dc.contributor.author | Dalal, Nisha | |
dc.contributor.author | Mølnå, Martin | |
dc.contributor.author | Herrem, Mette | |
dc.contributor.author | Røen, Magne | |
dc.contributor.author | Gundersen, Odd Erik | |
dc.date.accessioned | 2021-01-25T18:35:11Z | |
dc.date.available | 2021-01-25T18:35:11Z | |
dc.date.created | 2020-11-22T23:16:09Z | |
dc.date.issued | 2020 | |
dc.identifier.isbn | 978-1-57735-823-7 | |
dc.identifier.uri | https://hdl.handle.net/11250/2724630 | |
dc.description.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. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Association for the Advancement of Artificial Intelligence | en_US |
dc.relation.ispartof | The Thirty-Fourth Conference on Artificial Intelligence, AAAI | |
dc.title | Day-Ahead Forecasting of Losses in the Distribution Network | en_US |
dc.type | Chapter | en_US |
dc.description.version | publishedVersion | en_US |
dc.identifier.doi | 10.1609/aaai.v34i08.7018 | |
dc.identifier.cristin | 1850845 | |
dc.description.localcode | This chapter will not be available due to copyright restrictions (c) 2020 by Association for the Advancement of Artificial Intelligence | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |