  An effective way to oppose global warming and mitigate climate change is to
electrify our energy sectors and supply their electric power from renewable
wind and solar. Spatio-temporal predictions of electric load become
increasingly important for planning this transition, while deep learning
prediction models provide increasingly accurate predictions for it. The data
used for training deep learning models, however, is usually collected at random
using a passive learning approach. This naturally results in a large demand for
data and associated costs for sensors like smart meters, posing a large barrier
for electric utilities in decarbonizing their grids. Here, we test active
learning where we leverage additional computation for collecting a more
informative subset of data. We show how electric utilities can apply active
learning to better distribute smart meters and collect their data for more
accurate predictions of load with about half the data compared to when applying
passive learning.
