Data-driven analysis for the characterization of household appliance ownership and use in Sub-Saharan Africa

To grant reliable and affordable electricity provision to non-electrified communities, proper system sizing, based on accurate demand estimation is crucial. However, the absence of historical data, and scarce, scattered, and often unreliable pre-electrification surveys, make this process particularly prone to errors. Acquiring data, especially with high quality and detail, is difficult, time-consuming and expensive. Even though, in a few site-specific cases the limited data collected has allowed researchers to develop methodologies to generate synthetic demand profiles based on variegated site-specific socio-economic information and appliance adoption patterns. However, given the lack of comprehensive datasets of such information, the use of synthetic methodologies has been circumscribed to limited regional and socio-economic scopes. This research proposes the development of a data-driven machine-learning framework for estimating appliance adoption patterns with a subset of relevant socio-economic indicators, identified throughout a comprehensive literature analysis and data collection across various sources. To successfully train the model, a novel open-access database has been created and populated with socio-economic information combined with appliance data collected from public and private sources. Finally, a structured logistic regression analysis has been performed, not only to capture the nexus of socio-economic factors with appliance adoption but also to estimate the most relevant ones. The methodology calibrated with the proposed open-access database has shown 71.7 % accuracy, which represents an important achievement in the field. The study's findings lay the foundations for simplifying the estimation of appliance adoption, which can facilitate the demand estimation for sizing rural energy systems and rural electrification approaches.