Energy Poverty Clustering by Using Power-cut Job Order Data of the Electricity Distribution Companies
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Keywords:Energy poverty, Fuel poverty, Arrears on utility bills, Power-cut job order
AbstractThe identification of the population suffering from energy poverty, which is more visible after Covid-19 pandemic following by the 2021 energy crisis, is an essential requirement for producing systematic and sustainable solutions. Although European Union approaches to the problem with a multi-indicator sets; this indicator sets have a large amount of secure and almost unreachable data, such as identity information, wage information, health information, asset information (title deed, rental income), expenditure information, debt information, credit information, bank records, etc. Experienced two long term projects between 2014 and 2016 (problem definition for energy theft and the best practices searching 13 different country examples including Brazil, Hungary, India, etc.) and 2016–2018 (energy poverty set and consumption characteristics in Turkey) over 6 million end-user consumption and payment data brings us to confirm that. The primary indicator of energy poverty is the arrears on utility bills. The arrears resulting from the affordability problem of the energy consumed trigger a power cut-off job order in the utility company. This research examines the literature and country social assistance implementation data to see how an energy poverty level can be identified using details on arrears and powercut job orders. On this subject, power-cut job orders were constituted, because of arrears on utility bills, were subjected to statistical analysis, and the compatibility of the trend data with the socio-economic development index was investigated. Cities with a less indexes have more utility bill arrears in terms of both number and volume, according to correlation-test data. Urban cities are more visible in data since the non-urbanized cities have some energy theft activities which show us no efficiency target for the consumption! Hence one of the strategical step for decreasing the non-technical losses is having more registered customer, the relationship between the growth index and the number of customers is another intriguing finding. Separating the consumption levels of arrears, it is found that 63% of total non-payment is depending on 18% of consumers. Trend analysis confirmed that every energy consumption level has the absolute and fluctuated component inside. The number of people inside the absolute poverty cluster is coherent with national and international approaches almost in the same number. The findings revealed that arrears on utility bills can be used specifically to assess the population identified with energy dependency rather than relying on evidence from a variety of sources.
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How to Cite
Emre, T., & Sozen, A. (2022). Energy Poverty Clustering by Using Power-cut Job Order Data of the Electricity Distribution Companies. International Journal of Energy Economics and Policy, 12(3), 401–409. https://doi.org/10.32479/ijeep.12961