Computation of ultra-short-term prediction intervals of the power prosumption in active distribution networks
Year: 2024
Type: article
Abstract: Microgrids and, in general, active distribution networks require ultra-short-term prediction, i.e., for sub-second time scales, for specific control decisions. Conventional forecasting methodologies are not effective at such time scales. To address this issue, we propose a non-parametric method for computing ultra short-term prediction intervals (PIs) of the power prosumption of generic electrical-distribution networks. The method groups historical observations into clusters according to the values of influential variables. It is applied either to the original or to the differentiated power-prosumption time series. The clusters are considered statistically representative pools of future realizations of power prosumption (or its derivative). They are used to determine empirical PDFs and, by extracting the quantiles, to deliver PIs for respective arbitrary confidence levels. The models are validated a posteriori by carrying out a performance analysis that uses experimentally observed power-prosumption for different building types, thus allowing the identification of the dominant model. • Method for computing prediction intervals of electrical production/load. • Clustering of measurements based on power level and time of day. • Construct the histogram of the data empirically in an online fashion. • Algorithm tested for buildings at different aggregation levels. • Method achieves high confidence levels at sub-second resolutions.
Source: Electric Power Systems Research
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,480
Funder: Bundesamt für Energie
Awards: G7663882994, G8130849371
OpenAlex ID: W4400151980