Day-Ahead Forecasting of Losses in the Distribution Network

Authors

  • 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

DOI:

https://doi.org/10.1609/aaai.v34i08.7018

Abstract

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.

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Published

2020-04-03

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. https://doi.org/10.1609/aaai.v34i08.7018

Issue

Vol. 34 No. 08: AAAI-20 / IAAI-20 Technical Tracks

Section

IAAI Technical Track: Deployed Papers