Oleg Valgaev 2
Nonparametric Regression for Short Term Building Power Demand Forecasting
Following the ongoing transformation of the European power system, in the future, it will be necessary to balance the increasing share of decentralized renewable energy supply. Its volatility will have to be coped with locally, within the distribution grid, where low volt-age consumers can act as flexible loads. This will require reliable short-term load fore-casts at the level of single buildings, which are more accurate than currently used stand-ardized load profiles. At the same time, the forecaster has to require minimal amount of manual setup and parametrization since it will be applied at numerous buildings without any specific knowledge about them.
In this work, we develop such forecaster investigating nonparametric regression methods such as Kernel and K-Nearest Neighbors regression. We demonstrate its accuracy on a large set of low voltage consumers of different types and aggregation size, comparing them to load profiling, similar day, as well as other approaches discussed in the literature.
Start: 28. März 2018 um 15:45
Ende: 28. März 2018 um 17:15
Im Gebäude 05.20, Raum: 1C-04
Veranstaltung vormerken: (iCal)
Veranstalter: Forschungsgruppe(n) Effiziente Algorithmen
Information: Media:Valgaev 28-03-2018.pdf