Continuous and Distribution-free Probabilistic Wind Power Forecasting: A Conditional Normalizing Flow Approach

TL;DR

This paper introduces a novel distribution-free probabilistic wind power forecasting method using conditional normalizing flows, which directly models continuous probability densities without quantile crossing, validated on open datasets.

Contribution

The paper proposes a distribution-free, continuous probabilistic forecasting approach based on conditional normalizing flows, improving over existing quantile-based methods.

Findings

Effective probabilistic wind power forecasts demonstrated on open datasets.

Advantages over traditional quantile methods discussed.

Model avoids quantile crossing issues.

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 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.