Day-Ahead Forecasting of Losses in the Distribution Network


  • Nisha Dalal TrønderEnergi Kraft AS
  • Martin Mølnå TrønderEnergi Kraft AS
  • Mette Herrem TrønderEnergi Kraft AS
  • Magne Røen TrønderEnergi Kraft AS
  • Odd Erik Gundersen Norwegian University of Science and Technology and TrønderEnergi Kraft AS



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, among other things, delayed, missing and incorrect data and a small data set. The system incorporates a total of 24 different models that performs forecasts for three sub-grids. Each day one model is selected for making the hourly day-ahead forecasts for each sub-grid. The deployed system reduces the MAE with 41% from 3.68 MW to 2.17 MW per hour from mid July to mid October. It is robust and reduces manual work.




How to Cite

Dalal, N., Mølnå, M., Herrem, M., Røen, M., & Gundersen, O. E. (2020). Day-Ahead Forecasting of Losses in the Distribution Network. Proceedings of the AAAI Conference on Artificial Intelligence, 34(08), 13148-13155.



IAAI Technical Track: Deployed Papers