Matches in SemOpenAlex for { <https://semopenalex.org/work/W4246513260> ?p ?o ?g. }
Showing items 1 to 96 of
96
with 100 items per page.
- W4246513260 endingPage "49" @default.
- W4246513260 startingPage "38" @default.
- W4246513260 abstract "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 day‐ahead 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." @default.
- W4246513260 created "2022-05-12" @default.
- W4246513260 creator A5022601333 @default.
- W4246513260 creator A5065997562 @default.
- W4246513260 creator A5068647440 @default.
- W4246513260 creator A5070076671 @default.
- W4246513260 creator A5085248892 @default.
- W4246513260 date "2021-06-01" @default.
- W4246513260 modified "2023-10-17" @default.
- W4246513260 title "Day‐ahead forecasting of losses in the distribution network" @default.
- W4246513260 cites W1984585304 @default.
- W4246513260 cites W2125960964 @default.
- W4246513260 cites W2136972741 @default.
- W4246513260 cites W2148308534 @default.
- W4246513260 cites W2296609147 @default.
- W4246513260 cites W2417333619 @default.
- W4246513260 cites W2507901382 @default.
- W4246513260 cites W2516466667 @default.
- W4246513260 cites W2709974847 @default.
- W4246513260 cites W2779922928 @default.
- W4246513260 cites W2804543664 @default.
- W4246513260 cites W2901522902 @default.
- W4246513260 doi "https://doi.org/10.1609/aimag.v42i2.15097" @default.
- W4246513260 hasPublicationYear "2021" @default.
- W4246513260 type Work @default.
- W4246513260 citedByCount "0" @default.
- W4246513260 crossrefType "journal-article" @default.
- W4246513260 hasAuthorship W4246513260A5022601333 @default.
- W4246513260 hasAuthorship W4246513260A5065997562 @default.
- W4246513260 hasAuthorship W4246513260A5068647440 @default.
- W4246513260 hasAuthorship W4246513260A5070076671 @default.
- W4246513260 hasAuthorship W4246513260A5085248892 @default.
- W4246513260 hasBestOaLocation W42465132601 @default.
- W4246513260 hasConcept C101468663 @default.
- W4246513260 hasConcept C10558101 @default.
- W4246513260 hasConcept C111919701 @default.
- W4246513260 hasConcept C119599485 @default.
- W4246513260 hasConcept C119857082 @default.
- W4246513260 hasConcept C121332964 @default.
- W4246513260 hasConcept C127413603 @default.
- W4246513260 hasConcept C140367253 @default.
- W4246513260 hasConcept C154945302 @default.
- W4246513260 hasConcept C163258240 @default.
- W4246513260 hasConcept C177264268 @default.
- W4246513260 hasConcept C187691185 @default.
- W4246513260 hasConcept C199360897 @default.
- W4246513260 hasConcept C2524010 @default.
- W4246513260 hasConcept C33923547 @default.
- W4246513260 hasConcept C41008148 @default.
- W4246513260 hasConcept C42475967 @default.
- W4246513260 hasConcept C58489278 @default.
- W4246513260 hasConcept C62520636 @default.
- W4246513260 hasConcept C79403827 @default.
- W4246513260 hasConcept C89227174 @default.
- W4246513260 hasConceptScore W4246513260C101468663 @default.
- W4246513260 hasConceptScore W4246513260C10558101 @default.
- W4246513260 hasConceptScore W4246513260C111919701 @default.
- W4246513260 hasConceptScore W4246513260C119599485 @default.
- W4246513260 hasConceptScore W4246513260C119857082 @default.
- W4246513260 hasConceptScore W4246513260C121332964 @default.
- W4246513260 hasConceptScore W4246513260C127413603 @default.
- W4246513260 hasConceptScore W4246513260C140367253 @default.
- W4246513260 hasConceptScore W4246513260C154945302 @default.
- W4246513260 hasConceptScore W4246513260C163258240 @default.
- W4246513260 hasConceptScore W4246513260C177264268 @default.
- W4246513260 hasConceptScore W4246513260C187691185 @default.
- W4246513260 hasConceptScore W4246513260C199360897 @default.
- W4246513260 hasConceptScore W4246513260C2524010 @default.
- W4246513260 hasConceptScore W4246513260C33923547 @default.
- W4246513260 hasConceptScore W4246513260C41008148 @default.
- W4246513260 hasConceptScore W4246513260C42475967 @default.
- W4246513260 hasConceptScore W4246513260C58489278 @default.
- W4246513260 hasConceptScore W4246513260C62520636 @default.
- W4246513260 hasConceptScore W4246513260C79403827 @default.
- W4246513260 hasConceptScore W4246513260C89227174 @default.
- W4246513260 hasIssue "2" @default.
- W4246513260 hasLocation W42465132601 @default.
- W4246513260 hasLocation W42465132602 @default.
- W4246513260 hasOpenAccess W4246513260 @default.
- W4246513260 hasPrimaryLocation W42465132601 @default.
- W4246513260 hasRelatedWork W2265619108 @default.
- W4246513260 hasRelatedWork W2468827065 @default.
- W4246513260 hasRelatedWork W2521605489 @default.
- W4246513260 hasRelatedWork W2939495184 @default.
- W4246513260 hasRelatedWork W2999598281 @default.
- W4246513260 hasRelatedWork W3214505305 @default.
- W4246513260 hasRelatedWork W4236336998 @default.
- W4246513260 hasRelatedWork W4239066011 @default.
- W4246513260 hasRelatedWork W4242151928 @default.
- W4246513260 hasRelatedWork W4244590767 @default.
- W4246513260 hasVolume "42" @default.
- W4246513260 isParatext "false" @default.
- W4246513260 isRetracted "false" @default.
- W4246513260 workType "article" @default.