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Using Machine Learning to Optimize Energy Consumption of HVAC Systems in Vehicles

Martin Boehme, Andreas Lauber, Marco Stang, Luyi Pan, Eric Sax

Abstract:
The detachment and calculation of functionalities from a vehicle into a cloud creates new chances. By linking different data sources with the in-vehicle data in the cloud, an optimization of these functionalities in terms of en-ergy efficiency can be applied. For example, the Heating, Ventilation and Air Conditioning (HVAC) consumes up to 30% of total energy in a vehicle. Electric vehicles in particular lead to these high values because they are not able to re-cover the waste heat from combustion engines for interior heating. Therefore, the optimization of energy efficient strategies with respect to the vehicle energy management system becomes more relevant. Forecasts of the interior vehicle temperature are directly related to the HVAC energy consumption. This work focuses on the implementation and accuracy evaluation of Recurrent Neural Networks (RNN) for interior vehicle temperature forecasting.

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Postprint §
DOI: 10.5445/IR/1000096849/post
Veröffentlicht am 26.07.2020
Originalveröffentlichung
DOI: 10.1007/978-3-030-25629-6
Dimensions
Zitationen: 1
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Technik der Informationsverarbeitung (ITIV)
Publikationstyp Proceedingsbeitrag
Publikationsdatum 25.07.2019
Sprache Englisch
Identifikator ISBN: 978-3-030-25628-9
ISSN: 2194-5357
KITopen-ID: 1000096849
Erschienen in Human Interaction and Emerging Technologies: Proceedings of the 1st International Conference on Human Interaction and Emerging Technologies (IHIET 2019), August 22-24, 2019, Nice, France. Eds.: Tareq Ahram
Veranstaltung 1 International Conference on Human Interaction & Emerging Technologies (2019), Nizza, Frankreich, 22.08.2019 – 24.08.2019
Auflage 1
Verlag Springer US
Seiten 706-712
Serie Advances in Intelligent Systems and Computing ; 1018
Schlagwörter Heating Ventilation and Air Conditioning (HVAC) · Energy Efficiency · Internet of Things · Machine Learning
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