Probabilistic Prediction of Energy Demand and Driving Range for Electric Vehicles with Federated Learning
In this work, an extension of the federated averaging algorithm, FedAvg-Gaussian, is applied to train probabilistic neural networks. The performance advantage of probabilistic prediction models is demonstrated and it is shown that federated learning can improve driving range prediction. Using probab...
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| Formato: | Online |
| Idioma: | inglês |
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KIT Scientific Publishing
2024
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| Acesso em linha: | https://library.oapen.org/handle/20.500.12657/93282 |
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| _version_ | 1869516402834014208 |
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| author | Thorgeirsson, Adam Thor |
| author_browse | Thorgeirsson, Adam Thor |
| author_facet | Thorgeirsson, Adam Thor |
| author_sort | Thorgeirsson, Adam Thor |
| collection | Directory of Open Access Books |
| description | In this work, an extension of the federated averaging algorithm, FedAvg-Gaussian, is applied to train probabilistic neural networks. The performance advantage of probabilistic prediction models is demonstrated and it is shown that federated learning can improve driving range prediction. Using probabilistic predictions, routing and charge planning based on destination attainability can be applied. Furthermore, it is shown that probabilistic predictions lead to reduced travel time. |
| format | Online |
| id | doab-20.500.12854ir-145317 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | KIT Scientific Publishing |
| publisherStr | KIT Scientific Publishing |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1453172025-05-27T06:49:46Z Probabilistic Prediction of Energy Demand and Driving Range for Electric Vehicles with Federated Learning Thorgeirsson, Adam Thor Reichweite; Federated Learning; Probabilistic Predictions; Driving Range; Electric Vehicles; Föderiertes Lernen; Probabilistische Vorhersage; Elektrofahrzeuge thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials In this work, an extension of the federated averaging algorithm, FedAvg-Gaussian, is applied to train probabilistic neural networks. The performance advantage of probabilistic prediction models is demonstrated and it is shown that federated learning can improve driving range prediction. Using probabilistic predictions, routing and charge planning based on destination attainability can be applied. Furthermore, it is shown that probabilistic predictions lead to reduced travel time. 2024-09-17T04:32:56Z 2024-09-17T04:32:56Z 2024-09-16T10:02:59Z 2024 book https://library.oapen.org/handle/20.500.12657/93282 9783731513711 https://directory.doabooks.org/handle/20.500.12854/145317 eng Karlsruher Schriftenreihe Fahrzeugsystemtechnik open access image/jpeg image/jpeg image/jpeg image/jpeg Attribution 4.0 International Attribution 4.0 International Attribution 4.0 International Attribution 4.0 International https://library.oapen.org/bitstream/20.500.12657/93282/1/probabilistic-prediction-of-energy-demand-and-driving-range-for-electric-vehicles-with-federated-learning.pdf https://library.oapen.org/bitstream/20.500.12657/93282/1/probabilistic-prediction-of-energy-demand-and-driving-range-for-electric-vehicles-with-federated-learning.pdf https://library.oapen.org/bitstream/20.500.12657/93282/1/probabilistic-prediction-of-energy-demand-and-driving-range-for-electric-vehicles-with-federated-learning.pdf https://library.oapen.org/bitstream/20.500.12657/93282/1/probabilistic-prediction-of-energy-demand-and-driving-range-for-electric-vehicles-with-federated-learning.pdf KIT Scientific Publishing 10.5445/KSP/1000171796 10.5445/KSP/1000171796 68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2 9783731513711 AG Universitätsverlage 190 open access |
| spellingShingle | Reichweite; Federated Learning; Probabilistic Predictions; Driving Range; Electric Vehicles; Föderiertes Lernen; Probabilistische Vorhersage; Elektrofahrzeuge thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials Thorgeirsson, Adam Thor Probabilistic Prediction of Energy Demand and Driving Range for Electric Vehicles with Federated Learning |
| title | Probabilistic Prediction of Energy Demand and Driving Range for Electric Vehicles with Federated Learning |
| title_full | Probabilistic Prediction of Energy Demand and Driving Range for Electric Vehicles with Federated Learning |
| title_fullStr | Probabilistic Prediction of Energy Demand and Driving Range for Electric Vehicles with Federated Learning |
| title_full_unstemmed | Probabilistic Prediction of Energy Demand and Driving Range for Electric Vehicles with Federated Learning |
| title_short | Probabilistic Prediction of Energy Demand and Driving Range for Electric Vehicles with Federated Learning |
| title_sort | probabilistic prediction of energy demand and driving range for electric vehicles with federated learning |
| topic | Reichweite; Federated Learning; Probabilistic Predictions; Driving Range; Electric Vehicles; Föderiertes Lernen; Probabilistische Vorhersage; Elektrofahrzeuge thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials |
| topic_facet | Reichweite; Federated Learning; Probabilistic Predictions; Driving Range; Electric Vehicles; Föderiertes Lernen; Probabilistische Vorhersage; Elektrofahrzeuge thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials |
| url | https://library.oapen.org/handle/20.500.12657/93282 |
| work_keys_str_mv | AT thorgeirssonadamthor probabilisticpredictionofenergydemandanddrivingrangeforelectricvehicleswithfederatedlearning |