Electricity demand forecasting in agriculture – harvesting the synergies of machine learning and survey data for electrification planning in Ethiopia
This project assesses how electricity utilities in developing countries
can develop effective electricity demand forecasts and stimulation
techniques for productive uses in agriculture, using the example of
Ethiopia. For demand forecasting, we will apply an approach that
combines innovative machine learning techniques with classical
on-the-ground’ surveys among households, enterprises, and communities.
The aim is to develop a scalable cost-effective approach that uses
relatively ‘expensive to collect’ information from on-the-ground surveys
in a limited number of regions to train machine algorithms how to
extrapolate this in-depth information to the entire country. In an
additional component, we will use the collected and generated data from
the demand forecasts to derive data-driven suggestions for demand
stimulation interventions. Embedding the knowledge of local policy
agencies, we will map the demand for new irrigation and agro-processing
potentials with an eye on most promising value chains. We thereby intend
to contribute to the growing literature on techno-economic planning
tools to design electrification strategies. Effective planning tools are
pivotal to define the most suitable electrification strategy for
different sub-national regions, comparing the extension of the national
grid to the establishment of a mini-grid or other off-grid systems.
This research is part of the Applied Research Programme on Energy for
Economic Growth (EEG) led by Oxford Policy Management. The programme is
funded by the UK Government, through UK Aid.
Applied Research Programme on Energy for Economic Growth (EEG)
Publications
Project start:
01. March 2019
Project end:
31. December 2021
Project staff:
Prof. Dr. Jörg Peters,
Dr. Gunther Bensch
Project partners:
University of Massachusetts Amherst,
Ethiopian Development Research Institute,
Policy Studies Institute
Principal:
UK Aid