A clustering based approach for energy efficient routing

One of the most significant issues the research community has focused on during the last decades, is the reduction of the energy consumed in every aspect of everyday life. A standout amongst the most important factors of energy consumption is transportation. To this end, a lot of work in the field o...

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Bibliographic Details
Published in2016 IEEE Symposium on Computers and Communication (ISCC) pp. 232 - 237
Main Authors Kosmides, Pavlos, Lambrinos, Lambros, Asthenopoulos, Vasilis, Demestichas, Konstantinos, Adamopoulou, Evgenia
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2016
Subjects
clustering based prediction
Energy consumption
Energy efficiency
Intelligent Transport Systems
machine learning
Multilayer perceptrons
Roads
Routing
Support vector machines
Vehicles
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Summary:One of the most significant issues the research community has focused on during the last decades, is the reduction of the energy consumed in every aspect of everyday life. A standout amongst the most important factors of energy consumption is transportation. To this end, a lot of work in the field of Intelligent Transport Systems concentrates on enhancing energy efficiency. This trend was reinforced by the appearance of Fully Electric Vehicles (FEVs), where it is more crucial to increase their energy efficiency in any manner. Eco-routing refers to the choice of the most energy efficient route towards a destination and seems very promising for reducing everyday energy consumption. In this paper, we present a novel method for predicting energy consumption levels, based on machine learning techniques. In addition, addressing the problem of ever increasing amounts of tracking data acquired from vehicles, we introduce a clustering based prediction method and apply it on real world measurements in order to evaluate its performance.
DOI:10.1109/ISCC.2016.7543745