• Day-Ahead Forecasting of Losses in the Distribution Network 

      Dalal, Nisha; Mølnå, Martin; Herrem, Mette; Røen, Magne; Gundersen, Odd Erik (Chapter, 2020)
      We present a commercially deployed machine learning system that automates the day-ahead nomination of the expected grid loss for a Norwegian utility company. It meets several practical constraints and issues related to, ...
    • Day-ahead forecasting of losses in the distribution network 

      Dalal, Nisha; Mølnå, Martin; Herrem, Mette; Røen, Magne; Gundersen, Odd Erik (Peer reviewed; Journal article, 2021)
      Utility companies in the Nordics have to nominate how much electricity is expected to be lost in their power grid the next day. We present a commercially deployed machine learning system that automates this day-ahead ...
    • A Robust and Scalable Stacked Ensemble for Day-Ahead Forecasting of Distribution Network Losses 

      Grotmol, Gunnar Grung; Furdal, Eivind Hovdegård; Dalal, Nisha; Ottesen, Are Løkken; Rørvik, Ella-Lovise Hammervold; Mølnå, Martin; Sizov, Gleb Valerjevich; Gundersen, Odd Erik (Chapter, 2023)
      Accurate day-ahead nominations of grid losses in electrical distribution networks are important to reduce the societal cost of these losses. We present a modification of the CatBoost ensemble-based system for day-ahead ...
    • Tag Inference 

      Minsås, Håvard Rakbjørg (Master thesis, 2023)
      Moderne bygg bruker i større grad enn tidligere ulike sensorer for å overvåke ulike deler av konstruksjonen. Slik data lagres gjerne som en tidsserie, hvor verdier assosieres med et tidsstempel. I denne oppgaven behandles ...