Day-Ahead Forecasting of Losses in the Distribution Network
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.
Cite
Text
Dalal et al. "Day-Ahead Forecasting of Losses in the Distribution Network." AAAI Conference on Artificial Intelligence, 2020. doi:10.1609/AAAI.V34I08.7018Markdown
[Dalal et al. "Day-Ahead Forecasting of Losses in the Distribution Network." AAAI Conference on Artificial Intelligence, 2020.](https://mlanthology.org/aaai/2020/dalal2020aaai-day/) doi:10.1609/AAAI.V34I08.7018BibTeX
@inproceedings{dalal2020aaai-day,
title = {{Day-Ahead Forecasting of Losses in the Distribution Network}},
author = {Dalal, Nisha and Mølnå, Martin and Herrem, Mette and Røen, Magne and Gundersen, Odd Erik},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2020},
pages = {13148-13155},
doi = {10.1609/AAAI.V34I08.7018},
url = {https://mlanthology.org/aaai/2020/dalal2020aaai-day/}
}