Stage-oe-small.jpg

Article3259: Unterschied zwischen den Versionen

Aus Aifbportal
Wechseln zu:Navigation, Suche
(Die Seite wurde neu angelegt: „{{Publikation Erster Autor |ErsterAutorNachname=Valgaev |ErsterAutorVorname=Oleg }} {{Publikation Author |Rank=2 |Author=Friederich Kupzog }} {{Publikation Aut…“)
 
 
Zeile 24: Zeile 24:
 
|Abstract=Following the ongoing transformation of the European power system, in the future, it will be necessary to locally balance the increasing share of decentralised renewable energy supply. Therefore, a reliable short-term load forecast at the level of single buildings is required. In this study, we use a forecaster, which is based on K -nearest neighbours approach and was introduced in an earlier publication, on three buildings of Smart City Demo Aspern project. The authors demonstrate how this forecaster can be applied on different buildings without any manual setup or parametrisation, showing that it is viable to replace load-profiling solutions for predicting electricity consumption at the level of single buildings.e
 
|Abstract=Following the ongoing transformation of the European power system, in the future, it will be necessary to locally balance the increasing share of decentralised renewable energy supply. Therefore, a reliable short-term load forecast at the level of single buildings is required. In this study, we use a forecaster, which is based on K -nearest neighbours approach and was introduced in an earlier publication, on three buildings of Smart City Demo Aspern project. The authors demonstrate how this forecaster can be applied on different buildings without any manual setup or parametrisation, showing that it is viable to replace load-profiling solutions for predicting electricity consumption at the level of single buildings.e
 
|Forschungsgruppe=Effiziente Algorithmen/en
 
|Forschungsgruppe=Effiziente Algorithmen/en
 +
}}
 +
{{Forschungsgebiet Auswahl
 +
|Forschungsgebiet=Energieinformatik
 +
}}
 +
{{Forschungsgebiet Auswahl
 +
|Forschungsgebiet=Sicherheit
 
}}
 
}}

Aktuelle Version vom 24. August 2021, 19:44 Uhr


Building power demand forecasting using K-nearest neighbours model – practical application in Smart City Demo Aspern project


Building power demand forecasting using K-nearest neighbours model – practical application in Smart City Demo Aspern project



Veröffentlicht: 2017 Oktober

Journal: CIRED-Open Access Proceedings Journal
Nummer: 1
Seiten: 1601-1604

Volume: 2017


Referierte Veröffentlichung

BibTeX




Kurzfassung
Following the ongoing transformation of the European power system, in the future, it will be necessary to locally balance the increasing share of decentralised renewable energy supply. Therefore, a reliable short-term load forecast at the level of single buildings is required. In this study, we use a forecaster, which is based on K -nearest neighbours approach and was introduced in an earlier publication, on three buildings of Smart City Demo Aspern project. The authors demonstrate how this forecaster can be applied on different buildings without any manual setup or parametrisation, showing that it is viable to replace load-profiling solutions for predicting electricity consumption at the level of single buildings.e



Forschungsgruppe

Effiziente Algorithmen/en


Forschungsgebiet

Energieinformatik, Sicherheit