Continuous and Distribution-free Probabilistic Wind Power Forecasting: A Conditional Normalizing Flow Approach
This work addresses wind power forecasting for energy grid management, offering a novel method but is incremental as it builds on existing normalizing flow techniques.
The paper tackles probabilistic wind power forecasting by proposing a conditional normalizing flow approach that is distribution-free and yields continuous probability densities, avoiding quantile crossing issues; case studies on open datasets validate its effectiveness.
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.