualitative and quantitative effective factors of energy consumption using batch self-organizing maps (BSOM). Electricity consumption in the residential sector accounts for one-third of total electricity consumption. Therefore, this study evaluated the consumption of urban households in Iran’s provinces. According to the results, electricity price, household income, and NG gas piping costs, as quantitative factors, and the number of adolescents, number of rooms, employment status of the household responsible person (HRP), number of children, education level of HRP, house area, house material and use of the stationary gas cooler, as qualitative factors, are the most important factors affecting electricity consumption. Electricity price, the number of teenagers, rooms, and status of household head activity are identified as the most important quantitative and qualitative factors in all provinces of the country. | ||
| کلیدواژهها | ||
| Electricity Energy Consumption؛ Residential Sectors؛ Self-organizing Maps؛ Socioeconomic Factors؛ Categorical Data | ||
| مراجع | ||
|
Abounoori, E., & Lajevardi. H. (2016). Estimated Elasticities of Household Electricity Consumption to Changes in Weather Temperature. 31st International Power System Conference, Retrieved from http://psc-ir.com/cd/2016/include/index.html
Abounoori, E., & Rahimi Bonekaghi, M. (2007). Household Electricity Consumption Model for Proposing a Targeted Tariff. Daneshvar Raftar, 5(23), 33-52.
Ahmadi, S., Salehi, F., & Navaei, S. (2015). Relationship between Awareness to Consequences of Excessive Consumption of Electricity and Power Saving Among Married Women in Yasouj. Quarterly Journal of Social Development, 9(4), 51-66.
Baker, K. J., & Rylatt, R. M. (2008). Improving the Prediction of UK Domestic Energy-Demand Using Annual Consumption-Data. Applied Energy, 85(6), 475-482.
Barnes, R., & Gillingham, R. (1984). Demographic Effects in Demand Analysis: Estimation of the Quadratic Expenditure System Using Microdata. The Review of Economics and Statistics, 66(4), 591-601.
Bartiaux, F., & Gram-Hanssen, K. (2005). Socio-political Factors Influencing Household Electricity Consumption: A Comparison between Denmark and Belgium. ECEEE Summer Study Proceedings, 3, 1313-1325.
Bhattacharjee, S., & Reichard, G. (2011). Socio-economic Factors Affecting Individual Household Energy Consumption: A Systematic Review (891-901). ASME; 5th International Conference on Energy Sustainability, Washington, DC: American Society of Mechanical Engineers.
Bedir, M., Hasselaar, E., & Itard, L. (2013). Determinants of Electricity Consumption in Dutch Dwellings. Energy and Buildings, 58, 194-207.
Besagni, G., & Borgarello, M. (2018). The Determinants of Residential Energy Expenditure in Italy. Energy, 165, 369-386.
Boulet, R., Jouve, B., Rossi, F., & Villa, N. (2008). Batch Kernel SOM and Related Laplacian Methods for Social Network Analysis. Neurocomputing, 71(7-9), 1257-1273.
Chen, Y. T. (2017). The Factors Affecting Electricity Consumption and the Consumption Characteristics in the Residential Sector-A Case Example of Taiwan. Sustainability, Retrieved from https://www.mdpi.com/2071-1050/9/8/1484/pdf.
Ding, Y., Qu, W., Niu, S., Liang, M., Qiang, W., & Hong, Z. (2016). Factors Influencing the Spatial Difference in Household Energy Consumption in China. Sustainability, 8(12), 1-20.
Duvall, E. M. (1988). Family Development's First Forty Years. Family Relations, 37(2), 127-134.
Epstein, R. A. (2012). The Theory of Gambling and Statistical Logic. Oxford: Academic Press.
Huang, Z. (1997). Clustering Large Data Sets with Mixed Numeric and Categorical Values. Proceedings of the 1st Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) (21-34), Retrieved from https://grid.cs.gsu.edu/~wkim/index_files/papers/kprototype.pdf
Jones, R. V., Fuertes, A., & Lomas, K. J. (2015). The Socio-economic, Dwelling, and Appliance Related Factors Affecting Electricity Consumption in Domestic Buildings. Renewable and Sustainable Energy Reviews, 43, 901-917.
Kaestner, R., & Grossman, M. (2009). Effects of Weight on Children's Educational Achievement. Economics of Education Review, 28(6), 651-661.
Kavousian, A., Rajagopal, R., & Fischer, M. (2013). Determinants of Residential Electricity Consumption: Using Smart Meter Data to Examine the Effect of Climate, Building Characteristics, Appliance Stock, and Occupants' Behavior. Energy, 55, 184-194.
Kohenen, T. (1997). Self-organizing Maps, 30. Lecture Notes in Information Sciences, Retrieved from https://www.springer.com/gp/book/9783540679219
---------- (1990). The Self-organizing Map. Proceedings of the IEEE, 78(9), 1464-1480.
Levinson, D. J. (1978). Eras: The Anatomy of the Life Cycle. Psychiatric Opinion, 15(9), 10–11, 39–48.
Lotfalipour Lotfi, A. (2005). The Survey and the Estimation of Effective Factors on Household Electricity Demand in Khorasan Province. Knowledge and Development, 7(15), 47-69.
McLoughlin, F., Duffy, A., & Conlon, M. (2012). Characterizing Domestic Electricity Consumption Patterns by Dwelling and Occupant Socio-economic Variables: An Irish Case Study. Energy and Buildings, 48, 240-248.
Pollak, R. A., & Wales, T. J. (1981). Demographic Variables in Demand Analysis. Econometrica: Journal of the Econometric Society, 49(6), 1533-1551.
Rangriz, H., & Pashootanizadeh, H. (2014). Evaluation of the Effects of Targeted Subsidies on Household Subscribers Electricity Consumption in Tehran Using Genetic. Journal of Economic Modeling, 5(17), 123-144.
Ray, R. (1982). The Testing and Estimation of Complete Demand Systems on Household Budget Surveys. European Economic Review, 17(3), 349-369.
Ritonga, H. (1994). The Impact of Household Characteristics on Household Consumption Behavior: A Demand System Analysis on the Consumption Behavior of Urban Households in the Province of Central Java, Indonesia. Retrieved from https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=12311&context=rtd
Roque, M. (2013). Survey and Artificial Neural Network Analysis on Occupant's Household Energy Use in a High-Rise Multi-Unit Residential Building in Toronto, Canada (Doctoral Dissertation and Master Thesis, Ryerson University, Canada). Retrieved from file:///C:/Users/hossein%20zade/Downloads/OBJ%20Datastream.pdf
Santamouris, M., Kapsis, K., Korres, D., Livada, I., Pavlou, C., & Assimakopoulos, M. N. (2007). On the Relation between the Energy and Social Characteristics of the Residential Sector. Energy and Buildings, 39(8), 893-905.
Schultz, T. W. (1963). The Economic Value of Education. Columbia: Columbia University Press.
Sena, B., Zaki, S. A., Yakub, F., Yusoff, N. M., & Ridwan, M. K. (2018). Conceptual Framework of Modelling for Malaysian Household Electrical Energy Consumption using Artificial Neural Network based on Techno-Socio Economic Approach. International Journal of Electrical and Computer Engineering (IJECE), 8(3), 1844-1853.
Stover, B. (2012). The Influence of Age on Consumption (3808). Retrieved from file:///C:/Users/hossein%20zade/Desktop/Discussion%20Paper_Britta%20Stoever.pdf
Swan, L. G., & Ugursal, V. I. (2009). Modeling of End-use Energy Consumption in the Residential Sector: A Review of Modeling Techniques. Renewable and Sustainable Energy Reviews, 13(8), 1819-1835.
Talebzadeh, S., & NasiriPour, A. (2016). Electricity Consumption Peak Efficiency. 31st International Power System Conference, Retrieved from https://www.sid.ir/FileServer/SF/7831395H31313.pdf
Tonn, B., & Eisenberg, J. (2007). The Aging US Population and Residential Energy Demand. Energy Policy, 35(1), 743-745.
Raaij, W. F., & Verhallen, T. M. (1983). Patterns of Residential Energy Behavior. Journal of Economic Psychology, 4(1-2), 85-106.
Villmann, T. (1999). Benefits and Limits of the Self-Organizing Map and its Variants in the Area of Satellite Remote Sensoring Processing. Topology, Retrieved from https://www.researchgate.net/
Wilbanks, T., Bhatt, V., Bilello, D., Bull, S., Ekmann, J., Horak, W., & Scott, M. J. (2008). Effects of Climate Change on Energy Production and Use in the United States. US Department of Energy Publications, Retrieved from https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1005&context=usdoepub
Yegnanarayana, B. (2009). Artificial Neural Networks. New Delhi: Prentice-Hall of India.
Yohanis, Y. G. (2012). Domestic Energy Use and Householders' Energy Behavior. Energy Policy, 41, 654-665.
Zare Shahabadi, A., Hajizadeh Meymandi, M., & Lotfaliyani, A. Z. (2013). Socio-cultural Factors Affecting Energy Consumption Patterns of Households in Yazd. Quarterly Journal of Energy Policy and Planning Research, 1(3), 17-50.
Ziyaei, O., & Parsamoghadam, M. (2010). Long Term Electricity Consumption Modeling for Iran Using Cointegration. Modarres Technical and Engineering, 38, 31-39. | ||
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