Continuous and Distribution-free Probabilistic Wind Power Forecasting: A
Conditional Normalizing Flow Approach
Abstract
We present a data-driven approach for probabilistic wind power
forecasting based on conditional normalizing flow~(CNF).
In contrast with the existing, this approach is distribution-free (as
for non-parametric and quantile-based approaches) and can directly yield
continuous probability densities, hence avoiding quantile crossing. It
relies on a base distribution and a set of bijective mappings. Both the
shape parameters of the base distribution and the bijective mappings are
approximated with neural networks. Spline-based conditional normalizing
flow is considered owing to its universal approximation capability. Over
the training phase, the model sequentially maps input examples onto
samples of base distribution, where parameters are estimated through
maximum likelihood. To issue probabilistic forecasts, one eventually map
samples of the base distribution into samples of a desired distribution.
Case studies based on open datasets validate the effectiveness of the
proposed model, and allows us to discuss its advantages and caveats with
respect to the state of the art. Code will be released upon publication.