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Source publication
Documentation of the SciXMiner Extension Package Forecasting, code is available at https://sourceforge.net/projects/scixminer/files/Extension%20packages/forecasting.zip/download
Citations
... For the nodes within the second group we train a series of quantile regressions that estimate 99 different quantiles ( = 0.01, 0.02, … , 0.99) of the nodes' active power one, two, and three hours in advance using as input the active power measurements of the past two days. These regressions are all polynomials of maximum degree two trained using the matlab open source toolbox scixminer [41] and the method described in [14]. As previously mentioned, the ppf uses as input the pce coefficients and bases of the future active and reactive power. ...
... Note that these quantile regressions have the same structure as the ones used for the power. They are polynomials of maximal degree two that take the current measurements of the past two days as input and that are trained using the matlab open source toolbox scixminer [41] and the method described in [14]. The presented approach also allows us to obtain correlated samples of the state defining variables that we can then use to forecast the probability of joint events, without having to estimate the joint distribution. ...
The uncertainty associated with renewable energies creates challenges in the operation of distribution grids. One way for Distribution System Operators to deal with this is the computation of probabilistic forecasts of the full state of the grid. Recently, probabilistic forecasts have seen increased interest for quantifying the uncertainty of renewable generation and load. However, individual probabilistic forecasts of the state defining variables do not allow the prediction of the probability of joint events, for instance, the probability of two line flows exceeding their limits simultaneously. To overcome the issue of estimating the probability of joint events, we present an approach that combines data-driven probabilistic forecasts (obtained more specifically with quantile regressions) and probabilistic power flow. Moreover, we test the presented method using data from a real-world distribution grid that is part of the Energy Lab 2.0 of the Karlsruhe Institute of Technology and we implement it within a state-of-the-art computational framework.
