  In low-income settings, the most critical piece of information for electric
utilities is the anticipated consumption of a customer. Electricity consumption
assessment is difficult to do in settings where a significant fraction of
households do not yet have an electricity connection. In such settings the
absolute levels of anticipated consumption can range from 5-100 kWh/month,
leading to high variability amongst these customers. Precious resources are at
stake if a significant fraction of low consumers are connected over those with
higher consumption.
  This is the first study of it's kind in low-income settings that attempts to
predict a building's consumption and not that of an aggregate administrative
area. We train a Convolutional Neural Network (CNN) over pre-electrification
daytime satellite imagery with a sample of utility bills from 20,000
geo-referenced electricity customers in Kenya (0.01% of Kenya's residential
customers). This is made possible with a two-stage approach that uses a novel
building segmentation approach to leverage much larger volumes of no-cost
satellite imagery to make the most of scarce and expensive customer data. Our
method shows that competitive accuracies can be achieved at the building level,
addressing the challenge of consumption variability. This work shows that the
building's characteristics and it's surrounding context are both important in
predicting consumption levels. We also evaluate the addition of lower
resolution geospatial datasets into the training process, including nighttime
lights and census-derived data. The results are already helping inform site
selection and distribution-level planning, through granular predictions at the
level of individual structures in Kenya and there is no reason this cannot be
extended to other countries.
