Predicting the traction power of metropolitan railway lines using different machine learning models
[EN] Railways are an efficient transport mean with lower energy consumption and emissions in comparison to other transport means for freight and passengers, and yet there is a growing need to increase their efficiency. To achieve this, it is needed to accurately predict their energy consumption, a t...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/176321 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/176321 |
| Access Level: | acceso abierto |
| Palabra clave: | Machine learning Traction power Random forests Metropolitan railway lines Energy consumption Artificial neural networks INGENIERIA E INFRAESTRUCTURA DE LOS TRANSPORTES |
| Sumario: | [EN] Railways are an efficient transport mean with lower energy consumption and emissions in comparison to other transport means for freight and passengers, and yet there is a growing need to increase their efficiency. To achieve this, it is needed to accurately predict their energy consumption, a task which is traditionally carried out using deterministic models which rely on data measured through money- and time-consuming methods. Using four basic (and cheap to measure) features (train speed, acceleration, track slope and radius of curvature) from MetroValencia (Spain), we predicted the traction power using different machine learning models, obtaining that a random forest model outperforms other approaches in such task. The results show the possibility of using basic features to predict the traction power in a metropolitan railway line, and the chance of using this model as a tool to assess different strategies in order to increase the energy efficiency in these lines. |
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