Abstract
Accurate forecasting of vehicular consumption is a task of primary importance for several applications. Herein, a vehicular consumption prediction model is proposed, with special emphasis on robustness and reliability. Both features are enabled due to the selection of general regression neural networks (GRNNs) for the implementation of the proposed model. GRNNs are widely used among neural networks because of their capabilities for fast learning and successful convergence to the solution. In particular, the designed GRNN is responsible for approximating the nonlinearities and the specificities between the factors identified as major contributors in vehicular consumption. In order to evaluate its efficiency, a case study involving the application of the introduced model in fully electric vehicles (FEVs) is examined. The performance of the proposed model is successfully validated using real measurements collected during a data acquisition field campaign.
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Acknowledgments
This work has been performed under the FP7 314151 project EMERALD, which has received research funding from the European Union’s Seventh Framework Programme. This paper reflects only the authors’ views, and the Community is not liable for any use that may be made of the information contained therein.
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Communicated by M. J. Watts.
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Masikos, M., Demestichas, K., Adamopoulou, E. et al. Mesoscopic forecasting of vehicular consumption using neural networks. Soft Comput 19, 145–156 (2015). https://doi.org/10.1007/s00500-014-1238-4
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DOI: https://doi.org/10.1007/s00500-014-1238-4