ETH Grant - Forecasting rural electricity usage

1.2 billion people around the world do not have access to electricity. Almost 100% of this un-electrified population resides in developing countries, mainly in Sub-Saharan Africa and Asia. The International Energy Agency predicts that 60% ofthe additional electricity generation that will be required to provide universal electricity access will come from offgrid solutions. However, for these offgrid solutions to be economically viable, sustainable, and beneficial,their design must beoptimised to match the energy demands of the consumers. While numerous approaches have been developed to address the energy supply system modelling side of the analysis, far fewer tackle the energy demand side, which is vital for the accurate dimensioning of thesystems. Therefore, in order to facilitate decision-making for increasing electricity access, there is a need to develop a better understanding of both the current electricity demand patterns, as well as the potential for future electricity demand growth,in rural areas of developing countries.The aim of this project is to develop a rural demand-modelling framework that can be employed to support efforts in analysing and modelling the required energy system transformations. The methodology will be based on a hybrid approachthat uses machine learning to combine both bottom-up appliance ownership modelling techniques that rely on survey data and technology diffusion models, with top-down statistical approaches that utilize national demographic and social data, and macroeconomic trends. The methodology will build on available geo-spatial information combined with existing field study, survey, and measurement data to derive and validate a more robust and generalizable rural energy demand modelling methodology.To facilitatethe broader utilisation of the developedmethodology, akey output of this project will be the creation of a dissemination strategy. This might include making the developed demand modelling code open-source, or even the creation of a GIS-based tool that can be used to assess the rural energy demands in developing countries in sub-Saharan Africa and Asia