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Microgrids and, in general, active distribution networks requireultra-short-term prediction, i.e., for sub-second time scales, for specificcontrol decisions. Conventional forecasting methodologies are not effective atsuch time scales. To address this issue, we propose a non-parametric method forcomputing ultra short-term prediction intervals (PIs) of the power prosumptionof generic electrical-distribution networks. The method groups historicalobservations into clusters according to the values of influential variables. Itis applied either to the original or to the differentiated power-prosumptiontime series. The clusters are considered statistically representative pools offuture realizations of power prosumption (or its derivative). They are used todetermine empirical PDFs and, by extracting the quantiles, to deliver PIs forrespective arbitrary confidence levels. The models are validated a posterioriby carrying out a performance analysis that uses experimentally observedpower-prosumption for different building types, thus allowing theidentification of the dominant model.

Computation of Ultra-Short-Term Prediction Intervals of the Power Prosumption in Active Distribution Networks