  The transition to a fully renewable energy grid requires better forecasting
of demand at the low-voltage level to increase efficiency and ensure reliable
control. However, high fluctuations and increasing electrification cause huge
forecast variability, not reflected in traditional point estimates.
Probabilistic load forecasts take future uncertainties into account and thus
allow more informed decision-making for the planning and operation of
low-carbon energy systems. We propose an approach for flexible conditional
density forecasting of short-term load based on Bernstein polynomial
normalizing flows, where a neural network controls the parameters of the flow.
In an empirical study with 363 smart meter customers, our density predictions
compare favorably against Gaussian and Gaussian mixture densities. Also, they
outperform a non-parametric approach based on the pinball loss for 24h-ahead
load forecasting for two different neural network architectures.
