  Modern decision-making processes require uncertainty-aware models, especially
those relying on non-symmetric costs and risk-averse profiles. The objective of
this work is to propose a dynamic model for the conditional non-parametric
distribution function (CDF) to generate probabilistic forecasts for a renewable
generation time series. To do that, we propose an adaptive non-parametric
time-series model driven by a regularized multiple-quantile-regression (MQR)
framework. In our approach, all regression models are jointly estimated through
a single linear optimization problem that finds the global-optimal parameters
in polynomial time. An innovative feature of our work is the consideration of a
Lipschitz regularization of the first derivative of coefficients in the
quantile space, which imposes coefficient smoothness. The proposed
regularization induces a coupling effect among quantiles creating a single
non-parametric CDF model with improved out-of-sample performance. A case study
with realistic wind-power generation data from the Brazilian system shows: 1)
the regularization model is capable to improve the performance of MQR
probabilistic forecasts, and 2) our MQR model outperforms five relevant
benchmarks: two based on the MQR framework, and three based on parametric
models, namely, SARIMA, and GAS with Beta and Weibull CDF.
