A hybrid supervised learning model for a medium-term MV/LV transformer loading forecast with an increasing capacity of PV panels

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

Samenvatting

The share of photovoltaic (PV) generation has increased quickly in the last decade. Many PV panels are connected behind-the-meter (BTM), so that they can not be identified with measurement equipment at MV/LV transformers. This poses a challenge for a medium-term MV/LV transformer loading forecast if the capacity of PV panels is increasing over time. Therefore, this paper proposes a hybrid approach for a medium-term load forecast (MTLF) of a MV/LV transformer with an increasing capacity of PV panels that are not separately measured. This approach combines a supervised learning model (data-driven approach) with a model to estimate the generation profile of the PV panels (model-based approach). The results indicate that the accuracy of the forecast improves significantly, while an accurate generation profile of the PV panels connected BTM or a disaggregation of the net load is unnecessary.
Originele taal-2Engels
Titel 2021 IEEE Madrid PowerTech, PowerTech 2021
Aantal pagina's6
StatusGeaccepteerd/In druk - 16 apr 2021
Evenement2021 IEEE Madrid PowerTech, PowerTech 2021 - Madrid, Madrid, Spanje
Duur: 28 jun 20212 jul 2021

Congres

Congres2021 IEEE Madrid PowerTech, PowerTech 2021
LandSpanje
StadMadrid
Periode28/06/212/07/21

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