An open and cost-effective bottom-up engineering model for comprehensive disaggregation of residential energy consumption in developing countries,Energy Conversion and Management

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An open and cost-effective bottom-up engineering model for comprehensive disaggregation of residential energy consumption in developing countries
Energy Conversion and Management ( IF 9.9 ) Pub Date : 2024-11-15 , DOI: 10.1016/j.enconman.2024.119216
Pedro Chévez

In the coming years, a major challenge for developing countries will be gaining a deep understanding of their residential energy consumption. This knowledge is crucial for designing targeted energy policies, as accurate insights can guide subsidy allocation, manage consumption, reduce dependence on imports, and address energy shortages. While various methods exist for disaggregating consumption in this sector, countries should prioritize those that are both straightforward for their staff to implement and affordable. This work proposes that a universal, open, and cost-effective bottom-up engineering model, based on a synthetic energy questionnaire and methods for estimating missing variables, can accurately estimate residential energy consumption for a country/region, particularly in those lacking detailed statistical data. This model was applied to an “equipment dataset” from the 2017/2018 Argentine National Household Expenditure Survey and validated on both monthly and annual basis, without the need for individual data collection. It enables the characterization of energy consumption disaggregated by province, by user income segments, by energy sources, by end uses and by month. The case study’s main findings reveal significant energy inequalities among Argentine households, with higher-income households consuming between 39.35% and 90.71% more energy than lower-income households. This work highlights the effectiveness of bottom-up sample models when paired with appropriate methods for estimating uncollected data. A key innovation lies in the model’s open nature, which was designed for universal applicability across climate variables, allowing for easy replication in other studies.

更新日期：2024-11-15

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