dc.contributor.author | Abrahamsen, Erik Boye | |
dc.contributor.author | Brastein, Ole Magnus | |
dc.contributor.author | Lie, Bernt | |
dc.date.accessioned | 2019-01-23T12:33:57Z | |
dc.date.available | 2019-01-23T12:33:57Z | |
dc.date.created | 2018-12-04T18:33:08Z | |
dc.date.issued | 2018 | |
dc.identifier.citation | Linköping Electronic Conference Proceedings. 2018, (153), 169-176. | nb_NO |
dc.identifier.issn | 1650-3686 | |
dc.identifier.uri | http://hdl.handle.net/11250/2581934 | |
dc.description.abstract | Forecasting weather conditions is important for, e.g., operation of hydro power plants and for flood management. Mechanistic models are known to be computationally demanding. Hence, it is of interest to develop models that can predict weather conditions faster than traditional meteorological models. The field of machine learning has received much interest from the scientific community. Due to its applicability in a variety of fields, it is of interest to study whether an artificial neural network can be a good candidate for prediction of weather conditions in combination with large data sets. The availability of meteorological data from multiple online sources is an advantage. In order to simplify the retrieval of data, a Python API to read meteorological data has been developed, and ANN models have been developed using TensorFlow | nb_NO |
dc.description.abstract | Machine Learning in Python for Weather Forecast based on Freely Available Weather Data | nb_NO |
dc.language.iso | eng | nb_NO |
dc.rights | Navngivelse-Ikkekommersiell 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/deed.no | * |
dc.title | Machine Learning in Python for Weather Forecast based on Freely Available Weather Data | nb_NO |
dc.type | Journal article | nb_NO |
dc.type | Peer reviewed | nb_NO |
dc.description.version | publishedVersion | nb_NO |
dc.source.pagenumber | 169-176 | nb_NO |
dc.source.journal | Linköping Electronic Conference Proceedings | nb_NO |
dc.source.issue | 153 | nb_NO |
dc.identifier.doi | 10.3384/ecp18153169 | |
dc.identifier.cristin | 1639147 | |
cristin.unitcode | 222,58,2,0 | |
cristin.unitname | Institutt for elektro, IT og kybernetikk | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |