Zhao, R., Stincescu, T., Ballantyne, E.E.F. orcid.org/0000-0003-4665-0941 et al. (1 more author) (2020) Sustainable city : energy usage prediction method for electrified refuse collection vehicles. Smart Cities, 3 (3). pp. 1100-1116.
Abstract
With the initiative of sustainable smart city space, services and structures (3S), progress towards zero-emission municipal services has advanced the deployment of electric refuse collection vehicles (eRCVs). However, eRCVs are commonly equipped with oversized batteries which not only contribute to the majority of the weight of the vehicles but also remain a consistent weight, independent of the stage of charge (SoC), thus crucially jeopardising the significance of eRCVs in sustainability and economic strategies. Hence, customising the battery capacity in such a way that minimises its weight while storing ample energy for stalwart serviceability could significantly enhance its sustainability. In this study, taking only addresses as input, through an emergent two-stage data analysis, the energy required to collect refuse from a group of addresses was predicted. Therefore, predictions of the battery capacity requirement for the target location are possible. The theories and techniques presented in this paper were evaluated using real-life data from eRCV trials. For the same group of addresses, predicted results show an averaged error rate of 8.44%, which successfully demonstrates that using the proposed address-driven energy prediction approach, the energy required to collect refuse from a set of addresses can be predicted, which can provide a means to optimise the vehicle’s battery requirement.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 The Authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
Keywords: | eRCV; route planning; energy prediction |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) The University of Sheffield > Faculty of Social Sciences (Sheffield) > Management School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 06 Oct 2020 10:22 |
Last Modified: | 06 Oct 2020 10:22 |
Status: | Published |
Publisher: | MDPI AG |
Refereed: | Yes |
Identification Number: | 10.3390/smartcities3030054 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:165905 |