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

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Abstract

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.
Original languageEnglish
Title of host publication2021 IEEE Madrid PowerTech
PublisherInstitute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)978-1-6654-3597-0
DOIs
Publication statusPublished - 29 Jul 2021
Event2021 IEEE Madrid PowerTech, PowerTech 2021 - Madrid, Madrid, Spain
Duration: 28 Jun 20212 Jul 2021

Conference

Conference2021 IEEE Madrid PowerTech, PowerTech 2021
Country/TerritorySpain
CityMadrid
Period28/06/212/07/21

Keywords

  • behind-the-meter PV generation
  • distribution network
  • medium-term
  • net load forecasting
  • supervised machine learning

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