  The present contribution offers a simple methodology for the obtainment of
data-driven interval forecasting models by combining pairs of quantile
regressions. Those regressions are created without the usage of the
non-differentiable pinball-loss function, but through a k-nearest-neighbors
based training set transformation and traditional regression approaches. By
leaving the underlying training algorithms of the data mining techniques
unchanged, the presented approach simplifies the creation of quantile
regressions with more complex techniques (e.g. artificial neural networks). The
quality of the presented methodology is tested on the usecase of photovoltaic
power forecasting, for which quantile regressions using polynomial models as
well as artificial neural networks and support vector regressions are created.
From the resulting evaluation values it can be concluded that acceptable
interval forecasting models are created.
