TY - JOUR
T1 - Building power demand forecasting using K-nearest neighbours model – practical application in Smart City Demo Aspern project
AU - Valgaev, Oleg
AU - Kupzog, Friederich
AU - Schmeck, Hartmut
PY - 2017/10
Y1 - 2017/10
N2 - 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.
AB - 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.
KW - load forecasting
KW - functional regression
KW - buildings
KW - smart grid
U2 - 10.1049/oap-cired.2017.0419
DO - 10.1049/oap-cired.2017.0419
M3 - Article
SN - 2515-0855
JO - CIRED - Open Access Proceedings Journal
JF - CIRED - Open Access Proceedings Journal
ER -