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Continuous and Distribution-free Probabilistic Wind Power Forecasting: A Conditional Normalizing Flow Approach
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 non-affine
characteristics. Over the training phase, the model sequentially maps input
examples onto samples of base distribution, given the conditional contexts,
where parameters are estimated through maximum likelihood. To issue
probabilistic forecasts, one eventually maps 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.Comment: The second revision to IEEE Transactions on Sustainable Energ
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