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 | 2022-03-02T08:26:41Z | |
dc.date.available | 2022-03-02T08:26:41Z | |
dc.date.created | 2021-12-15T16:05:49Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | The AI Magazine. 2021, 42 (2), 38-49. | en_US |
dc.identifier.issn | 0738-4602 | |
dc.identifier.uri | https://hdl.handle.net/11250/2982288 | |
dc.description.abstract | 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 nomination of the expected grid loss. 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 dayahead forecasts for each sub-grid. The deployed system reduced the mean average percentage error (MAPE) with 40% from 12.17 to 7.26 per hour from mid-July to mid-October, 2019. It is robust, flexible and reduces manual work. Recently, the system was deployed to forecast and nominate grid losses for two new grids belonging to a new customer. As the presented system is modular and adaptive, the integration was quick and needed minimal work. We have shared the grid loss data-set on Kaggle. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Association for the Advancement of Artificial Intelligence | en_US |
dc.title | Day-ahead forecasting of losses in the distribution network | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | acceptedVersion | en_US |
dc.rights.holder | This is the authors' accepted manuscript to an article published by Association for the Advancement of Artificial Intelligence. | en_US |
dc.source.pagenumber | 38-49 | en_US |
dc.source.volume | 42 | en_US |
dc.source.journal | The AI Magazine | en_US |
dc.source.issue | 2 | en_US |
dc.identifier.doi | 10.1609/aaai.12004 | |
dc.identifier.cristin | 1969060 | |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.qualitycode | 1 | |