تعیین عوامل اقتصادی ـ اجتماعی مؤثر بر مخارج برق خانوارهای روستایی ایران با استفاده از نقشه‌های خود سازمان‌ده دسته‌ای

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار دانشکده مدیریت و حسابداری، دانشگاه آزاد اسلامی واحد قزوین

2 استادیار گروه اقتصاد دانشکده اقتصاد و علوم سیاسی دانشگاه شهید بهشتی

3 استادیار دانشکده اقتصاد دانشگاه علامه طباطبایی

چکیده

امروزه برق به عنوان یکی از زیرساخت‌های توسعه کشورها مطرح است و همواره مصرف آن در طی سال‌های اخیر با افزایش مواجه بوده است. مصرف برق در ایران همواره بالاتر از سطح استانداردهای جهانی است، علاوه بر این در سال‌های اخیر افزایش جمعیت، مهاجرت روستاییان به شهرها و ارتقا سطح زندگی آنها، توسعه شهرها و فعالیت­های صنعتی، کشاورزی و خدمات، تقاضای برق را بیش از پیش افزایش داده است. ارتقاء سطح رفاه روستاییان اگرچه برای جلوگیری از مهاجرت آنها به شهرها به عنوان یکی از مشکلات کشور کافی نیست، اما شرط لازم می باشد. بنابراین تامین برق و مدیریت مصرف آن در استمرار برق­رسانی به روستاها با توجه محدودیت طرف عرضه و شدت گرفتن آن به دلیل وقوع خشکسالی پیاپی در کشور در سال‌های اخیر از اهمیت برخوردار است. به همین دلیل، در این پژوهش به شناسایی عوامل اقتصادی- اجتماعی موثر بر توابع مخارج برق خانوارهای روستایی در فصول مختلف سال پرداخته شده و برای انجام این کار از روش نقشه‌های خود سازمانده دسته­ای استفاده شده است. نتایج بدست آمده حاکی از این است که در مجموع 2 عامل کمی و ۱۳ عامل کیفی در سه سطح تاثیر­گذاری بر مصرف برق خانگی روستاییان شناسایی شدند.

کلیدواژه‌ها

عنوان مقاله [English]

Determination of Economic and Social Factors on of Electricity Expenditure for Rural Households in Iran: Batch Self-Organizing Map Approach

نویسندگان [English]

  • Neda Bayat 1
  • Vida Varahrami 2
  • Ali Asghar Salem 3
1 Assistant Professor of Economics, Faculty of Management and Accounting Engineering, Islamic Azad University, Qazvin Branch, (Corresponding Author)
2 Assistant Professor of Economics, Faculty of Economics and Political Sciences, Shahid Beheshti University
3 Assistant Professor of Economics, Faculty of Economics, Allameh Tabataba`i University
چکیده [English]

Today, electricity is considered as one of the countries' development infrastructure, and its consumption has been increasing over the past years. Electricity consumption in Iran is always higher than global standards. In addition, in recent years, population growth, migrating villagers to cities and improving their living standards, urban development, and industrial, agricultural and service activities have further increased electricity demand. Promoting the welfare of villagers is not enough to prevent them from migrating to cities as one of the country's problems, but a condition is required. Therefore, electricity supply and management of its consumption in the continuation of electricity supply to the villages is important due to the limited supply side and intensification due to the successive drought in the country in recent years. For this reason, in this research, the identification of socioeconomic factors affecting the functions of household electricity expenditure in different seasons of the year has been investigated. To do this, self-organized maps have been used. The results show that a total of 2 quantitative factors and 13 qualitative factors were identified in three levels of influence on the household electric power consumption of the villagers.

کلیدواژه‌ها [English]

  • Rural Households
  • Electricity Consumption
  • Batch Self-Organizing Map
  • Socio - economic Factors

مراجع

- 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, May). Socio-political factors influencing household electricity consumption: A comparison between Denmark and Belgium. ECEEE Summer Study Proceedings, 3, 1313-25.
- 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.
- Bhattacharjee, S., & Reichard, G. (2011, January). Socio-economic factors affecting individual household energy consumption: A systematic review. In ASME 2011 5th International Conference on Energy Sustainability (pp. 891-901). American Society of Mechanical Engineers.
- Chen, Y. T. (2017). The Factors Affecting Electricity Consumption and the Consumption Characteristics in the Residential Sector—A Case Example of Taiwan. Sustainability, 9(8), 1484.
- 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), 1285.
- Duvall, E.M. (1988). Family development's first forty years. Family Relations, 37(2), 127-134.
- Fort, J. C., Letrémy, P., & Cottrell, M. (2002). Advantages and drawbacks of the Batch Kohonen algorithm. ESANN, 2, 223-230.
- Hamidizadeh, M., Kargar, M. & Hamidian, M. (2014). Presenting the Prediction Model of Iran’s Electricity Annual Consumption by means of Narx Neural Network and Studying Effect of Targeted Subsidies on it. Journal of Econmics and Modelling, 4(16), 89-113. (In Persian).
- Hapfelmeier, A., Hothorn, T., Ulm, K., & Strobl, C. (2014). A new variable importance measure for random forests with missing data. Statistics and Computing, 24(1), 21-34.
- Hatefi, M. M., Jalali, O. & Alaei, R. (2017). The survey of Iran’s household electricity demand function by usage of concept of integration and attention to the structural variation in period of 1355-1389. Iranian Journal of Energy, 3(1), 102-108 (In Persian).
- Heidari, H., Najjar Firoozjaee M, & Saeidpour L. ) 2011(. Investigating the Relationship Between Electricity Consumption, Electricity Price and Economic Growth in Iran. Economic Research and Policies, 19 (59), 175-20. (In Persian).
- Henderson, J. M., & Quandt, R. E. (1971). Microeconomic theory: A mathematical approach. McGraw-Hill.
- Huang, Z. (1998). Extensions to the k-means algorithm for clustering large data sets with categorical values. Data mining and knowledge discovery, 2(3), 283-304.
- 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.
- 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, Vol. 30 of Lecture Notes in Information Sciences.
- Levinson, D. J. (1978). Eras: The anatomy of the life cycle. Psychiatric Opinion. 15(9), 39-48.
- Lotfalipour, M.R., & Lotfi A. (2005). The Survay and the Estimation of Effective Factors on Household Electricity Demand in Khorasan Province. Quarterly Knowledge and Development, 15, 48-68 (In Persian).
- McLoughlin, F., Duffy, A., & Conlon, M. (2012). Characterising domestic electricity consumption patterns by dwelling and occupant socio-economic variables: An Irish case study. Energy and Buildings, 48, 240-248.
- Michael, R. T. (1972). Front matter, The Effect of Education on Efficiency in Consumption. In The Effect of Education on Efficiency in Consumption (pp. 12-0). NBER.
- Mohammadi, & T., Korooki, M. (2014). The comparison between residential demand for electricity in hot and cold provinces in country. Quarterly Energy Economics Review, 10 (40), 1-20 (In Persian).
- Nasiripour, A. &Talebzadeh, S. (2016). Electricity consumption peak efficiency, 31st International Power System Conference, Tehran-Iran (In Persian).
- Olaleye, S. O., & Akinbode, S. O. (2012). Analysis of Households’ Demand for Alternative Power Supply in Lagos State, Nigeria. Current Research Journal of Social Sciences, 4(2), 121-127.
- 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 Financial modeling, 17, 123-144 (In Persian).
- 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.
- Roque, M. (2013). Survey and Articifical Neural Network Analysis on Occupant's Household Energy Use in a High-Rise Multi-Unit Residential Building in Toronto, Canada (Doctoral dissertation, Master Thesis, Ryerson University, Canada).
- 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 University Press.
- Stöver, B. (2012). The influence of age on consumption (No. 3808). EcoMod.
- 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.
- Varahrami, V., & Movahedian, M. (2017). Estimation of Residential Electricity Demand among the Selected Counties in Tehran Province using Dynamic Panel Data Model. The Economic Research, 17 (2), 121-144 (In Persian).
- 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 (In Persian).

فایل ها

هم رسانی

ارجاع به این مقاله

آمار